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A Guide to SARS-CoV-2 and the COVID-19 Pandemic

Nexus COVID-19 Response Team
Cambridge, MA
October 23, 2021



Background Information

“An ounce of prevention is worth a pound of cure.”

―Benjamin Franklin, Statesman (1706-1790)

Introduction

Declared a Public Health Emergency of International Concern (PHEIC) by the World Health Organization (WHO) on January 30, 2020 and a Pandemic (Phase 6) on March 11, 2020, the Coronavirus Disease of 2019 or COVID-19 is caused by SARS-CoV-2, formerly 2019-nCoV, an emergent and zoonotic RNA-virus and the second-known novel Severe Acute Respiratory Syndrome Coronavirus affecting the human species. At the time of writing, the scale, scope, and reach of the COVID-19 Pandemic have yet to be fully realized, as the global counts of infections and deaths continue to increase, causing clinical stresses or near-breaking points for healthcare systems and medical infrastructures worldwide, while the long-term societal and global economic consequences still have yet to be determined. There is currently no universally-accepted treatment for COVID-19 or widely available vaccine against SARS-CoV-2, which can cause an overwhelming immune response and other systemic detriments even death in some (with currently unclear pathophysiology) due to a general lack of natural immunity in all. The combination of human-to-human transmission through airborne aerosolized respiratory droplets, a relatively high rate of transmissibility including by asymptomatic and pre-symptomatic but contagious individuals (due to a relatively lengthy incubation period) through currently unknown modes of communicability, relatively high case fatality rate compared to the most common influenza viruses, and a significant potential for global endemicity have made this pandemic particularly swift, lethal, and of immediate global concern.

As of October 20, 2020, exactly 287 days since the reported identification (January 7, 2020) of the aforementioned novel coronavirus responsible for the viral pneumonia of unknown etiology in the outbreak in Wuhan, Hubei Province, China, the global infection count exceeds 40,634,575 reported COVID-19 cases as confirmed by laboratory testing from 744,948,090 reported samples across 216 countries and territories and 2 international conveyances (i.e., ocean liners). These consist of 9,651,107 (23.75%) active cases (9,578,333 mild/moderate cases and 72,774 serious/critical cases) and 30,983,468 (76.25%) closed cases (29,860,725 recoveries/discharges and 1,122,743 related deaths directly and indirectly attributed to COVID-19), yielding a global Testing Rate (TR) of 9.57%, Positivity Rate (PR) of 5.45%, Provisional Infection Rate (PIR) of 5.22‰ (permille), Provisional Mortality Rate (PMR) of 144 dpm (deaths per million), Provisional [1]Case Fatality Rate (PCFR) of 2.76%, and a 14-Day Adjusted PCFR of 3.16%. The global distribution of reported COVID-19 cases by country is illustrated in Figure 1.1a. The time series of cumulative reported COVID-19 cases of several leading countries and Europe since first reporting 10,000 COVID-19 cases is illustrated in Figure 1.1b.

Figure 1.1a: Global Distribution of Reported COVID-19 Cases by Country and Fraction (October 20, 2020, Adapted from Worldometers.info)


The United States of America (U.S.), constituting 50 states, four territories (e.g., Puerto Rico, Guam, the U.S. Virgin Islands, and the Commonwealth of the Northern Mariana Islands), and the District of Columbia, is currently the global epicenter of the COVID-19 Pandemic in terms of total reported cases, daily reported case rate, total reported deaths, and daily reported death rate with 8,453,185 reported COVID-19 cases from 127,054,590 laboratory tests and 225,191 reported COVID-19-related deaths, which accounts for 20.80% (case fraction), 17.06% (test fraction), and 20.06% (death fraction) of the corresponding worldwide counts, respectively, yielding a country-wide TR, PR, PIR, PMR, and PCFR of 38.32%, 6.65%, 25.50‰, 679 dpm, and 2.66%, respectively.

Figure 1.1b Time Series of Cumulative Reported COVID-19

Figure 1.1c Time Series of Cumulative Reported COVID-19 Deaths for Several Leading Countries since first reporting 100 COVID-19 Deaths (October 20, 2020)


The State of New York leads the other 49 states and territories in terms of total U.S. COVID-19-related deaths, as the former domestic epicenter of the COVID-19 pandemic, with 521,215 reported cases from 12,982,175 tests and 33,497 related deaths, which is 6.17%, 10.22%, and 14.87% of the corresponding U.S. counts, respectively, with a state-wide TR, PR, PIR, PMR, and PCFR of 66.73%, 4.01%, 26.79‰, 1,722 dpm, and 6.43%, respectively. New York City (NYC), in particular, previously the epicenter within the global epicenter in terms of total reported cases, has 245,086 reported cases from 4,428,340 reported laboratory tests with 23,782 related deaths[2], which is 50.59% (cases fraction), 44.37% (test fraction), and 71.66% (death fraction) of the corresponding state-wide counts, respectively, yielding a city-wide TR, PR, PIR, PMR, and PCFR of 52.45%, 5.53%, 29.03‰, 2,821 dpm, and 9.70%, respectively. Detailed global, country, and U.S. data may be found in the sequel in Chronology, Data, and Observations.

Of the five boroughs comprising NYC, in fact, across all counties comprising the state of New York, Queens County leads the other boroughs and counties in terms of cases (second only to Kings County in deaths) with 72,979 reported cases from 1,123,958 reported laboratory tests with 7,244 related deaths, which is 29.78% (case fraction), 25.38% (test fraction), and 30.46% (death fraction) of the corresponding city-wide counts, respectively, yielding a borough/county-wide TR, PR, PIR, PMR, and PCFR of 48.90%, 6.49%, 31.75‰, 3,152 dpm, and 9.93%, respectively. On the level of neighborhoods within the state of New York, Corona and North Corona (ZIP Code: 11368) leads all others in terms of both cases and deaths with 5,154 reported cases and 447 related deaths, representing 7.06% and 6.17% of the Queens County counts, respectively, yielding a ZIP code-wide PIR, PMR, and PCFR of 46.88‰, 4,066 dpm, and 8.67%, respectively.

Across the U.S. recently reported COVID-19 cases initially peaked on April 24, 2020 (39,130), then subsided to a low on June 7, 2020 (18,934). In early July 2020, however, due to premature lifting of Stay-at-Home orders and advisories as well as poor adoption of on-going safety measures, a widespread resurgence of COVID-19 outbreaks took place in as many almost all contiguous U.S. states, most notably Florida, California, and Texas, all of which have now overtaken New York in terms of total reported cases. Together, these four states comprise 3,035,890 reported cases (35.91% of U.S. cases), 43,849,970tests (34.51% of U.S. tests), and 84,122 reported deaths (37.36% of U.S. deaths). In particular, the State of California now leads the other 49 states, districts, and territories in terms of total reported COVID-19 cases with 880,871 cases (10.42% case fraction) from 17,042,408 tests (13.41% test fraction) and 17,001 related deaths (7.55% death fraction). Los Angeles County currently leads all U.S. counties in terms of reported COVID-19 cases with 261,446 reported cases (33.07% of CA), 2,552,055 tests (18.67% of CA), and 6,366 related deaths (42.24% of CA). Similar resurgence of outbreaks of COVID-19 have occurred in several other countries.

To add further uncertainty, however, all of the aforementioned counts may be significant underestimates that ignore or incorrectly discount many thousands, even millions of tested, infected, and/or deceased individuals as a result of COVID-19, including the pre-symptomatic, asymptomatic and/or mildly symptomatic carriers who do not seek laboratory testing, improperly documented due to innate errors with the testing procedure (i.e., faulty reagents and false negatives), and inaccurately documented cause(s) of death. As of writing, the CDC estimates that the reported COVID-19 cases in the U.S. are a factor of 6-24 times too low, and the reported COVID-19-related deaths are approximately 50% too low. Other studies involving random testing and RNA detection in sewage samples estimate a factor for infections closer to two orders-of-magnitude, or 50-85 times, too low. It should also be noted that the time from initial infection to onset of symptoms to laboratory testing to reporting of test results to local and federal officials is approximately 7-28 days, which may pose further difficulty with response efforts.

Despite questions of data accuracy, the rapid growth of cases in locales once hit cannot be argued. To illustrate the rapid acceleration of the COVID-19 Pandemic for the U.S., in particular, we consider two important early dates, February 29, 2020 and March 13, 2020, as well as the week of March 20-26, 2020, all of which were pivotal for the four countries, namely, China, Italy, Spain, and the U.S., which accounted for 57.45-61.35% and 70.82-75.84% of the global share of COVID-19 reported cases and related deaths, respectively, during this time frame. On February 29, 2020, the U.S. had only 68 (0.08%) reported cases and just 1 (0.03%) death, the first reported[3] U.S. death due to COVID-19, compared to the significantly advanced situation in China, the first country to sustain an epidemic from the initial outbreak in Wuhan, Hubei Province, which had 79,824 (92.17%) reported cases and 2,870 (96.41%) related deaths, the majority share of the corresponding worldwide counts of 86,604 and 2,977, respectively. Two weeks later, on March 13, 2020, the U.S. declared a State of Emergency due to the rapidly increasing number of confirmed COVID-19 cases both domestically and globally, namely, 2,183 (1.50%) reported cases and 48 (0.88%) related deaths stateside among the worldwide counts of 145,417 reported cases and 5,427 related deaths, respectively, compared to the apparently stabilized situation in China which had grown marginally to 80,824 (55.58%) reported cases and 3,189 (58.76%) related deaths.

Table 1.1: COVID-19 Reported Counts for China, Italy, Spain, and U.S. (March 20/26, 2020)
Reported Cases RC / Total Reported Deaths RD / Total
China 81,008 / 81,340 29.38% / 15.27% 3,255 / 3,292 28.41% / 13.33%
Italy 47,021 / 80,589 17.05% / 15.13% 4,032 / 8,215 35.19% / 33.27%
Spain 21,571 / 57,786 7.82% / 10.85% 1,093 / 4,365 9.54% / 17.68%
U.S 19,551 / 86,379 7.09% / 16.21% 309 / 1,614 2.70% / 6.54%
Subtotal 169,151 / 306,094 61.35% / 57.54% 8,689 / 17,486 75.84% / 70.82%
Earth 275,733 / 532,807 11,457 / 24,691

One week later, on March 20, 2020, the U.S. counts had grown by nearly an order of magnitude to 19,551 (7.09%) reported cases and 309 (2.70%) related deaths, consistent with exponential growth, among the 275,733 reported cases and 11,457 related deaths worldwide, which now involved all populated continents excluding Antarctica. At the time, the U.S. was fourth to China (81,008, 29.38%), Italy (47,021, 17.05%), and Spain (21,571, 7.82%) in terms of reported cases. By the end of the week, however, the U.S. surpassed all three countries and took the global lead in reported cases and later deaths, quadrupling its counts and becoming the new epicenter of the global pandemic. This rapid development was due in part to a convolution of delayed governmental and statewide responses, several potentially large seeding events (e.g., Mardi Gras parades, St. Patrick’s Day festivities, the Biogen Conference, etc.), outbreaks in locations with dense populations (e.g., long term care facilities, correctional institutions, and food packing factories, etc.), and the increasing availability of laboratory testing. During the same time frame, however, Italy and Spain had approximately doubled or trebled their reported cases from 41,021 to 80,589 and 21,571 to 57,786, respectively, causing a veritable crisis for their healthcare systems, while China remained anchored with a negligible increase from 80,928 to 81,340 reported cases, having added just 412 during the week in question. A comparison of the simultaneous daily growth of the cumulative COVID-19 reported cases for China, Italy, Spain, and the U.S. is shown in Figure 1.2. Corresponding counts of cumulative reported cases (RC), reported deaths (RD), and their global fractions for these four countries on the extremal dates March 20, 2020 and March 26, 2020 are given in Table 1.1.

Figure 1.2a: Cumulative Reported COVID-19 Cases of Four Leading Countries (March 20-26, 2020)


In an attempt to slow the progress of the escalating situation, as early as January 21, 2020, the WHO recommended a worldwide strategic response through ten key points including 1.) implementing measures to interrupt human-to-human transmission to reduce secondary infections, 2.) preventing amplification events such as social gatherings, 3.) reducing global spread through seeding events such as international travel, 4.) isolating and optimally caring for the infected, 5.) identifying and reducing transmission from the animal source, 6.) addressing crucial scientific and medical unknowns including clinical severity of COVID-19, 7.) accelerating development of diagnostics, therapeutics, and potential vaccines against SARS-CoV-2, 8.) communicating risk factors and event data, 9.) countering the spread of misinformation and disinformation, and 10.) mitigating societal and economic impacts. Unfortunately, while such directives are prudent and necessary, they require global cooperation, and they have not yet been fully adopted by all affected nations including, in particular, the U.S.


Table 1.2: Stage Distribution of Current Global COVID-19
Phase I
Phase II
Phase III
Limited
Approved
31
15
11
6
0



Figure 1.2 Global Clinical Trials


Despite this grave predicament, there remains hope. Due to the rapid mobilization of first responders, acceleration of collaborative scientific research, growing availability of diagnostic tests and experimental medications, including commencement of several clinical trials, fast-tracked governmental grant funding, and expedited Federal Drug Administration (FDA) approvals, several treatments for COVID-19 appear to be on the horizon. By October 19, 2020, there were 46 candidate vaccines in testing through various human trials (Table 1.2), with an additional 91 preclinical candidate vaccines in animal studies, and 17 drug treatments demonstrating some clinical promise. In support of these efforts, our pandemic response team of over 100 researchers, medical professionals, and content experts have mobilized across disciplines, international borders, and institutional affiliations in a large-scale and collaborative effort to develop a rapidly evolving work of scientific facts, data analysis, medical observations, and professional recommendations concerning SARS-CoV-2, the COVID-19 infection, and the resulting pandemic to aid the community at large with the understanding and development of potential therapeutics and vaccines. It is our shared belief that such a large and detailed resource is not only timely but also necessary as a valuable contribution to the existing literature during this on-going calamity.

Molecular Characteristics

Characteristics of Coronaviruses

It would be helpful to cite mutation rate relative to other viruses rather than to the host.RNA-viruses (or riboviruses) are viruses that use single or double stranded ribonucleic acid (RNA), but not deoxyribonucleic acid (DNA), as their constituent genomes. Such viruses are abundant in nature, vary markedly in virulence (the ability to cause harm to the host), and include the common cold, influenza, rabies, polio, measles, hepatitis C/E, West Nile virus, and the Ebola virus. RNA-viruses are distinguished by the polarity of their strands and include negative-sense, positive-sense, and ambi-sense genomes. Only the positive-sense can be immediately translated by the host cell, whereas the negative-sense and ambi-sense polarities require initial activation by an RNA-dependent RNA polymerase (RdRp) to be converted to positive-sense, that is, by transcription. Coronaviruses are positive-sense, single-stranded RNA-viruses.

RNA-viruses are highly infective, often virulent, and possess very high mutation rates relative to other viruses, up to a million times that of their hosts, which may present difficulty in discovering effective treatments such as specific and effective antiviral drugs and vaccines. In contrast, however, retroviruses like the Human Immunodeficiency Viruses, HIV-1 and HIV-2, that cause Acquired Immune Deficiency Syndrome (AIDS) and several adenoviruses that cause gastroenteritis, conjunctivitis, and cystitis are effectively treatable diseases, despite the fact that some evade the human immune system.

Figure 1.3a Digital Illustration of a SARS-CoV-2 Virion (CDC PHIL)


Coronaviruses comprise the Orthocoronavirinae subfamily in the Coronaviridae family within the Nidovirales order of the Riboviria realm that consist of all RNA viruses and viroids that replicate by RNA-dependent RNA polymerases. These viruses are coronal, meaning they are enveloped in spiked, crown-like outer proteins, which allows them to remain intact in mucosal droplets in air and on surfaces for several days. They are some of the largest RNA-viruses, with genomes of 26.4 to 31.7 kilobases (kb) in length. Four genera distinguish the known coronaviruses that infect humans (H), non-human mammals (M), and birds (B), namely, Alpha-CoV (H/M), Beta-CoV (H/M), Gamma-CoV (M/B), and Delta-CoV (B), and include the seven known human coronaviruses: HCoV-229E, HCoV-NL63, HCoV-OC43, HCoV-HKU1, Middle East Respiratory Syndrome Coronavirus or MERS-CoV, SARS-CoV-1, and now SARS-CoV-2 (Figure 1.3a, Table 1.3). In particular, the coronaviruses HCoV-229E and HCoV-OC43 are responsible for 10-15% of infections of the common cold. The rest is caused by rhinoviruses (10-40%), influenza viruses (10-15%), parainfluenza viruses (20%), adenoviruses (5%), respiratory syncytial virus (RSV), ortho- and metapneumovirus, certain enteroviruses, and unknown sources, more than 200 types in total. Of the most lethal human coronaviruses causing severe respiratory illnesses, SARS-CoV-1 and MERS-CoV are zoonotic Betacoronaviruses.

Other coronaviruses are known to infect only non-human mammals, in particular, only bats and pigs, including Swine Acute Diarrhea Syndrome Coronavirus or SADS-CoV, which caused the death of approximately 24,000 piglets in Guangdong Province, China. SADS-CoV is thought to have been transmitted through the fecal matter of several species of horseshoe bats including Rhinolophus sinicus, Rhinolophus pusillus, Rhinolophus rex, and Rhinolophus affinus. However, despite the genetic proximity of pigs to humans, it is believed that SADS-CoV cannot infect humans.


Table 1.3: Human Orthocoronavirinae
Coronavirus
Genus
Receptor
Disease
Reservoir(s)
Similar
HCoV-229E
Alpha-CoV
APN
Common Cold
Bats, Camels
-
HCoV-NL63
Alpha-CoV
ACE2
Respiratory Illness
Bats, Civets
-
MERS-CoV
Beta-CoV
DPP4
Severe Respiratory Illness
Bats, Camels
-
HCoV-OC43
Beta-CoV
N-acetyl-9-O-acetylneuraminic acid
Common Cold
Mice, Cattle
-
HCoV-HKU1
Beta-CoV
N-acetyl-9-O-acetylneuraminic acid
Respiratory Illness
Mice
Mouse Hepatic Virus
SARS-CoV-1
Beta-CoV
ACE2
SARS-02
Bats, Civets
-
SARS-CoV-2
Beta-CoV
ACE2
COVID-19
Bats, Pangolins
SARS-CoV-1


Coronaviridae is unique among the families of enveloped viruses to cause gastroenteritis; all other families of viruses that cause gastroenteritis are non-enveloped. SARS-CoV-2 is similar to SARS-CoV-1, the coronavirus responsible for the SARS Pandemic of 2002-2004 or SARS-02, both structurally, in modality, and possibly in origin. Electron micrographs of samples taken from infected patients in Wuhan, China reveal that the SARS-CoV-2 virion is spherical and approximately 60-140 nm in diameter (Zhu, N. et al., 2020), with an envelope roughly 82-94 nm in diameter, which is ¼-⅓ of the length of the smallest wavelength of visible light and is, therefore, effectively invisible. The spiked crown-like outer proteins are 9-20 nm in length. The genome of the SARS-CoV-2 virion is 29.8-29.9 kb (Zhou et al., 2020). In general, coronaviruses genomes consist of 7 genes organized in non-structural, structural and non-essential accessory protein coding regions. The non-structural protein coding region comprises the replicase gene (~⅔ of the genome), whereas the structural and non-essential accessory protein coding regions comprise the other 6 genes. After translation and post-translational modifications of proteins encoded in the replicase gene, 16 non-structural proteins form the Double-Membrane Vesicles (DMV), which forms part of the Replicase-Transcriptase Complex (RTC) (Fehr and Perlman, 2015). The RTC contains the viral RdRp, which is an attractive drug target for some antivirals. Four structural proteins are recognized and illustrated in Figure 1.3b: S (spike, red), M (membrane, orange), E (envelope, yellow), and N (nucleocapsid protein or nucleoprotein, indigo). The S, M, and E proteins comprise the viral envelope, which surrounds the nucleocapsid that houses the viral RNA (violet). The M protein is a transmembrane protein that connects the viral membrane to the nucleocapsid, and its C-terminal domain makes contact with the N protein.

Figure 1.3b: Cross-Sectional Illustration of SARS-CoV-2 (Adapted from Encyclopedia Britannica)


Characteristics of SARS-CoV-2 Genome

The SARS-CoV-2 genome is 79.6% identical to the SARS-CoV-1 genome and 96.2% identical to the genome of the Bat coronavirus BatCoV RaTG13 found in the species Rhinolophus affinis in Yunnan Province in China (Zhou et al., 2020). Bats are purported to serve as a reservoir for many different types of coronaviruses, but an intermediate species is often needed for transmission to humans, such as civets with SARS-CoV-1 and camels with MERS-CoV. While pangolin coronavirus (Pangolin-CoV) identified in Malay pangolins share a lower percentage of identical genomic sequence (91.2%) with the SARS-CoV-2 genome (Zhang et al., 2020), the receptor binding domain of the genome shares 99% of its sequence with SARS-CoV-2 (Xiao, T., et al., 2020). In particular, the receptor binding site of the spike protein of Pangolin-CoV is identical to the analogous site in SARS-CoV-2, with the exception of only one amino acid residue (ibid.). Furthermore, the receptor to which the virus binds in pangolin species is closer in structure to human ACE2 receptors, the entry point for SARS-CoV-2 in humans. Taken together, this evidence strongly suggests that bat and pangolin coronaviruses are ancestral to SARS-CoV-2. Genomic sequences that code for the spike protein present in pangolin species may have been instrumental in the transition of the mutated or recombined version of the virus to infecting human cells.

The SARS-CoV-2 genome consists of a total of 11 expressed genes with 11 Open Reading Frames (ORFs), namely, ORF1a, ORF1ab, ORF2, ORF3a, ORF4, ORF5, ORF6, ORF7a, ORF7b, ORF8, ORF9, and ORF10. The spike protein, membrane protein, nucleocapsid protein, and the envelope protein, which are the four main structural proteins, are encoded by the ORF2, ORF5, ORF9, and the ORF4 genes, respectively. A table of these genes, the proteins that they encode, the number of amino acids in each protein, and the gene locations as found in the SARS-CoV-2 Wuhan-Hu-1 isolate (NCBI reference sequence: NC_045512.2) are given in Table 1.4.

The SARS-CoV-2 genome is notable because it contains one of the lowest proportions of CpG sites in its genome of any Betacoronavirus yet identified (Xia, X. et al., 2020). CpG sites serve as binding sites for Zinc finger activating protein (ZAP), a protein that activates other proteins to degrade viral RNA. Many RNA viruses have adapted over time by reducing the percentage of CpG sites they contain, thereby increasing their overall virulence. However, because ZAP expression is highly tissue specific, only viruses that target particular tissue layers where ZAP is more robustly expressed will show CpG deficiencies. Of 56 Betacoronavirus genomes tested from Rhinolophus bats, only BatCoV RaTG13, which shares 96% homology with SARS-CoV-2, demonstrated an extreme CpG deficiency (Xia et al., 2020). The authors suggest that SARS-CoV-2 and BatCoV RaTG13 evolved from mammalian tissues with high ZAP expression. The species in which the viruses evolved was not likely a bat, since other bat Betacoronaviruses do not show this same deficiency. In fact, of the 927 Betacoronavirus genomes the authors searched, no other genomes demonstrated similarly low CpG percentages found in SARS-CoV-2 and BatCoV RaTG13. However, the authors identified some highly infectious canine Alphacoronaviruses that infect the intestinal and respiratory tract that share a similar level of CpG deficiency.

One of the major distinguishing features between the SARS-CoV-1 and SARS-CoV-2 genomes is their ORF3b genes (open reading frame 3b). In particular, SARS-CoV-2 codes for a premature stop codon, which results in a truncated protein product when compared to its SARS-CoV-1 counterpart (22 amino acids in length versus 153 residues in SARS-CoV-1). Kopecky-Bromerg et al. (2007) report that the ORF3b protein of SARS-CoV-1 acts as a potent IFN-I antagonist, thereby inhibiting the activity of IFN-I activity, and so it is natural to ask how the truncated version of the ORF3b protein of SARS-CoV-2 affects type 1 interferon inhibition. Konno et al. (2020) report that the truncated SARS-CoV-2 ORF3b protein is more effective at suppressing the activation of type 1 interferon than the analogous SARS-CoV-1 protein. It is important to note that enhancement of type 1 interferon suppression is associated with a more severe COVID-19 clinical course. The authors also find that SARS-CoV-2 related viruses from bats and pangolins also encode for a similarly shortened version of the protein with enhanced type 1 interferon inhibition.

Characteristics of SARS-CoV-2 Proteins

The SARS-CoV-2 proteome is made up of several different types of proteins. The gene ORF1a encodes a large replicase polyprotein known as pp1a. During the translation of the ORF1a gene, a ribosomal frameshift to the adjacent ORF1b can occur, which will produce the larger of the two replicase polyproteins, pp1ab. Together, pp1a and pp1ab can undergo proteolysis to form 16 different non-structural proteins. Many of these non-structural proteins can recombine to form important replication machinery such as helicase and the RNA-replicase-transcriptase complex. The SARS-CoV-2 proteome also contains the four structural proteins characteristic of all coronaviruses: the Spike protein (S), the Envelope protein (E), the Membrane protein (M), and the Nucleocapsid protein (N) (see Characteristics of Coronaviruses). These four proteins are encoded by the ORF2, ORF4, ORF5, and ORF9 genes, respectively. Finally, there are also six known accessory proteins, namely, the ORF 3a, ORF6, ORF7a, ORF7b, ORF8a, and ORF8b. Table 1.4 lists the 11 expressed genes of the SARS-CoV-2 genome as found in the SARS-CoV-2 Wuhan-Hu-1 isolate, as well as their locations, the proteins they encode, and the size of these proteins. Table 1.5 lists the 16 non-structural proteins that are proteolytic products of the ORF1a and ORF1ab polyproteins. Figure 1.4 illustrates the portions of the SARS-CoV-2 genome that encode each of these proteins.

Figure 1.4 SARSCoV-2 Genome and Protein Map (Adapted from Gordon et al., 2020)



Table 1.4: Expressed Genes and Protein Products of SARS-CoV-2 Wuhan-Hu-1 Isolate

Gene
Nucleotide Location (from 5’ UTR)
Protein Expressed
Molecular Weight (Daltons)
Number of Amino Acids
ORF1ab
266-21,555
ORF1ab Polyprotein (pp1ab)
793,931
7,096
ORF1a
266-13,483
ORF 1a Polyprotein (pp1a)
489,861
4,405
ORF2
21,563-25,384
Spike Protein (S)
141,048
1,273
ORF3a
25,393-26,220
ORF3a Protein
30,992
275
ORF4
26,245-26,472
Envelope Protein (E)
8,234
75
ORF5
26,523-27,191
Membrane Protein (M)
25,016
222
ORF6
27,202-27,387
ORF6 Protein
7,141
61
ORF7a
27,394-27,759
ORF7a Protein
13,613
121
ORF7b
27,756-27,887
ORF7b Protein
5,049
43
ORF8
27,894-28,259
ORF8 Protein
13,700
121
ORF9
28,274-29,533
Nucleocapsid Protein (N)
45,495
419
ORF10
29,558-29,674
ORF10 Protein
4,318
38


Table 1.5: Non-Structural Proteins (NSPs) of SARS-CoV-2 Wuhan-Hu-1 Isolate (Adapted from Yoshimoto et al., 2020)
Protein
Molecular Weight (Daltons)
Number of Amino Acids
Possible Function
NSP1 / leader protein
19,644
180
Ribosomal protein leader
NSP2
70,512
638
Binds to host PHB1 and PHB2 proteins
NSP3 / Papain like protease
217,254
1,945
Release NSPs 1,2,3
NSP4
56,184
500
Membrane rearrangement
NSP5 / 3C-like proteinase
33,797
306
Cleaves at 11 sites of NSP polyprotein
NSP6
33,034
290
Generates autophagosomes
NSP7
9,240
83
Dimerizes with NSP8
NSP8
21,881
198
Stimulates NSP12
NSP9
12,378
113
May bind to helicase (NSP14)
NSP10
14,790
139
May stimulate NSP16
NSP11
1,326
13
Unknown
NSP12 / RNA Dependent RNA Polymerase
106,661
932
Copies viral RNA; guanine methylation
NSP13 / helicase
66,855
601
Unwinds duplex RNA
NSP14 / 3’-5’ exonuclease
59,816
527
5’cap RNA
NSP15 / EndoRNAse
38,815
346
Degrade RNA to evade host defense
NSP16 / 2’-O-ribose methyltransferase
33,324
298
5’-cap RNA; adenine methylation


SARS-CoV-2 Spike (S) Protein

The SARS-CoV-2 spike protein shares 76% amino acid sequence homology with the SARS-CoV-1 protein (Grifoni et al.), demonstrating less amino acid conservation than that found in other portions of the SARS-CoV-2 proteome. All coronaviruses by definition encode the spike protein, which is responsible for recognizing and binding to the cell surface receptor of the host. The SARS-CoV-2 spike protein binds to the ACE2 (angiotensin-converting enzyme 2) receptor, which is expressed in the human heart, kidney, esophagus, bladder and lungs (Zou et al., 2020). Within the lungs, ACE2 is primarily expressed in the type 1 and type 2 alveolar epithelial cells, which are more common in the lower respiratory tract, and some reports suggest ACE2 is also expressed in ciliated bronchial epithelial cells. The SARS-CoV-2 spike protein binds with at least ten times greater affinity to the ACE2 receptor than the SARS-CoV-1 spike protein (Wrapp et al., 2020). The spike protein may also bind with high affinity to the CD147 receptor (with a dissociation constant of 185 nM), also known as basignin, which is an immunoglobulin that determines the antigen expressed in the Ok blood group system (Wang, K. et al., 2020). Using this receptor to mediate viral invasion, SARS-CoV-2 was able to successfully infect Vero E6 cells in vitro. The use of Meplazumab, an anti-CD147 antibody, significantly inhibited viral infection of the same cell line.

Figure 1.5a: SARS-CoV-2 Spike (S) Protein (Adapted from Walls et al., 2020)


Identifying the structure of a spike protein can provide key insights for drug and vaccine design. Cryo-electron microscopy has revealed the structure of the SARS-CoV-2 spike protein (Figure 1.5a) and its chemical binding affinities (Walls et al., 2020). The spike protein is composed of three heavily glycosylated protomers that have identical amino acid sequences but different three-dimensional conformation. Each monomer contains an N-terminal S2 domain and a C-terminal S1 domain, where many of the beta sheets of the protein are located (ibid.). The C-terminus also contains the receptor binding domain (RBD) of the spike protein, while the N-terminal S2 domain contains a fusion peptide that allows the membrane of the virion to bind to the host cell membrane. For the fusion peptide to be exposed, the spike protein must be cleaved by cell proteases, specifically the serine protease TMPRSS2. Each protomer contains a receptor binding domain, two of which are in a lower energy and more stable “down” conformation and one that is in an active “up” conformation. The receptor binding domain in the “up” confirmation is less energetically stable, which primes it for binding to the ACE2 protein. Hydrogen bonding between key amino acids of ACE2 and the RBD create strong attractive intermolecular forces between the molecules. Once a virion fuses to the host cell membrane, the virion can release its RNA into the intracellular space, thereby infecting the cell. Inhibitors of TMPRSS2 (such as Camostat Mesylate) provide possible future treatment options, and such protease inhibitors have shown promising results in cell culture and animal models (Hoffmann et al., 2020).

Coutard et al. (2020) identify a feature that is unique to SARS-CoV-2 that may distinguish it from other betacoronaviruses to which it shares extensive homology: the presence of a unique furin-like cleavage site in the spike protein located at the S1/S2 boundary. Furin is an enzyme, expressed abundantly in the lungs, that catalyzes the proteolysis of a precursor protein at a particular cleavage site, activating the protein functionally. In the SARS-CoV-2 spike protein, a furin-like protease cleaves the spike protein at the S1/S2 site, thereby enhancing the protein’s ability to bind to the cell membrane fusion site for intracellular entry. This site is notably absent in the SARS-CoV-1 spike protein (as well as the RaTG13 spike protein), and the feature is speculated to play a role in the enhanced infectivity of SARS-CoV-2. Figure 1.5b illustrates a single protein subunit of the homotrimer of the SARS-CoV-2 spike protein, C-terminus (violet), N-terminus (blue), central helix (orange), and the ACE2 binding domain (magenta).

SARS-CoV-2 Nucleocapsid (N) Protein

In contrast to the spike protein, the nucleocapsid (N) protein is highly conserved across all human beta-coronaviruses. In an effort to identify T and B cell epitopes of SARS-CoV-2, Grifoni et al. (2020) looked at areas of high amino acid sequence homology between sequences identified as part of the SARS-CoV-1 epitope and corresponding sequences from the SARS-CoV-2 proteome. Of the 10 nucleocapsid amino acid sequences compared, 8 had sequence homology over 85%, making the N protein the most conserved epitope identified, followed by the membrane protein epitope. When comparing genomic sequences using the NCBI databank, Kang et al. (2020) determined that the SARS-CoV-2 N protein encoding region shared 89.74% homology with the corresponding region in the SARS-CoV-1 genome.

The N protein serves several important functions in the life cycle of the SARS-CoV-2 virion. In addition to housing the viral RNA, it is known to play various pivotal roles during viral self-assembly (Chang et al. 2014). There is also evidence to suggest it may have the ability to modify the host cell metabolism and may also modify host-pathogen interaction, enabling the virus to evade host cell recognition. Perhaps most importantly, the N protein binds to the viral RNA at multiple sites, packaging the RNA into a helical nucleocapsid structure known as the ribonucleoprotein complex.


Figure 1.5b: ACE2 Binding Domain and Homotrimer with highlighted protein subunit of SARS-CoV-2 Spike (S) Protein. C-terminus (violet), N-terminus (blue), central helix (orange), and the ACE2 binding domain (magenta) (Adapted from Wikipedia)


Understanding the structural features of the SARS-CoV-2 N protein may provide insight into the design of therapeutics, as well as vaccines which can target known regions of the protein’s epitope. Nucleocapsid proteins of coronaviruses commonly include three conserved regions, which include the N-terminal RNA-binding domain, a C-terminal dimerization domain whose primary purpose is for oligomerization, and a central Serine-Arginine rich linker used for phosphorylation (Kang et al., 2020). Kang et al. (2020) determined the crystal structure of the N-terminal domain of the N protein using X-ray crystallography methods. They found that the N-terminal domain crystals pack in an orthorhombic form, with each crystal unit made up of four monomers. The monomers contain two loop regions adjacent to a β-sheet core that is composed of five antiparallel β-strands. The N-terminal domain contains regions enriched with basic and aromatic residues, forming what the authors characterize as the shape of a right hand, complete with an acidic wrist, basic palms, and basic fingers. Figure 1.6 illustrates these three areas on the N-terminal domain of the N protein. The figure also reveals the electrostatic surface potential of the protein: blue designates a positive charge potential and red a negative charge potential. The authors go on to compare the structure of the N-terminus to that found on SARS-CoV-1, MERS-CoV, and HCoV-OC443, finding that the basic palm region contains the highest frequency of conserved residues. While many regions are well conserved, the surface charge distributions on the respective N protein N-terminus differ dramatically, largely due to the relative positioning of the beta sheets located in the core. In particular, a protruding hairpin between two β-strands is less extended in SARS-CoV-2, creating a loosened N-terminal tail.


Figure 1.6: N-Terminal Domain of the SARS-CoV-2 Nucleocapsid Protein (Adapted from Kang et al., 2020)


SARS-CoV-2 Envelope (E) and Membrane (M) Proteins

The envelope of SARS-CoV-2 is a highly conserved, small protein made up of 75 amino acids, one less than the number found in the SARS-CoV-1 envelope protein. Overall, the two primary amino acid sequences share 94.7% homology. The envelope protein is found in all coronaviruses and has been shown to have the potential to oligomerize and form ion channels in the lipid membrane surrounding the nucleocapsid. It also may play roles in several stages of the viral replication cycle, specifically in viral assembly and virion release (Yoshimoto, 2020). The SARS-CoV-2 membrane (M) protein is made up of 222 amino acids, one more than the number found in the corresponding SARS-CoV-1 protein. Overall, the SARS-CoV-1 and SARS-CoV-2 M proteins share 90.5% sequence homology. Like the envelope protein, the membrane protein is an integral membrane protein that may play a role in viral assembly (Yoshimoto, 2020). Tsoi et al. (2014) have previously shown that the SARS-CoV-1 M protein can induce host cell apoptosis, and so it is likely that the SARS-CoV-2 protein may have similar action. The M protein is also significant because it interacts with the nucleocapsid protein, together forming a capsule around the viral RNA.

SARS-CoV-2 Non-Structural Proteins

There is some striking structural similarity between the SARS-CoV-1 and SARS-CoV-2 proteomes, particularly in amino acid sequences encoded from highly conserved Open Reading Frames (ORFs) found in their respective genomes. Of particular note are amino acid sequences from seven conserved replicase domains from ORF1ab polyprotein (pp1ab), which have been shown to be 94.4% identical (Zhou, P. et al., 2020).

The 16 non-structural proteins are all proteolytic products of either the pp1a or pp1ab polyproteins. They are believed to serve a variety of functions which are briefly outlined in Table 1.5. Some of the possible functions can be inferred from the known activity of the corresponding protein found in SARS-CoV-1, particularly when there is substantial homology between the sequences. For example, the NSP2 protein of SARS-CoV-1, which shares 68.3% of the same amino acid sequence as the SARS-CoV-2 NSP2 protein, is known to bind to two host proteins: prohibitin 1 and prohibitin 2 (Cornillez-Ty et al., 2009). Since PHB1 and PHB2 are involved in host mitochondrial biogenesis and intracellular signalling, it is believed that NSP2 may dysregulate the host cell function. For this reason, it is speculated that the corresponding protein in SARS-CoV-2 likely shares a similar function.

The SARS-CoV-2 NSP3 protein or Papain-like protease is approximately 217 kDa and is thus the largest protein encoded by SARS-CoV2 and shares 76.0% amino acid sequence identity with the NSP protein of SARS-CoV-1. The protein contains many highly conserved regions, which include the ssRNA binding domain, the ADPr binding domain, the G-quadruplex binding domain, the protease domain, the NSP4 binding domain, and a transmembrane domain (Yoshimoto, 2020). The protease domain of Papain-like proteases of other coronaviruses are known to release NSP1, NSP2, and NSP3 from the N-terminal regions of the two precursor polyproteins, making the inhibition of NSP3’s protease activity a target for future antiviral therapies.

ACE2 Receptor

The Angiotensin-converting Enzyme 2 (ACE2) receptor has been identified as the cellular entrypoint for SARS-CoV-2 (Zhou et al., 2020), as well as for other human coronaviruses such as SARS-CoV-1 and HCov-NL65. After cleavage by a serine protease, the SARS-CoV-2 spike protein can bind with high affinity to ACE2 and enter the cell. The receptor binding domain (RBD), a short sequence of amino acids located near the C-terminus of the spike protein, is the region of the protein that binds to the ACE2 receptor through strong attractive intermolecular forces (see Figure 1.7). After binding to the receptor, the spike protein purportedly can take ACE2 with it, as it does with SARS-CoV-1 and the SARS-02 infection (Wang, H. et al., 2008), where it can be intracellularly degraded by lysosomes. Note that SARS-CoV-1 is thought to cause severe lung failure by binding to ACE2 and causing its downregulation (Kuba et al., 2005).

ACE2 is expressed abundantly in the cell membranes of type I and type II pneumocyte cells, the epithelial cells that line the alveolus, a sac-like structure of the lung where gas exchange with the blood capillaries occurs. ACE2 and TMPRSS2, the serine protease that cleaves the N-terminal S2 subunit of the spike protein that exposes the fusion peptide necessary to bind to ACE2, are co-expressed most abundantly in the type II pneumocytes, absorptive intestinal cells, and nasal goblet secretory cells (Ziegler et al., 2020). Furthermore, Ziegler et al. report that interferon may stimulate the upregulation of ACE2 in human epithelial cells. Interferon is typically expressed to enhance antiviral activity during the initial stages of viral infection. However, the interferon-driven enrichment of ACE2 expression may provide coronaviruses more opportunities for successful intracellular transport and infection.

ACE2 is perhaps most well-known for the role it plays in the Renin-Angiotensin system (RAS). This system regulates many physiological processes, such as blood pressure and fluid regulation in the human body. The system is initiated by the hormone angiotensinogen, which is secreted by the liver. The hormone can be converted to angiotensin I by renin, an enzyme secreted by the kidneys when blood flow to the kidneys is reduced. In the lung, angiotensin I is converted to angiotensin II by ACE (note that ACE is different from ACE2). Angiotensin II is a powerful vasoconstrictor (a substance that constricts blood vessels), and it also stimulates the adrenal glands to produce aldosterone, which in turn reduces potassium and increases sodium concentration in the body. Furthermore, angiotensin II stimulates the central nervous system to produce vasopressin. Combined, these effects increase blood pressure and water retention.

The concentration of angiotensin II determines the action of ACE2 in RAS. When angiotensin II levels are low, ACE2 will bind to angiotensin receptor 1 and will cleave angiotensin II to produce angiotensin (1-7). Angiotensin (1-7) has both a vasodilative and anti-inflammatory effect. When angiotensin II levels are high, angiotensin receptor 1 separates from ACE2. Angiotensin receptor 1 then interacts with angiotensin II, leading to vasoconstriction, increased blood pressure, and increased pulmonary permeability, which can contribute to Acute Respiratory Distress Syndrome (ARDS).

Figure 1.7: SARS-CoV-2 Receptor Binding Domain (crimson) bound to ACE2 (green) (Adapted from Lan, J, et al ., 2020)


ACE2 may have a powerful pulmonary protective effect in humans. Imai et al. (2005) bred ACE2 knockout mice (mice that are deficient for the genes that code for ACE2) and compared them to wildtype mice (regular mice expressing ACE2) after subjecting both groups to acute pulmonary injury by acid aspiration and viral pneumonia induced by SARS-CoV-1. Acid aspiration in mice models acute lung injury in humans, leading to pulmonary edema, increased inflammation, and lowered blood oxygenation. The knockouts showed markedly increased stiffness in the lung tissue when compared to their wildtype counterparts. The knockout mice also showed higher levels of angiotensin II and angiotensin 1 receptor (since there was no ACE2 to which it could bind), which was associated with increased lung edema and worsened outcomes with viral pneumonia. When the knockouts were injected with a recombinant human ACE2 protein, lung stiffness was reduced and overall condition significantly improved. Penninger’s group later used this research to develop APN01, a soluble ACE2 drug intended to treat SARS-02 produced by APEIRON, and it is now in a clinical trial for use in treating COVID-19. The mechanism behind the action is simple: by overwhelming the spike protein of the virus with soluble ACE2, the virus’s ability to bind to the ACE2 receptors in the cellular membrane is drastically reduced.

In 2005, another team also led by Penninger was able to show that SARS-CoV-1 infection and the virus’s spike protein significantly reduced levels of ACE2 in wildtype mice infected with the virus. Furthermore, ACE2 knockout mice showed markedly worsened conditions and decreased recovery rates from infection. Lung injury caused by the virus was also mitigated when the RAS pathway was blocked (Kuba et al., 2005). These results point to the important protective role that ACE2 may play during lung injury. ACE2 may be protective against the inflammatory mechanisms in COVID-19 that lead to ARDS. A mouse model from the Baric group at UNC holds promise for being able to utilize a human ACE2 gene insertion transgenic animal to screen for new treatments (Dinnon et al., 2020).

Angiotensin receptor blockers, which include drugs like Losartan and Telmisartan, are used clinically to treat high blood pressure. They do so by keeping angiotensin receptor 1 bound to ACE2, leaving ACE2 to continue to catalyze the conversion of angiotensin II into angiotensin (1-7). The use of ARBs has been tied to the upregulation of ACE2 (Li XC et al., 2017), which leads to an increase in the production of ACE2 in the body. This has led some researchers to hypothesize that the use of ARBs may contribute to worsened COVID-19 outcome, since ACE2 is the receptor for the virus that causes the disease (Fang et al., 2020). However, Kuba et al. (2005) show that ACE2 upregulation may lead to improved conditions in both SARS-type infections and in acute lung injury. Furthermore, increased soluble ACE2 is already an effective therapy in the treatments of ARDS.

Replication Cycle

It is believed that SARS-CoV-2 can enter the cell in one of at least two ways, either through an endocytic pathway or by fusing directly to the plasma membrane. Both pathways are mediated by the ACE2 receptor, although Wang, K., et al. report that infection may also be mediated by the CD-147 receptor, which the spike protein must bind to first. In order to be shuttled into the cell via endosomes, the virus’s spike protein must be activated by cathepsin L (Ou et al., 2020). Cathepsin L is a cysteine protease that catalyzes the cleavage of the spike protein, a process that begins through the deprotonation of a thiol group from an adjacent basic side chain, such as an imidazole group from a histidine residue. Ou et al. (2020) were able to successfully decrease intracellular entry of SARS-CoV-2 into 293/hACE2 kidney cells by 99% through incubating the cells in 20 mM ammonium chloride or 100 nM bafilomycin A, two known inhibitors of cathepsin L. This result shows that in this particular kidney cell line, endocytosis was the primary means of cell entry for SARS-CoV-2. Endocytosis occurs through the invagination of the cell membrane, which surrounds the virus. A portion of the membrane will pinch off from the rest, bringing its fully encapsulated contents into the intracellular space.

The other pathway in which SARS-CoV-2 gains cellular entry is through direct fusion of the viral membrane to the cell membrane. In order to do this, the spike protein must be activated and cleaved by the TMPRSS2 serine protease (Hoffmann et al., 2020). Previous studies have shown that other human coronaviruses, such as HCoV-229E, HCoV-OC43, and HCoV-HKU1, prefer direct fusion with the cell membrane over endocytosis when infecting human airway epithelial cells (Shirato et al., 2017). Furthemore, direct fusion with the membrane enables the virus to evade host cell antiviral immunity, thereby allowing for enhanced SARS-CoV-2 replication (ibid.), whereas endocytic entry activates host cell immunity more pronouncedly, diminishing viral replication. SARS-CoV-2 likely has a similar preference for direct fusion in the pneumocytes of the respiratory tract, possibly explaining its enhanced replication in these cells. Camostat mesylate is a serine protease inhibitor that acts to inhibit TMPRSS2, making it a potential candidate for treatment of COVID-19. Hoffman et al. (2020) have reported that this protease inhibitor successfully reduces SARS-CoV-2 infection in Calu-3 lung cells.

Once SARS-CoV-2 translocates into the cell, its envelope and nucleocapsid proteins are uncoated, which releases the positive-sense viral RNA into the cytoplasm. The RNA can be immediately translated by the host cell, resulting in the production of two replicase polyproteins known as pp1a and pp1ab. The ORF1a domain of the SARS-CoV-2 genome translates directly into the pp1a polyprotein. However, because of a ribosomal frameshift site between ORF1a and ORF1b, during translation of ORF1a, a frameshift to the adjacent open reading frame (ORF1b) can occur, resulting in the production of pp1ab. These polyproteins undergo proteolysis in the cell, resulting in smaller proteins that can recombine to form helicase or the RNA replicase-transcriptase complex (RdRp). The RdRp (RNA dependent RNA polymerase) is especially essential, as it allows the virus to replicate. Proteases such as Papain-like protease and 3C-like protease catalyze the proteolytic process that lead to the production of the RNA replicase-transcriptase complex and may be potential targets for inhibition in future drug therapies (Zhavonronkov et al., 2020).

The viral RNA can then be replicated using the newly formed RdRp, forming new antisense RNA strands in the host cell cytoplasm. Due to its polarity, the antisense RNA cannot be translated in the same way that the initial positive-sense RNA that first entered the cell can be. However, the antisense RNA can be replicated back into positive-sense RNA, where it can be repackaged as virion in the viral offspring. The antisense RNA can also be transcribed via discontinuous transcription, which can result in various lengths of mRNAs transcripts (known as subgenomic mRNAs), all of which can be translated into different proteins, such as the structural protein components that make up the viral envelope. These structural proteins, which include new spike, envelope, and membrane, and nucleocapsid proteins, are synthesized on the surface of the rough endoplasmic reticulum of the host cell. These proteins, along with the newly synthesized positive-sense RNA, are shuttled to the Golgi apparatus, where they are put into vesicles and packaged as new, fully functional viral offspring. The vesicles can fuse to the membrane, allowing the virus to be released extracellularly. In turn, the newly formed viral offspring can attach to new cells and initiate the replication cycle again. In infected Vero E6 cells, SARS-CoV-1 and SARS-CoV-2 replicate at similar rates, peaking at 48 hours after post infection (Lokugamage, 2020). Figure 1.8 illustrates the sequence of events that take place during SARS-CoV-2 replication, from its initial transport into the cell, to its subsequence replication, translation, assembly, and exocytosis from the cell.

A combination platform that includes structure-assisted drug design, as well as virtual and high throughput drug screening (using FRET assays and a drug library of over 10,000 compounds), has revealed the crystalline structure of the main protease (Mpro), which is implicated in viral replication and transcription, and has identified several candidate drugs that have potential to deactivate or target this protease (Jin et al., 2020). Based on the structure of the protease, two drugs, Ebselen and N3, showed great promise at inhibiting the protease and thereby the virus’s replication. Furthermore, the organoselenium compound, Ebselen, shows extremely low cytotoxicity and anti-inflammatory, antioxidant, and cytoprotective properties from studies of its action in other diseases.

Two papers from May 2020 have raised additional issues to the matter of proteolytic activation of the spike protein and entry of SARS-CoV-2 into target cells. Jaimes et al. (2020) have explored the cleavage of the S1/S2 site by furin as well as a variety of other proteolytic enzymes. They used a biochemical peptide cleavage assay in which relevant peptides from the S1/S2 region are coupled to a Mca/Dnp FRET pair so that fluorescence is observed only after peptide cleavage. The peptide sequences studied were HTVSLLR|STSQ corresponding to SARS-CoV S1/S2 region and TNSPRRAR|SVA corresponding to the S1/S2 region of SARS-CoV-2. They investigated the ability of a variety of proteases including furin and PC1, trypsin, the type II transmembrane serine protease (TTSP) matriptase, and cathepsins B and L to cleave these peptides at the RS site. As hypothesized, furin cleaved the SARS-CoV-2 peptide but not the SARS-CoV peptide. Other proteases with the exception of cathepsin L were also more active at cleaving the SARS-CoV-2 peptide. The authors speculated that the insertion of the highly basic peptide PRRA prior to the RS cleavage site as well as the leading proline may have increased the accessibility of this sequence for proteolytic cleavage. They proposed that while furin cleavage may be the dominant mode of spike activation, other enzymes such as TMPRSS2 and matriptase may also be involved. The authors acknowledge a key limitation of the study, that it was conducted on model peptides mimicking the S1/S2 cleavage site and that the conformation of this, in addition to its susceptibility to cleavage by a variety of the studied proteases, may be quite different in the intact full-length protein. These results are suggestive and await further studies of cleavage of the S1/S2 site in intact full-length proteins.


Figure 1.8: SARS-CoV-2 Replication Cycle (Adapted from Shereen et al., 2020)


A different approach has been taken by Anand et al. (2020) who have published in eLife on May 26, 2020 a study in which he and researchers at nference, Inc., an artificial intelligence company, sought to investigate other instances in which the cleavage site in SARS-CoV-2 (RRAR|SVAS) might be identified in other proteins. The sequence of PRRA upstream from the cleavage site is not seen in other coronaviruses but mimics a site found exclusively in the human epithelial channel α-subunit (ENaC-α). They also found that on review of single-cell RNA-seq data from 65 studies that there was marked overlap between expression of ENaC-α and the viral receptor ACE2, particularly in tissues such as heart, lung, and kidney that bear much of the brunt of damage in severe COVID-19. They postulate that the spike protein may, by its mimicry of the ENaC-α furin cleavable site, result in reduction of tissue levels of ENaC-α leading to dysregulation of fluid balance in these tissues. This is a mechanism quite distinct from the inflammatory changes typically implicated in pulmonary damage due to COVID-19. This theoretical study provides no data that in fact there are alterations in ENaC-α levels or activity or abnormalities in airway surface liquid homeostasis seen in tissues undergoing such damage (Olena, 2020).

The aforementioned studies both require validation of their predictions in more relevant cell systems before they can be explored as candidates for antiviral therapy. The use of peptidomimetic inhibitors of furin and other proteases involved in spike activation is appealing, but may be limited by effects on other relevant cellular processes (Hoffmann, 2020).

As we continue to define the cell biology of coronaviruses like SARS-CoV-2 using higher resolution methods including electron microscopy, we determine additional vulnerabilities in the viral replication machinery. One such study has found a molecular pore complex in the membrane of the virus through which the transfer of viral RNA into the host cytosol may occur, allowing viral RNA to be eventually packaged into new exosome-like vesicles (Candelario and Steindler, 2014) and virions (Wolff G et al., 2020). This mechanism of replication, now better understood because of deep cell and molecular biological analysis, represents another target for replication-mitigating drug design.

Interaction with the Immune System

Since SARS-CoV-1 has previously been shown to downregulate type I interferon production and response in its host (Kopecky-Bromberg et al., 2007), thereby diminishing the innate immune response of the host, questions surrounding the ability of SARS-CoV-2 to do the same naturally arise. The innate immune system initiates its attack on a viral pathogen when PRRs (pattern recognition receptors) in a sentinel cell, usually a macrophage, detect characteristic viral motifs known as pathogen-associated molecular patterns (PAMPs). This detection leads to changes in gene expression in the sentinel cell, which initiates a release of cytokines and chemokines that recruit other leukocytes (white blood cells) to the site of infection. These leukocytes include neutrophils that can attack the infected cells directly, without the use of antibodies. The innate immune system is also initiated when the infected host cell begins to secrete Type I interferons (e.g. IFN-ɑ and IFN-β). Type I interferons are proteins that interfere with viral replication and recruit natural killers (NK) cells to the site of infection. The NK cells kill infected cells by releasing granules that contain apoptotic inducing granzymes, which initiate apoptosis (cell death), and perforin, which forms pores that perforate the cell membrane. Type I interferons can initiate a cascade of signalling proteins through its receptor, which in turn activates STAT1 (Signal Transducers and Activators of Transcription 1), STAT2, and other transcription factors through phosphorylation. These activated transcription factors can then upregulate IFN-stimulated genes (ISGs) by increasing the rate of their transcription and subsequent translation, producing proteins that will enhance the host’s antiviral response.

Lokugamage et al. (2020) reported that when Vero E6 cells were pretreated with IFN-α 18 hours before infection with either SARS-CoV-1 or SARS-CoV-2, the amount of virus detected 48 hours after infection differed substantially between the two groups. While pre-treated SARS-CoV-1 infected cells had almost the same viral titer as SARS-CoV-1 infected cells that were not pre-treated with interferon, the pre-treated SARS-CoV-2 infected cells showed significant reduction in viral titer (a 3-log to 4-log drop) compared to their untreated counterparts. Furthermore, the authors reported that production of the nucleocapsid protein of the virus was significantly reduced for SARS-CoV-2 infected cells that received IFN-α pre-treatment. These results suggest that SARS-CoV-2 is much more vulnerable to the antiviral effects of IFN-α. SARS-CoV-2 cells that received interferon-alpha pretreatment were also the only cells that showed any STAT1 phosphorylation and that demonstrated enhanced production of ISG proteins. These data suggest that SARS-CoV-2 is not as effective as SARS-CoV-1 at modulating the response of a type I interferon. On the other hand, it should be noted that SARS-CoV-1 is remarkably adept at regulating the type I interferon response, as pre-treatment with interferon led to no detectable STAT1 phosphorylation, just as in the infected cells that were not treated with interferon.

Among the protein products that result from the cleavage of precursor proteins 1a and 1ab are a set of functional proteins that include nonstructural protein 1 (Nsp1). Nsp1 isolated from various alpha and beta coronaviruses have consistently demonstrated the ability to effectively suppress host gene expression by inhibiting protein translation in the host cell. They do so by binding to the small ribosomal subunit (40S), thereby impeding mRNA translation. When Nsp1 binds to the ribosome, the host mRNA transcript that would have been translated is cleaved and later degraded. Thus, Nsp1 has the potential to inhibit any antiviral response that is dependent on the expression of host cell factors, such as the interferon response. Thoms et al. (2020) demonstrate that Nsp1 from SARS-CoV-2 can bind to the 40S but not to the 60S ribosomal subunit. The binding resulted in the loss of capped mRNA translation in cells and in vitro studies. The researchers used cryoelectron microscopy to show that the Nsp1 C-terminus is the region that tightly binds to and thereby obstructs the mRNA entry channel. By doing so, it effectively shuts down the type I-interferon pathway by inhibiting the translation of IL-8 and IFN-β mRNA transcripts. These transcripts are upregulated when RIG-I, a cytosolic PRR of the innate immune system, initiates signaling upon recognition of a coronavirus. The authors note that Nsp1 may be an important therapeutic target, since eliminating its binding to the 40S subunit may enhance host cell immunity, which could result in the reduction of viral load in the earliest rounds of viral replication.

SARS-CoV-1 and SARS-CoV-2 share a high degree of homology in their genomic sequences. In an effort to identify specific protein products that may differentiate the viruses’ sensitivity to IFN, Lokugamage et al. (2020) identified gene domains that encode protein products related to IFN antagonism activity in SARS-CoV-1. They prioritized regions that differed more substantially in nucleotide sequence in the respective portions of the SARS-CoV-2 genome. Of particular note is SARS-CoV-1 ORF3b, which encodes a 154 amino acid protein that Kopecky-Bromberg et al. (2007) previously found could antagonize IFN response by reducing the phosphorylation and activation of IRF-3 (a transcription factor that can upregulate the transcription of interferon). In SARS-CoV-2, the corresponding ORF3b region contains a premature stop codon, which truncates the resulting protein into a 24-amino acid fragment. Konno et al. (2020) report that the truncated SARS-CoV-2 ORF3b protein is more effective at suppressing the activation of type 1 interferon than the analogous SARS-CoV-1 protein. Lokugamage et al. (2020) also note that SARS-CoV-1 ORF6 encodes a protein that Kopecky-Bromberg et al. (2007) previously showed is a powerful IFN antagonist, through inhibiting the translocation of transcription factors like STAT1 through the nuclear membrane. The SARS-CoV-2 ORF6 only shares 69% of the same nucleotide sequence with SARS-CoV-1, resulting in a truncated protein product, which may also dampen the IFN antagonistic effect in SARS-CoV-2.

In vitro studies have demonstrated that SARS-CoV-2 can infect two T lymphocyte cell lines (MT-2 and A3.01) with very low ACE2 expression, and that these cells were more vulnerable to SARS-CoV-2 infection than they were to SARS-CoV-1 infection (Wang et al., 2020). The results demonstrate that SARS-CoV-2 spike protein can mediate potent infectivity even in cells with low ACE2 expression. This data suggests that the virus can infect cells through mediation by a different receptor, such as the CD-147 receptor. Furthermore, the researchers showed that SARS-CoV-2 was able to infect MT-2 cells through both direct fusion to the cell membrane and through an endocytic pathway. In contrast, however, SARS-CoV-1 showed no evidence of being able to enter the cells through direct fusion to the membrane, which may explain its diminished infectivity. Despite being able to infect T lymphocyte cells, SARS-CoV-2 was not able to efficiently replicate in these cells.

Blanco-Melo et al. (2020) arrive at similar conclusions and propose that SARS-CoV-2 regulates the host immune response in an imbalanced fashion. Firstly, infection with the virus induces low IFN-I and IFN-III levels, which is mediated by a suppressed interferon stimulating gene (ISG) response. This characteristic was confirmed in post-mortem lung samples from COVID-19 patients. On the other hand, the authors show that the virus induces a strong proinflammatory response through the induction of chemokines, such as IL-6(interleukin-6), CCL5, CCL8, and CCL11, which were detected at robust levels in the tissue samples. More specifically, SARS-CoV-2 induces high levels of the chemokines CCL20, CXCL1, IL-1B, IL-6, CXCL3, CXCL5, CXCL6, CXCL2, CXCL16, and the cytokine TNF (tumor necrosis factor). When ferrets were infected with SARS-CoV-2, the authors observed the induction of the chemokine response by Day 3 of infection, the time when viral load peaked. By Day 7, while virus levels were diminishing, cytokine levels continued to grow, including levels of CCL2, CCL8, CXCL9, as well as others. This same pattern was not observed when the ferrets were infected with influenza A virus.

There is a great deal of interest in how SARS-CoV-2 affects immune responses and function in the disease, especially with regard to normal activation and control of potential cytokine storms that result from over-activation. New insight has been gained from studying the cellular and molecular biology of clonogenic cells, including B cell genesis that takes place in the germinal centers of lymph nodes (Mesin et al., 2020) and that contributes to an effective antibody response to particular coronavirus antigens. From studies of postmortem lymph nodes and spleens in acute SARS-CoV-2-infected specimens (Kaneko et al., 2020), the fine line between an effective immune response to the virus versus the generation of a cytokine storm seems at risk in COVID-19. Kaneko et al. (2020) find that germinal centers are absent and exhibit a profound reduction in Bcl-6+ germinal center B cells. This finding offers insight into the dysregulated immune function characteristic of COVID-19, which include both cytokine storms and the potential for limited resilience in antibody responses.

Deep immune profiling in a SARS-CoV-2-infected patient population has revealed three immunotypes related to different patterns of lymphocyte responses (Mathew et al., 2020). These different responses may relate to recently described differences in patient myeloid signatures (Mann et al., 2020) that should aid in the stratification of patients and their immune responses to the coronavirus.

Epidemiology

Natural Reservoirs

Preliminary genetic evidence suggests that SARS-CoV-2 is zoonotic and indigenous to the horseshoe bat Rhinolophus affinis. It is currently suspected that SARS-CoV-2 migrated from horseshoe bats to an intermediate host, the Chinese pangolin (Manis pentadactyla) by blood-born means, and then migrated again, this time to a human host, at the Huanan Seafood Market in Wuhan, Hubei Province, China. In similar wet markets across China, live exotic animals as well as their uncooked flesh, meats, organs, bones, and pelts are legally sold for trade and consumption. Cages of such animals are often stacked vertically, so the lowest cages are subject to overhead biofluids including saliva, vomitus, urine, feces, blood, and other potentially infectious excretions.

Bats (order Chiroptera) are widely reported as a natural reservoir for a wide range of infectious viral illnesses that can also infect humans, including hantaviruses, rabies, henipaviruses, ebolaviruses, and coronaviruses, such as SARS-CoV-1 and SARS-CoV-2. An analysis of 754 mammalian species and 586 virus species, which included every virus then known to infect mammals, revealed that Chiroptera (followed by rodents and primates) harbor a significantly larger share of zoonotic viruses than all other animal orders (Olival et al., 2017). The largest viral diversity represented in bats are for the Flavivirus, Bunyavirus, and Rhabdovirus families, and RNA viruses are far more common than DNA viruses (ibid.). The authors of the analysis report that use of the animal in medical research and mammal sympatry (when two closely related species or populations live in close proximity to one another) were the two best predictors of viral richness, that is, a measure of the average number of viruses found in a reservoir species. However, despite identifying various predictors for viral richness, Chiroptera in particular demonstrated a significantly higher viral richness than could be predicted by mammal sympatry, proximity to human population, geographic range, and body size. These findings suggest that other features not captured by the analysis, such as immunological function, social behavior or other characteristics may be driving the large viral diversity represented in bats.

Bats’ unique capacity for flight, as well as their feeding and social behaviors, makes them exceptionally suited as natural reservoirs for viruses. It has been theorized that the evolution of flight in bats is closely tied to unique selective pressures on the evolution of their immune systems. Firstly, the capacity for flight is energetically cumbersome, which burdens mitochondria that release reactive oxygen species (ROS) in turn. ROS can damage DNA, which can lead to a host of pathologies not observed in bat populations. At least two distantly related species of bats (Pteropus alecto and Myotis davidii) have genes that code for protein products that enhance DNA repair (Zhang, G. et al., 2013). Foley et al. (2018) identified 14 genes in Myotis myotis that enhance DNA repair, and many other studies corroborate these results with similar findings. However, DNA damage is often indicative of viral infection, which in other species would result in an inflammatory response to the virus. In Chiroptera however, an inflammatory viral response is largely diminished. At least two bat species have no trace of PYHIN genes, a characteristic that is unique among mammals, which code for proteins that activate immunomodulators, enhancing inflammation, when they come in contact with foreign nucleic acids, such as viruses. Furthermore, bats have hollow bones without bone marrow and hence cannot produce B lymphocytes, further diminishing their immune response. The combination of enhanced DNA repair and downregulation of proteins associated with an inflammatory response have allowed for viruses to persist in bats for greater periods of time. In order to fight viral replication, bats have also evolved a higher baseline expression of interferons, which inhibit intracellular viral replication as soon as infection occurs, making them less vulnerable to expressing symptoms of viral infection (Zhou, P. et al., 2016). Since bats live in large communities, closely roosting together in large groups, it’s possible that viral transmission is rampant among bat populations, enhancing their ability to act as a natural reservoir for many viruses.

Bats are known reservoirs for coronaviruses that infect humans, including SARS-CoV-1. More recently, BatCoV RaTG13, a bat coronavirus identified in Rhinolophus affinis, has been shown to share 96.2% sequence homology with SARS-CoV-2, although a clear common ancestor to both has not yet been identified (Zhou et al., 2020). However, due to the viral richness of Chiroptera as an order, it is highly likely that a more direct ancestor to SARS-CoV-2 will be identified in a bat species. Furthermore, bats are the natural reservoir for future emerging zoonotic coronaviruses, which warrant further study. Valitutto et al. published results on April 9, 2020, reporting the discovery of six novel coronaviruses identified in bats captured in Myanmar. Bats were swabbed orally and rectally, and guano samples were collected and tested by RT-PCR. The researchers identified three novel alphacoronaviruses and three novel betacoronaviruses. The authors note that land use practices in Myanmar have put human populations in increased contact with local bat populations, which may increase risk of newly emergent zoonotic threats.

Early Human Cases

The first cluster of patients with viral pneumonia of unknown etiology was reported to the WHO China Country Office on December 31, 2019, and the Huanan Seafood Market was closed for immediate disinfection on January 1, 2020. Of the first 41-44 cases, at least 27 were traced to the aforementioned market, 11 were severely ill, and 33 were stable. All of the patients presented some difficulty breathing and several had invasive lesions in both lungs as seen in chest radiographs. According to the South China Morning Post, there were 9 patients aged 39 to 79 years old presenting flu-like symptoms detected even earlier in November 2019. In particular, a 55-year-old resident of Hubei Province, reported on November 17, 2019, may be the earliest known infected individual, but “patient zero” remains unknown.

Serological evidence by Cohen et al. (2020) suggests that the first COVID-19 infection outside of China in Europe may have occurred in France as early as middle to late December, 2019, at least a month before the initial reports of three cases of COVID-19 in France on January 24, 2020 or the first suspected human-to-human transmission in Germany occurring sometime during January 19-22, 2020 between a pair of Chinese and German colleagues. A 43-year old man from Bobigny, a town northeast of Paris, with symptoms consistent with a COVID-19 infection but no recent international travel was admitted to hospital on December 27, 2019, suggesting an infection as possibly as early as December 14, 2019. The patient’s spouse, likely an asymptomatic carrier, worked at a market near Charles de Gaulle International Airport, where travelers often frequent immediately upon arrival.

Ghinai et al. (2020) reports on an early COVID-19 patient, an Illinois resident in her 60s, who had recently traveled to China in mid-January 2020 and the first suspected human-to-human transmission to her husband hospitalized only eight days later from her return. Postmortem studies of patients in Santa Clara, CA who expired at home on February 6, 2020 and February 17, 2020 have confirmed the presence of SARS-CoV-2 and are consistent with infections in the U.S. as early as middle to late January 2020.

The first suspected COVID-19 infection in the United States was reported on January 12, 2020. Despite the ongoing epidemic in China, the WHO did not officially recognize COVID-19 as a pandemic until March 11, 2020. By February 27, 2020, the number of new cases outside of China first exceeded those inside China, thus marking its unofficial designation as a pandemic. This came only one day after COVID-19 first touched South America, with the first reported case appearing in Brazil, which gave COVID-19 worldwide coverage on all continents except Antarctica.

The Latham-Wilson Hypothesis

The Latham-Wilson Hypothesis on the origin of SARS-CoV-2 purports that the first documented animal-to-human and human-to-human transmissions originated in a Chinese coal mine outbreak in 2012 involving a previously unknown viral pneumonia, which was documented in a Chinese Master’s thesis. Latham and Wilson, who translated and studied the thesis, claim that the symptoms described in the work are consistent with those of COVID-19 and, therefore, postulate that COVID-19 may have preceded the Wuhan outbreak in November/December 2019 by several years. Without further evidence, this claim remains unsubstantiated.

Climate Factors of Disease Distribution

Certain regions have been more heavily impacted than others, and local environmental factors, such as temperature and humidity, may explain some of the observed variation. In a longitudinal study done across hundreds of counties in the U.S., researchers observed that lower humidity, specifically water density below 6 g of water vapor per 1 kg of air, was tied to higher influenza mortality even after controlling for temperature (Barrecca and Shimshack, 2012). Seasonal variations in transmission rates of influenza A during the 2009 H1N1 epidemic in the U.S. was also closely correlated with regional variation in absolute humidity (Lipsitch et al., 2011). This is likely because respiratory droplets travel further in drier environments, thereby increasing the chance of viral transmission, while higher humidity may decrease the reach of such droplets.

The regional variation in basic reproduction numbers for COVID-19 across Chinese provinces in the current outbreak appears more complicated, however, as preliminary research has not been able to establish as strong a relationship between humidity and regional transmission rates (Santillana et al., 2020). Regardless, the authors recommend further research into establishing the relationship between humidity and transmission rates of COVID-19. A more recent study, that has not yet been peer-reviewed, has concluded that higher regional average temperature (at temperatures over 1°C) is negatively associated with COVID-19 incidence (Cameron et al., 2020). Moreover, the survival time of SARS-CoV-1 on various surfaces has been shown to decline as temperature increases over 25°C and as relative humidity increases over 50% (Seto et al., 2011). Given that SARS-CoV-1 is a closely related virus transmitted through respiratory droplets, establishing further trends between temperature, humidity, and SARS-CoV-2 stability and transmission should be prioritized.

Undocumented Cases

Using a statistical analysis of reported infections of COVID-19 within China, Li et al. (2020) estimated that 86% of all infections in China were undocumented before the travel restrictions on the Wuhan Tiahne International Airport were implemented by the Chinese government on January 23, 2020. Furthermore, they estimate that while infection rates were likely lower for these undocumented individuals (estimated at 55% the transmission rate of documented cases), undocumented cases were the likely transmission vector in 79% of all documented cases during this early stage of the COVID-19 epidemic (Li R. et al., 2020). These results indicate that asymptomatic individuals with the infection or those individuals experiencing mild symptoms may contribute substantially to the transmission of the virus, further highlighting the importance of ubiquitous testing and self-isolation. Sewage samples taken from a Massachusetts wastewater treatment facility provide evidence that underreporting of true case count is prevalent in the region (Wu, F.Q. et al., 2020). Based on SARS-CoV-2 viral titers detected in sewage samples tested March 18-25, 2020, the authors estimate that roughly 5% of the fecal samples in the facility tested positive, which is substantially higher than the 0.026% estimated case rate for that time in the state. Asymptomatic cases as well as lack of ubiquitous testing may have contributed substantially to this problem. Because untested individuals who recover from the infection will never test positive for the virus using PCR techniques, since they do not test for the virus before the virus has cleared their system, the number of infections may be widely underreported. Therefore, the widespread use of serological assays testing for SARS-CoV-2 antibodies, which COVID-19 positive individuals will produce after the infection or in late stages of the infection, will be fundamental in tracking the spread of the disease.

Bendavid et al. (2020) sought to estimate COVID-19 seroprevalence (the level of pathogen in a population measured through blood serum) in Santa Clara County, CA from April 3-4, 2020. Using targeted Facebook advertising, the team recruited 3,300 subjects from the county and tested each individual with a lateral flow immunoassay. The unadjusted estimate for the proportion of the sample that tested positive for SARS-CoV-2 antibodies was 1.5%. However, the authors themselves report with 95% confidence that the false positive rate could be 0-1.2%, which casts substantial doubt concerning the validity of the sample estimate. Adjusting for a wide variety of factors, their analysis resulted in an estimate of between 48,000 and 81,000 people who had been infected with SARS-CoV-2 in Santa Clara County, CA. This estimate was 50-85 times the official number of reported cases by that time, which was about 1,000 cases. Streeck et al. published a report on April 9, 2020 where 500 residents of Gangelt, a small town in rural Germany, were tested by a combination of RT-PCR and immunoassay testing for seroprevalence. The authors found that 14% of the town had antibodies for the virus and 2% were actively infected with SARS-CoV-2. Based on the results of the study, the researchers estimated a case fatality rate of 0.37%, which was considerably lower than the reported value of 2% for Germany at the time. Because of the increasing number of suspected asymptomatic and undocumented cases, the current case fatality rates estimated by geographic location are likely widely overestimated.

Superspreaders and Seeding Events

SARS-CoV-2 superspreaders are individuals who possess a high degree of viral transmissibility and infect at least ten others. Certain clinical characteristics may make someone more prone to becoming a superspreader, including a higher degree of viral shedding or a longer contagious period. However, certain behaviors, such as attending crowded indoor events, are particularly impactful for increasing exposure risk to the susceptible population. Therefore, “seeding events” has emerged as a term used to define single events where clusters of cases originate, and “super-seeding events” are seeding events with clusters originating from exposure to one or more superspreaders.

Superspreaders contribute to a high degree of variability in the individual-level distribution (i.e. overdispersion) of secondary infections. While the basic reproduction number (R0) has been reported to be in the 1.9-8.9 range, a consistent finding is that 80-90% of infected individuals do not spread the disease, and so superspreaders may be to blame for the vast majority of cases. Using data from the number of reported COVID-19 cases from a WHO report published on February 27, 2020 and an R0 of 2-3, Endo et al. (2020) estimated the overdispersion parameter, k, to be approximately 0.1 (95% credible interval or CrI of 0.5-2.0 for R0 = 2.5). The authors interpret this estimate by stating that potentially 80% of secondary infections were caused by 10% of infected individuals.

Several studies have pointed to increased risk of outbreaks (i.e., large clusters) from seeding events of viral transmission in indoor environments. Qian et al. (2020) identified 318 outbreaks that gave rise to three or more COVID-19 cases tied to transmission from a single individual in China outside of Hubei Province. These 318 outbreaks gave rise to 1,245 confirmed cases in 120 cities between January 4, 2020 and February 11, 2020. Of these events, all 318 outbreaks occurred indoors, with the vast majority occurring at residences (79.9% of cases). At the time, residences became the primary location for quarantine, and severe lockdown restrictions were beginning to be implemented, so the increased incidence of transmission in residences was expected. Only 1.9% of the outbreaks involved 10 or more individuals, and the majority of these occurred in commercial venues including shops and outdoor food venues. The largest outbreak noted occurred in a Tianjin shopping mall and involved 21 cases. In a related study, Nishiura et al. (2020) identified 110 cases that were associated with 11 different clusters or sporadic cases in Japan and used contact tracing to identify transmission events. All traced transmission was tied to closed indoor environments, including fitness centers, hospitals, and shared eating environments. The authors estimate that transmission in a closed environment was 18.7 times more likely than in an open air environment.

Leclerc et al. (2020) performed a meta-analysis of the existing literature and media reports to find settings linked to SARS-CoV-2 transmission clusters or seeding events. Settings were defined as locations that resulted in secondary transmission of the virus.[4]The authors found evidence for clusters of cases linked to 152 events, of which 11 had more than 100 reported cases. These outbreaks were linked to transmission that occurred in hospitals, elderly care facilities, work dormitories, and ships. Four religious venues were also linked to clusters of over 100 cases. Five clusters of 50-100 cases were also identified in schools, sporting events, bars, shopping centers, and conferences. The authors report that worker dormitories had a notably higher rate of secondary transmission, with one worker dormitory in Singapore tied to 797 cases. The most common setting for a cluster of cases were residences, but all had clusters with fewer than ten individuals. Furthermore, the vast majority of clusters occurred in indoor venues.


Table 1.6a: Leading U.S. Outbreak Clusters of over 1,000 Confirmed COVID-19 Cases (September 27, 2020, Adapted from N.Y. Times)
Institution
City, State
Cases
% State Cases
Avenal State Prison
Avenal, Calif.
2,808
0.35%
San Quentin State Prison
San Quentin, Calif.
2,529
0.31%
Marion Correctional Institution
Marion, Ohio
2,443
1.64%
Pickaway Correctional Institution
Scioto Township, Ohio
1,795
1.20%
Columbia Correctional Institution
Lake City, Fla.
1,458
0.21%
North County Jail
Castaic, Calif.
1,402
0.17%
California Institution for Men
Chino, Calif.
1,393
0.17%
Seagoville Federal Prison
Seagoville, Texas
1,392
0.18%
Trousdale Turner Correctional Center
Hartsville, Tenn.
1,385
0.73%
Ouachita River Unit Prison
Malvern, Ark.
1,365
1.71%
Chuckawalla Valley State Prison
Blythe, Calif.
1,327
0.17%
Folsom Prison
Represa, Calif.
1,267
0.16%
South Central Correctional Facility
Clifton, Tenn.
1,229
0.65%
Cook County Jail
Chicago, Ill.
1,185
0.41%
California Rehabilitation Center Prison
Norco, Calif.
1,179
0.15%
Cummins Unit Prison
Grady, Ark.
1,164
1.46%
Bexar County Jail
San Antonio, Texas
1,138
0.15%


Of particular concern for outbreaks in the U.S. are slaughterhouses and meat processing plants. On April 23, it was reported that the South Dakota Smithfield pork processing had 783 employees that had tested positive for SARS-CoV-2, which contributed to more than half the infections reported for the state at the time. When the first 600 cases were reported on April 16, 2020, it became the single largest coronavirus hotspot in the U.S., surpassing the USS Theodore Roosevelt and Cook County Jail in Chicago. Other meat processing plants in the U.S. have been associated with outbreaks, including the Tyson Foods’ largest pork processing plant in Waterloo, Iowa, where 1,031 of 2,800 employees had tested positive for the virus by May 8 (and more since), contributing to over 90% of the cases reported in Black Hawk County. On May 22, 2020, it was reported that a quarter of workers at Tyson Foods’ Wilkesboro, North Carolina poultry facility had tested positive for the virus (570 of 2,200 employees).


Table 1.6b: Leading States by Number of Long-Term Care Facilities and their Reported COVID-19 Cases and Related Deaths (September 27, 2020, Adapted from N.Y. Times)
State
Facilities
Cases
% State
Deaths
% State
PCFR
Texas
1,667
37,892
4.94%
4,187
26.62%
11.05%
Florida
1,601
27,365
3.92%
5,266
37.55%
19.24%
California
1,431
52,768
6.56%
5,416
34.85%
10.26%
Ohio
1,030
18,631
12.50%
2,428
51.15%
13.03%
Pennsylvania
956
26,780
16.77%
5,300
64.80%
19.79%
Minnesota
892
6,993
7.31%
1,400
68.09%
20.02%
Illinois
851
28,189
9.84%
4,515
51.27%
16.02%
Massachusetts
702
24,716
19.09%
5,909
63.04%
23.91%
New Jersey
670
38,401
18.60%
6,873
42.37%
17.90%
Georgia
641
20,954
6.70%
2,389
34.75%
11.40%
Washington
587
6,781
7.71%
1,064
50.67%
15.69%
Wisconsin
577
1,791
1.62%
433
33.99%
24.18%
Indiana
542
10,981
9.52%
1,916
53.73%
17.45%
Arizona
530
7,404
3.41%
1,310
23.30%
17.69%
New York
523
7,068
1.45%
6,651
20.03%
94.10%
Utah
446
2,429
3.54%
191
42.63%
7.86%
Tennessee
412
7,526
3.97%
536
22.79%
7.12%
Virginia
411
10,011
6.93%
1,437
45.82%
14.35%
North Carolina
379
13,708
6.71%
1,569
46.03%
11.45%
South Carolina
369
8,186
5.69%
1,196
36.28%
14.61%


In late April, 2020 the CDC published a report on the prevalence of COVID-19 in meat and poultry processing facilities in 19 states in the U.S. Among 130,000 workers at 115 facilities reporting cases of COVID-19 in the CDC report, which is not exhaustive, 4,913 cases had been confirmed, just around 3.0% of all employees at these facilities (Dyal et al. 2020). The report also notes that the percentage of employees with confirmed cases at any one of these facilities ranged from 0.6% to 18.2%. Any facilities that reported more than 5% of COVID-19 positive staff were exclusively pork and/or beef processing facilities rather than poultry processing plants, which may be related to the fact that SARS-CoV-2 may be transmitted by the oral-fecal route through the vector of pigs. Meat processing plants have served as the setting for multiple COVID-19 outbreaks abroad as well, including notable ones in Canada, Brazil, Germany, Australia, Ireland, Spain, U.K., and France. Canada’s largest outbreak of COVID-19 was tied to the Cargill Meat Processing Plant in High River, Alberta, where 949 of 2,000 employees tested positive for the disease.


Table 1.6c: Leading States by Number of Colleges and Universities and their reported COVID-19 cases. (October 1, 2020, Adapted from N.Y. Times)
State
Reported Cases
% State Cases
Colleges / Universities
% State Institutions
Texas
12,460
1.59%
80
15.81%
Georgia
8,733
2.75%
35
16.67%
Alabama
7,481
4.84%
26
20.16%
Ohio
7,273
4.72%
48
12.44%
South Carolina
6,323
4.27%
26
26.80%
North Carolina
6,087
2.89%
46
24.47%
Florida
5,801
0.82%
43
9.79%
Illinois
5,558
1.88%
48
12.28%
Wisconsin
5,379
4.40%
27
20.45%
Indiana
5,201
4.33%
30
17.14%
Missouri
5,118
3.95%
35
14.46%
Pennsylvania
4,397
2.68%
81
14.89%
Virginia
4,312
2.91%
39
17.57%
Tennessee
4,253
2.17%
64
33.51%
Arizona
4,118
1.88%
7
4.52%


Prison inmates, who represent 2.3 million individuals in the U.S. as of 2020, have also been widely reported as a vulnerable population to infection due to overcrowding, confinement in small spaces, and limited access to healthcare. Indeed, multiple prisons have served as COVID-19 hotspots in the U.S.. The Marion Correctional Institution in Ohio is especially notable when it became the epicenter of the largest cluster of cases reported in the U.S., when on April 18, 2020, it reported that nearly 2,000 inmates and staff had tested positive for COVID-19. Over 70% of the prisoner population had tested positive Other notable clusters include Cook County Jail in Illinois, where 812 COVID-19 cases had been reported by April 22, 2020. The Pickaway Correctional Institution in Ohio became the second largest hotspot in the U.S. when it reported 1,555 cases of COVID-19 among approximately 2,000 inmates. Table 1.6a lists the largest known institutional outbreaks of COVID-19 in the U.S. Table 1.6b lists the number of long-term care facilities by state along with COVID-19 cases and deaths counts and their corresponding provisional case fatality rates. Tables 1.6c and 1.6d list the leading outbreaks among state colleges and universities and the aforementioned long-term facilities, respectively.

Table 1.6d: Leading U.S. Outbreaks at Long-Term Care Facilities with over 250 Confirmed COVID-19 Cases (September 27, 2020, Adapted from N.Y. Times)
Long-Term Care Facility
City, State
Cases
Deaths
PCFR
Brighton Rehabilitation & Wellness Center
Beaver, PA
445
73
16.40%
Bergen New Bridge Medical Center Nursing Home
Paramus, NJ
375
66
17.60%
Fair Acres Geriatric Center
Lima, PA
351
80
22.79%
Charlotte Hall Veterans Home
Charlotte Hall, MD
304
62
20.39%
Gracedale Nursing Home
Nazareth, PA
301
76
25.25%
Paramus Veterans Memorial Home
Paramus, NJ
292
82
28.08%
Conestoga View Nursing and Rehabilitation
Lancaster, PA
286
77
26.92%
New Jersey Veterans Memorial Home at Menlo Park
Edison, NJ
282
66
23.40%
Lincoln Park Care Center Rehabilitation Facility
Lincoln Park, NJ
273
66
24.18%
Spring Creek Rehabilitation & Health Care Center
Harrisburg, PA
262
42
16.03%
FutureCare Lochearn Nursing Home
Baltimore, MD
261
24
9.20%
Hammonton Center for Rehabilitation and Nursing
Hammonton, NJ
256
41
16.02%
Deptford Center for Rehabilitation and Healthcare
Deptford, NJ
254
38
14.96%
City View Multicare Center Nursing Home
Cicero, IL
252
14
5.56%


By May 26, 2020, of the 403 known locations with a documented outbreak of at least 100 confirmed COVID-19 cases, 278 (68.98%) locations involving 33,181 (41.64%) cases were homes, care facilities, and rehabilitation clinics, 98 (24.32%) l institutions, and 27 (6.70%) involving 8,460 (10.62%) cases were factories, plants, and farms. While long term care facilities, in particular, accounted for only 11% of U.S. reported COVID-19 cases, they represent over 35% of U.S. reported COVID-19 deaths. By July 7, 2020, some 14,000 long-term care facilities accounted for over 296,000 reported COVID-19 cases and over 55,000 related COVID-19 deaths, which represented approximately 10% and 42% of the corresponding cases and deaths in the U.S. In particular, as of July 12, 2020, 1,716 locations nationwide (1,312 long-term care facilities) with outbreaks of at least 50 confirmed COVID-19 cases, totaled 221,226 (122,907) COVID-19 infections in the U.S.


Table 1.7: Prevalence of SARS-CoV-2 in 19 U.S. Homeless Shelters in Four Cities (Adapted from Mosites et al., 2020)
City
Shelters
Testing Dates
Residents
Staff
Tests
Cases
Tests
Cases
Shelters Reporting >1 Case
Seattle
3
3/30 - 4/8
179
31 (17%)
35
6 (17%)
Boston
1
4/2 - 4/3
408
147 (36%)
50
15 (30%)
San Francisco
1
4/4 - 4/15
143
95 (66%)
63
10 (16%)
Shelters Reporting 1 Case
Seattle
12
3/27 - 4/15
213
10 (5%)
106
1 (1%)
Shelters Reporting 0 Cases
Atlanta
2
4/8 - 4/9
249
10 (4%)
59
1 (1%)
19
3/27 - 4/15
1,192
293 (25%)
313
33 (11%)


Another potential setting for outbreaks in the U.S. are homeless shelters, due to overcrowding and conditions that make social distancing difficult to implement. The CDC responded to clusters of SARS-CoV-2 reported in homeless shelters in Boston, MA, San Francisco, CA, and Seattle, WA, from March 27, 2020 to April 15, 2020. They also tracked the prevalence of SARS-CoV-2 in 12 other emergency housing facilities in Seattle where one case had been identified and also tracked the prevalence of the disease at two shelters in Atlanta, GA, where no cases had yet been reported. Their results are tracked in Table 1.7.

Mutations and Divergent Strains

Early Lineages

Early studies on the molecular divergence of the virus showed that there were two primary virus types: L-type and S-Type. The S-type virus is the older version, less prevalent in Wuhan, China, and patients with this variant reportedly had less severe symptoms. The S-type was more common in cases outside of Wuhan, China and became increasingly prevalent worldwide. In Wuhan, China, as many as 96% of cases were related to L-type (Tang, X. et al., 2020). These conclusions may be overstated, however. The SARS-CoV-2 L- and S-types differ by only 1 nonsynonymous mutation, but as of March 2, 2020, 111 nonsynonymous mutations have been identified in SARS-CoV-2 since the beginning of the outbreak (MacLean et al., 2020). It is likely that the differences in severity seen between the S- and L-types is due to epidemiology, not the small difference in genetic sequence (ibid.).

By March 28, 2020, there were 8 strains infecting humans worldwide. However, the most divergent of these strains differed by at most 3 nucleotides. The rate of individual nucleotide substitution for SARS-CoV-2 is therefore quite low, as it is for other coronaviruses, estimated at 8 × 10-4 substitutions per site per year, which is 2-4 times slower than that of influenza (Rembault, 2020). For a virus with as many as 30,000 nucleotides, this would correspond to an estimated 24 nucleotide substitutions per year. Since mutations do not necessarily give rise to the translation of different amino acids, because there is redundancy in the possible mRNA codons that code for a specific amino acid, not all mutations will result in a different protein coded by the virus (known as a nonsynonymous mutation). The radial and rectangular phylogenetic trees of SARS-CoV-2 from early December 2019 to June/September 2020 are given in Figures 1.9a and 1.9b, the latter showing the currently estimated mutation rate. A table of several viruses by type, genome size, and mutation rate (as substitutions per nucleotide site per cell infected and substitutions per nucleotide site per year) is given in Table 1.8.

Figure 1.9a.1: Radial Phylogenetic Tree of SARS-CoV-2 by Older Clade (A2a-A7, color-coded) as of mid-June 2020 (Adapted from NextStrain.org)



Figure 1.9a.2: Radial Phylogenetic Tree of SARS-CoV-2 by New Clade (19A-20C, color-coded) as of mid-September 2020 (Adapted from NextStrain.org)



Table 1.8: Several Viruses by Genome Size and Mutation Rates

(September 2020, Adapted from Sanjuan et al., 2010)

Virus
Type
Genome Size [kb]
Rate [s/n/c]
Rate [s/n/y]
Bacteriophage Qβ
ssRNA(+)
4.22
1.1 × 10-3
Human rhinovirus-14 (HRV-14)
ssRNA(+)
7.13
0.1-4.8 × 10-4
SARS-CoV-2
ssRNA(+)
29.8-29.9
8 × 10-4
SARS-CoV-1
ssRNA(+)
0.8-2.38 × 10-3
Poliovirus-1 (PV-1)
ssRNA(+)
7.44
0.22-3.0 ×10-4
Hepatitis C virus (HCV)
ssRNA(+)
9.65
1.2 × 10-4
0.82 × 10-3
Human immunodeficiency virus (HIV)
dsRNA
9.18
1.0-4.9 × 10-5
1.7× 10-3
Influenza A
ssRNA(-)
13.6
0.71-4.5 × 10-5
2.28 × 10-3
Human T-cell leukemia virus-1
dsRNA
8.50
1.6 × 10-5
Tobacco Mosaic Virus (TMV)
ssRNA(+)
6.40
8.7 × 10-6
Influenza B
ssRNA(-)
14.5
1.7 × 10-6
Herpes simplex virus-1 (HSV-1)
dsDNA
152
5.9 × 10-8


By April 8, 2020, Forster et al. had identified three central clusters of lineages of SARS-CoV-2 (A, B, and C), each carrying distinct nonsynonymous mutations, for the purposes of tracking global distribution of the virus. Their analysis of 160 SARS-CoV-2 genomes identified a central node A as most ancestral to other human strains. Two subclusters of A lineages differ by one synonymous mutation (T29095C), which changes a Thymine to a Cytosine at nucleotide site 29095. Both T-allele and C-allele subclusters were found predominantly in viral genomes isolated from Chinese and East Asian patients. However, about half of patients (15 of 33) with Type C subcluster viral genomes were from the U.S. or Australia, and the U.S. patients carrying virus in the T-allele subcluster all had a history of living in Wuhan, China suggesting that this strain is most common to this region. A secondary cluster of lineages, B, differs from A in two mutations, a synonymous T8782C mutation and a nonsynonymous C28144T mutation, which converted a leucine residue into a serine. 74 of 93 type B genomes were isolated from patients in Wuhan, eastern China, or other parts of East Asia. Curiously, the Type B strains outside of East Asia showed a higher degree of mutations, perhaps suggesting that Type B was well adapted to east Asian populations immunologically but may have had to adapt to overcome some resistance in populations outside of this region. Type C differentiates itself from Type B by the nonsynonymous G26144T mutation, which switches a glycine residue for a valine. At the time, the authors report that this was the most common type isolated from patients in Europe, particularly France, Sweden, Italy, and England. It had also been isolated from patients in California and Brazil.


Figure 1.9b: Times Series of the Phylogenetic Tree of SARS-CoV-2 by New Clade (19A-20C, color-coded) as of mid-September 2020 (Adapted from NextStrain.org)


A genetic analysis by Mt. Sinai Hospital has established that most U.S. COVID-19 reported cases in New York originated from European SARS-CoV-2 strains, not Asian ones, presumably by seeding events involving international travel from Europe. By mid-July 2020, the predominant global clade was A2a (teal), which has been further subdivided into a new global clade (19A-20C). Following the analysis of many hundreds of thousands of samples, it is estimated that SARS-CoV-2 (mainly A2a, 20A/B) mutates at a rate of approximately 22.3 nucleotide substitutions per year (Figure 1.9b).

By the time of its publication on April 23, 2020, data from Yao et al. provided direct evidence that SARS-CoV-2 had acquired at least 30 different genetic variations capable of substantially changing its pathogenicity. The researchers analysed the characterization of 11 patient-derived viral isolates from Hangzhou (ibid.). In Hangzhou, there have been 1,264 reported cases, and Yao et al. studied how efficiently the different viral strains could infect and kill Vero-E6 cells (a lineage of cells isolated from kidney epithelial cells extracted from an African green monkey). Results showed that the viral isolates exhibited significant variation in cytopathic effects and viral load. There were up to 270-fold differences when infecting Vero-E6 cells. Moreover, Yao et al. (2020) observed intrapersonal variation and found 6 different mutations in the spike glycoprotein (S protein). Two of these mutations include 2 different SNVs that lead to the same missense mutation (ibid.).

Mutations to the SARS-CoV-2 genome that encode the spike protein are of particular interest, especially in the regions that code for the receptor binding domain, as these mutations have potential to alter the transmissibility and virulence of the virus. From April through early December, 2020, the COVID-19 Genomics UK Consortium has identified at least 4,000 mutations in the spike protein alone. Mutations that affect the epitope of the virus are also crucial for study as they affect antigenic drift, which in turn affects the efficacy of vaccines and protective antibodies against the virus.

D614G Mutation

On May 5, 2020, Korber et al. (2020) identified specific variants of the spike protein that showed signs of positive selection, as indicated by their increasing prevalence at the time. The D614G mutation, arising from a guanine to adenine mutation at site 23,403 of the genome resulting in a change from an aspartic acid (D) to a glycine (G) residue in the spike protein, is of particular note as the authors identify that it is the mutation that resulted in a clade of SARS-CoV-2 genome that arose in Europe and grew in frequency. The mutation is often accompanied with two other mutations, one that is synonymous and another that resulted in a change in one amino acid residue in the RNA-dependent RNA polymerase of the virus, which is crucial for viral replication. The authors note that in regions where the D614 form was present early in the pandemic (such as in Europe, Australia, Canada, and the U.S.), if G614 entered the population, it quickly increased in frequency and in many cases became the predominant form within weeks. This result suggests that the increasing frequency of the mutation originated from a selective advantage bestowed by the mutation rather than from a founder effect.

Korber et al. suggest two possible mechanisms for the enhanced fitness of the mutation, one of which concerns the intermolecular forces between the protomers of the spike protein that may enhance binding to ACE2. It is also possible that the mutation enhances inhibition of neutralizing antibody activity, as the amino acid affected is located within the region that can bind to various SARS-CoV-2 antibodies (i.e. the epitope). This second mechanism, if at work, could give rise to an increasing number of secondary infections. The authors also found that patients with the D614G mutation had higher viral loads, as these patients needed fewer cycles of PCR for viral detection. However, the authors did not find any association between the mutation and an increase in disease severity.

Mink Variants

On November 6, 2020, the World Health Organization reported that 214 human cases of COVID-19 that traced to SARS-CoV-2 variants associated with mink farms had been reported in Denmark. Twelve cases, all of which were reported in September, 2020 in Jutland, Denmark, were associated with a specific variant known as Cluster 5, which was found on five mink farms in the local region. Of these twelves cases, eight individuals had direct links to the mink farming industry.

The cluster 5 variant contains five different mutations, which causes a change to three amino acids and two deletions in the spike protein. Preliminary evidence seems to suggest that the variant is more resistant to neutralizing antibodies, but further studies will be necessary to verify these findings.

On November 13, 2020, Danish researchers had identified a total of 170 variants of SARS-CoV-2 identified across 40 mink farms. 300 COVID-19 cases in Denmark could be traced back to variants found on mink farms. While there is limited evidence to suggest that these variants lead to higher rates of transmission or to more higher likelihood of serious disease, some variants may lead to strains that are more resistant to current COVID-19 vaccine candidates.

VUI-202012/01

VUI-202012/01 (Variant Under Investigation in December 2020), also known as lineage B.1.1.7, is a variant of SARS-CoV-2 that was first identified by the COVID-19 Genomics UK Consortium in October, 2020. The first sample identified with this variant was from an individual in the United Kingdom that was sampled on September 20, 2020 in Kent. In a news briefing from the BMJ published on December 16, 2020, Jacqui Wise elaborates on the variant’s characteristics. It is specifically defined by a set of 17 non-synonymous mutations, one of which is the N501Y mutation, which results in the replacement of the amino acid asparagine (N) with tyrosine (Y) at the 501st amino acid position. According to a report released by the COVID-19 Genomics UK Consortium on December 19, 2020, other mutations that affect the spike protein are a deletion of amino acids 69-70, a deletion of amino acid 144, A570D, the previously described D614G mutation, P681H, T716I, S982A, and D1118H. The authors state that the 69-70 deletion may be tied to enhanced evasion of the human immune response. They also note that the P618H mutation has immediate proximity to the furin cleavage site, which has biological importance for entry into respiratory epithelial cells. They note that both of these mutations have been previously observed but not in combination. Table 1.9 shows the affected genes, specific nucleotide sequences, and amino acids that the authors report gave rise to the B.1.1.7 lineage. Of particular interest is one mutation outside of the spike gene that results in a premature stop-codon, leading to a truncated version of ORF8, causing it to become inactive. There are also six synonymous mutations; five of them are in the ORF1ab gene and one is in the M gene.

Perhaps more troubling, the N501Y mutation occurs at the spike protein’s receptor binding domain and may be responsible for making the virus more infectious. Rambaut et al. (2020) state that the mutation shows increased binding affinity of the virus’s RBD to murine ACE2. The rapid spread of SARS-CoV-2 infection in Great Britain from October through December, 2020 is thought to be partially attributable to the increasing prevalence of the new variant, which as of December 13, 2020, has been identified in 1,108 cases from 60 different local authorities in the U.K., a number believed to be substantially lower than the true number of cases. As of December 15, 2020, Rambaut et al. report that there are 1623 genomes so far sequenced that belong to the B.1.1.7 lineage: 519 were sampled in Greater London, 555 were sampled in Kent, 545 were sampled in other UK regions such as Scotland and Wales, and four were sampled outside of the UL. As of December 20, 2020, a small set of cases have also been identified in Denmark, Belgium, Italy, the Netherlands, and Austria. Furthermore, a distinct strain carrying the N501Y mutation, known as the 501.V2 variant was also identified in South Africa on December 18, 2020. In mid-December, 2020, the New and Emerging Respiratory Virus Threats Advisory Group also raised concerns over the possibility that this variant may be resistant to current vaccine candidates and general antibody resistance, as the full set of mutations may make it antigenically distinct from previous variants. They specifically cited four likely SARS-CoV-2 reinfections that had been identified within a set of 915 new cases of the VUI-202012/01 variant.

Rambaut et al. speculate that the appearance of a large number of mutations in B.1.1.7 suggests that it may have arisen in a immunocompromised patient chronically infected with SARS-CoV-2, possibly one who had received convalescent plasma as part of their therapy. Such treatment has previously been noted to drive genetic diversity (Kemp et al., 2020).

Table 1.9: Mutations that gave rise to B.1.1.7 Lineage
(Adapted from Rambaut et al., 2020)
Gene Nucleotide Amino Acid
ORF1ab
C3267T
T1001I
ORF1ab
C5388A
A1708D
ORF1ab
T6954C
I2230T
ORF1ab
11288-11296 deletion
SGF 3675-3677 deletion
Spike (ORF2)
21765-21770 deletion
HV 69-70 deletion
Spike (ORF2)
21991-21993 deletion
Y144 deletion
Spike (ORF2)
A23063T
N501Y
Spike (ORF2)
C23271A
A570D
Spike (ORF2)
C23604A
P681H
Spike (ORF2)
C23709T
T716I
Spike (ORF2)
T24506G
S982A
Spike (ORF2)
G24914C
D1118H
ORF8
C27972T
Q27stop
ORF8
G28048T
R52I
ORF8
A28111G
Y72C
Nucleocapsid (ORF9)
28280 GAT → CTA
D3L
Nucleocapsid (ORF9)
C28977T
S235F


Other Notable Mutations

Korber et al. (2020) also identified an S943P (Serine (S) to Proline (P)) mutation, which was found only in Belgium. The mutation is notable because it is found in disparate lineages of the phylogeny of the virus, which suggests that it did not originate from a single founder but arose from a recombination event. For this to occur, the authors note that a host must have had simultaneous infection by two distinct viral genomes. The authors also identify nine other notable mutations that affect the spike protein: L5F and L89 (two signal protein mutations), V367F, G476S, and V483A (these three are found in the RBD region), H49Y, Y415H/del, Q239K (these three are located in the N-terminal S1 domain), A831V and D839Y/N/E (both located near the fusion peptide of the S2 domain), and P1263L.

The ORF3b protein product of SARS-CoV-2 is dramatically shorter than its SARS-CoV-1 counterpart due to the presence of four premature stop codons. The SARS-CoV-1 protein product is associated with potent type 1 interferon inhibition (Kopecky-Bromberg et al, 2007), but Konno et al. (2020) report that type 1 inhibition is further enhanced in the truncated SARS-CoV-2 version of the protein. Konno et al. also identify and describe two SARS-CoV-2 sequences isolated from Ecuadorian patients where the ORF3b genomic sequence was extended due to a loss in the first premature stop-codon. The two ORF3b genomic sequences themselves were more than 99.6% identical, and the proteins for which they encoded were exactly identical. IFNβ reporter assays revealed that the elongated variant of the SARS-CoV-2 ORF3b protein demonstrated significantly higher anti-IFN-I activity. Since the inhibition of type 1 interferon is associated with a worsened clinical outcome, the new variants isolated may be associated with more deleterious SARS-CoV-2 strains.

Infections in Non-human Species

While SARS-CoV-2 shares substantial homology with the coronaviruses found in reservoir species, the true intermediate species (e.g. civets in SARS-CoV-1) that may have transferred the virus to humans has not yet been identified. As yet, no virus sampled in non-human species shares enough common homology to be considered a direct phylogenetic ancestor to SARS-CoV-2. Data concerning which animal species SARS-CoV-2 can infect will provide further insight into potential natural reservoirs of the disease, particularly in domesticated animals that live in close contact with humans, such as pets and livestock.

On April 5, 2020, several news outlets began reporting a confirmed SARS-CoV-2 infection (confirmed by RT-PCR) in a four-year-old Malayan tiger residing in the Bronx Zoo in New York City. The tiger was speculated to have become infected by a possibly asymptomatic zoo employee. Five other felines in the zoo were reported to have a dry cough, loss of appetite, and some wheezing, leading to the suspicion of a SARS-CoV-2 infection in another Malayan tiger, two Amur tigers, and three African lions.

Researchers from the Harbin Veterinary Research Institute of the Chinese Academy of Agricultural Sciences sought to identify animal species that could potentially become infected with SARS-CoV-2. Two distinct SARS-CoV-2 viral samples—one isolated from an environmental sample in Huanan Seafood Market in Wuhan, China and the other isolated from a human patient—were used to study the viral dynamics of animal infections in various species. Four ferrets were inoculated with the virus intranasally, two ferrets inoculated per sample source. After four consecutive days of inoculation, the ferrets were sacrificed. SARS-CoV-2 RNA was detected in the nasal turbinate, soft palate, and tonsils of all four tested (Shi, J. et al., 2020). No viral RNA was detected in the lung, heart, spleen, kidney, pancreas, small intestine, or brain samples also extracted from the subjects. These results suggest that the virus can only replicate effectively in the upper respiratory tract of ferrets. Six other ferrets were also infected with the virus samples (three per sample), and these animals were monitored and tested by the researchers to observe symptoms and viral load. Viral RNA was detected in the nasal washes of all ferrets on Days 2, 4, 6, 8, and 10 after infection and also in the rectal swabs of the ferrets, but viral load was considerably lower in the rectal samples. Moreover, infectious virus was only detected in the nasal washes of the animals. One animal in each group of three developed a fever and loss of fever (one on Day 10 and the other on Day 12). These two animals were sacrificed on Day 13; their lungs showed signs of vasculitis (severe inflammation of blood vessels), increased count of type II pneumocytes, neutrophils, and macrophages around the alveoli, and mild peribronchitis. On Day 20, the other ferrets were euthanized; all six had neutralizing antibodies, and those sacrificed on Day 20 had higher antibody counts than the two sacrificed on Day 13.

Shi, J. et al. (2020) conducted similar studies to test for viral susceptibility in domesticated cats, dogs, and livestock. They confirmed that the virus was transmissible by respiratory droplets between domestic cats. Viral RNA was detected in the nasal turbinates, soft palates, tonsils, trachea, and small intestine in at least one of the adult cats later sacrificed. No viral RNA was detected in lung tissue, however. Neutralizing antibodies for SARS-CoV-2 were detected in all infected adult cats tested. Juvenile cats showed a worsened clinical course. Histological samples revealed large lesions in the nose, trachea, and lungs of the infected juvenile cats. Rectal samples taken from beagles infected with the virus tested positive for viral RNA, but no infectious virus was present. Furthermore, the virus was undetectable in any tissue samples taken in the infected dogs. Two of the four dogs tested showed antibodies for the virus, and two did not. Taken together, the evidence suggests that SARS-CoV-2 cannot replicate as efficiently in dogs as it can in cats and ferrets. The researchers were not able to detect viral RNA in samples collected from pigs, chickens, and ducks inoculated with the viral samples, suggesting that these species are not vulnerable to a SARS-CoV-2 infection.

On the contrary, further studies on pigs warrant investigation, as Zhou, P. et al. (2020) were able to successfully demonstrate that SARS-CoV-2 can infect HeLA cells that expressed ACE2 receptors from humans, horseshoe bats, civets, and pigs, but not mice. Moreover, Chen, W. et al. (2005) identified two pigs that had developed SARS-CoV-1 antibodies (a total of 242 domestic animals living in close contact with humans were surveyed in the study), one of which tested positive for the virus by RT-PCR performed on fecal samples. Viral isolates were obtained from this animal in both blood and fecal samples. The study performed by Shi, J. et al., which concluded that pigs were not vulnerable to SARS-CoV-2, only used five pigs, all of which were juveniles under 40 days of age, which may have contributed to a more robust immune response to the virus.

Sit et al. (2020) studied dogs from 15 different households with confirmed human SARS-CoV-2 infection. Of these, two dogs were confirmed to test positive for the virus from nasal and oral swabs collected for testing by RT-PCR. Their respective viral genomes were sequenced and confirmed to be identical to the virus detected in the humans from these homes. These dogs also showed antibody responses in plaque reduction SARS-CoV-2 neutralization assays. The two dogs were a 17-year-old Pomeranian and a 2.5-year-old German Shepherd, and both were clinically asymptomatic during their period of quarantine. The results suggest that in some cases, humans can transmit the virus to dogs, but it remains unclear whether animal-to-human transmissions can occur.

Rhesus macaques infected with SARS-CoV-2 in a laboratory setting have shown symptoms of infection (Bao et al., 2020), although no other non-human primates as of April 19, 2020 have been confirmed with SARS-CoV-2 infection. Nevertheless, all apes, including bonobos, chimpanzees, gorillas, orangutans, and all African and Asian monkeys, may show enhanced susceptibility to SARS-CoV-2 infection because their ACE2 receptors contain the key amino acids that have been identified to interact with the RBD of SARS-CoV-2 (Melin et al., 2020). American monkeys and some tarsiers and lemurs, however, show increased deviation at these key residues. Protein modeling reveals that these deviations limit the ability of the virus to efficiently bind to ACE2 receptors, which likely decreases susceptibility to infection in these species.

A July 2020 study conducted on household pets in northern Italy found SARS-CoV-2 infection rates in both cats and dogs to be comparable to human infection rates (Patterson et al., 2020). The researchers sampled 817 pets from northern Italy during the COVID-19 outbreak in the region. Of these animals, 540 were dogs and 277 were cats, and all tested negative by RT-PCR methods using oropharyngeal and/or rectal samples, indicating no active SARS-CoV-2 infection in these animals. However, for those animals where blood sera were collected, SARS-CoV-2 neutralizing antibodies were detected in 3.35% of dogs and 3.95% of cats, indicative of previous infection. Not all pets that were seropositive came from households with known previous COVID-19 infection, but the animals all came from regions with high rates of human infection. While previous studies have identified cats as being particularly susceptible to SARS-CoV-2, evidence suggesting that dogs may be equally as susceptible has not been as clear. These results, from a SARS-CoV-2 study that sampled the largest number of animals at the time of its publication, strongly indicate that susceptibility in dogs should be reevaluated and further investigated.

Previous Pandemics and Global Epidemics

Several pathogens have become a pestilence upon the human species during recent recorded history, and not all have been viral in origin. The most notable of these is the Bubonic Plague (or Black Death) of 1347-1351, caused by Yersinia pestis, a rod-shaped, coccobacillus bacterium, which was responsible for an estimated 75-200 million deaths worldwide including 30-60% of the population of Europe. The non-human animal reservoir for such devastation was later determined to be a flea species enzootic to rodents, mainly the black rat (Rattus rattus), which then transferred Yersinia pestis to humans by their flea-bites. Nearly a millennium earlier, 541-751, it is believed that the same pathogen was responsible for 25 million deaths in the Plague of Justinian, responsible for the loss of about 50% of the population of Europe at the time. The global populations then were approximately 200 and 475 million, respectively.

More recently, the Flu Pandemic of 1918-1920 (formerly Spanish Flu[5]), caused by the influenza A virus (subtype H1N1), infected an estimated 500 million individuals worldwide and is believed to have caused 50-100 million deaths through three infection waves from the spring of 1918 to the winter of 1919/1920 and a significantly smaller fourth wave in the spring of 2020. The second wave was the most deadly of the four, due in part to the increased virulence of the virus in late 1918, a result of natural mutations through millions of hosts. Premature governmental responses to the first wave through the quick reopening of schools and businesses to counter the consequences of World War I (WWI) are also to blame. The case fatality rate was further exacerbated by weakened supply chains that led to rampant malnutrition in addition to overcrowding of hospitals and clinics, which contributed to poor hygiene and lethal bacterial superinfections. Many survivors of the second wave had been victims of the first and, therefore, had developed a protective immunity to the virus sparing them from further danger. However, the curious age pattern of mortality leads to more questions, many unanswered, with a peak age for mortality occurring at 28 years in the U.S., Canada, and several locations in Europe. Gagnon et al. (2013) have proposed that early life exposure to the Russian Flu Pandemic of 1889-1894 may have resulted in an immunological memory that later contributed to a dysregulated immune response (leading to cytokine storm, for example) to the antigenically novel influenza strains of 1918-1920. In contrast, much younger and much older individuals were spared, likely due to early exposures with more antigenically similar flu viruses. Starko (2007) suggests differently, however, that the high mortality rate may have resulted from the overuse of salicylic acid (e.g., Aspirin) in quantities as large as 8-31 grams per day following recommendations made by the Surgeon General of the U.S. Army and the Journal of the American Medical Association as part of an experimental treatment protocol with an abundant drug that had recently expired from patent protection. Such doses are now known to be responsible for hyperventilation (33%) and also pulmonary edema (3%) in patients receiving them. No matter the actual causes, adjusting for population growth, a similar pandemic would result in 200-425 million deaths today.


Figure 1.10: Time Series of U.K. Deaths due the Influenza Pandemic of 1918-1920 indicating the initial three Waves (Adapted from Wikipedia)


Several other influenza-A-subtype pandemics have occurred in the last few centuries including, for example, most recently, the Flu Pandemic of 2009 (H1N1), the (Hong Kong) Flu Pandemic of 1968-1970 (H3N2), the (Asian) Flu Pandemic of 1957-1958 (H2N2), and the (Russian) Flu Pandemic of 1889-1890 (possibly H2N2, H3N8, or Betacoronavirus HCoV-OC43).

HIV-1 (groups M, N, O, and P) and HIV-2 (groups A, B, C, D, E, F, G, and H), the family of sexually transmitted zoonotic viruses responsible for the current Global AIDS Epidemic (not officially deemed a pandemic by the WHO), have caused approximately 58.3-98.1 million infections worldwide and approximately 23.6-43.8 million related deaths from 1981 to 2018. HIV-1 was identified in 1976 in Zaire but can be traced as far back as 1910 to Kinshasa, Belgian Congo (now, the Democratic Republic of the Congo) and is genetically similar to Simian Immunodeficiency Virus (SIV). The earliest known human case dates to 1959, believed to have been transmitted by consumption of bushmeat. The specific animal reservoirs of HIV-1, the more virulent and highly infective species of the two, and HIV-2, which appears to be isolated to West Africa, 5-30 times less transmittable, and less responsive to treatments than HIV-1, are believed to be the common chimpanzee (Pan troglodytes troglodytes) and the sooty mangabey (Cercocebus atys atys), respectively.

Table 1.10: Significant Epidemics in the Common Era (October 19, 2020)
Epidemic
Dates
Origin
Reach
Waves
Cases [M]
Deaths [M]
Antonine Plague
165-180
Near East
Eurasia, Africa
?
>5
5
Justinian Plague
541-751
Asia
Eurasia, Africa
?
>25
25-50
Black Death
1346-1453
Asia
Eurasia, Africa
?
>75
75-100
First Cocoliztli Epidemic
1545-1548
Europe
Mexico
?
>5
5-15
Second Cocoliztli Epidemic
1576-1577
Europe
Mexico
?
>2
2-2.5
Third Cholera Pandemic
1846-1860
India
Worldwide
>1
>1
1
Third Plague Pandemic
1855-1959
China
Worldwide
>1
>10
10-15
(Russian) Flu Pandemic
1889-1894
Russia
Worldwide
>3
>300
1
(Spanish) Flu Pandemic
1918-1920
Unknown
Worldwide
3
>500
17-100
(Asian) Flu Pandemic
1957-1958
China
Worldwide
2
>500
1-4
AIDS Pandemic
1959-
Africa
Worldwide
>1
>75
>32
(Hong Kong) Flu Pandemic
1968-1970
China
Worldwide
2
>500
>1
Flu Pandemic of 2009
2009-2010
China
Worldwide
1
700-1,400
0.16-0.58
COVID-19 Pandemic
2019-
China
Worldwide
>40
>1.1


Due to the advent of retroviral medications, the mortality rate of HIV/AIDS has decreased by 56% since its height in 2004 and by 40% since 2010. Currently, the epicenter of the Global AIDS Epidemic is in Southern Africa with an estimated 7.1 million infections with approximately 110,000 annual deaths, where the adult prevalence exceeds 27% in Eswatini (Swaziland), 25% in Lesotho, 21.9% in Botswana, and 3.1-18.9% for several smaller surrounding countries, followed by Nigeria with an estimated 3.2 million infections (2.9%) with approximately 160,000 annual deaths, and India with an estimated 2.1 million infections (0.22%) with approximately 67,000 annual deaths. At the time of writing, there is no known vaccine for either HIV-1 or HIV-2.

The suspected origin, geographic reach, and estimated number of deaths of recent pandemics and global epidemics are given in Table 1.9. In nearly all of the aforementioned examples, Africans and North Asians, predominantly involving the economically disadvantaged and otherwise underserved, were most impacted in terms of both infections and fatality compared to all other demographics.

With regard to the present situation, since SARS-CoV-2 is currently mutating as it propagates across the globe, the likelihood of a sustained COVID-19 Pandemic, possibly in several waves, cannot be ruled out at this time. However, while SARS-CoV-2 has twice the genomic length as that of seasonal influenza and mutates at half the nucleotide substitutions per year (24 vs 50 times per year), SARS-CoV-2 mutates about one-quarter of the rate as influenza in terms of substitutions per site per year (See Mutations and Divergent Strains). The seasonal frequency and mutation rate of influenza are precisely why new vaccinations are required on a yearly basis. At present, it is unclear whether a similar or delayed vaccination rate would arise for SARS-CoV-2, assuming a vaccine is found at all.

Viral Inactivation

While it is paramount to understand the underlying structure and primary action of any pathogen in the human host for the development of treatments for the infected and potential vaccines for the healthy, it is equally as valuable to understand vulnerabilities that may contribute to mitigating or evading infection in the first place. In particular, there are at least three ways to prevent a pathogen from effectuating its purpose, that is, by inhibiting its ability to infect cells and/or multiply. For viruses, the most obvious method involves 1.) mechanical removal or physical distancing from the host prior to infection (See Preparations and Recommendations) followed by 2.) inactivation, which involves altering the lipid/protein coat and effectively weakening or disabling it from its normal action, and finally 3.) denaturing, which is an extreme form of inactivation and involves dismantling into constituent parts. Viral inactivation may involve several concurrent processes including, but not limited to, the following:

  1. Chemical inactivation (such as acids/bases and reactive species)
  2. Pasteurization inactivation (such as heat)
  3. Radiation inactivation (such as far-UVC light)
  4. Solvent/detergent/surfactant inactivation (such as soap)

Oxygen, Heat, pH, and Radiation

The composition of air is primarily Nitrogen (78.084%), Oxygen (20.9476%), Argon (0.934%), and Carbon Dioxide (0.0314%), and the remainder is composed of several other gases in trace quantities. Oxygen, while vital to an animal host, is a known corrosive, causing oxidation in several metals, for example, and can inactivate several enveloped viruses given sufficient time, the rate of which depends on the chemical composition of the envelope proteins. Other oxygen reactive species have been used for viral decontamination such as vaporized hydrogen peroxide for personal protective equipment (PPE) currently used in hospitals and clinics.

SARS-CoV-2 shows increasing sensitivity to heat. Chin et al. (2020) demonstrated that when SARS-CoV-2 is in transport medium at 4°C, the virus remains most stable, experiencing only a 0.7 log-unit reduction in infectious viral titer over 14 days (approximately a 5-fold reduction to 20% of the original viral titer). In contrast, at 70°C tested under otherwise identical conditions, the viral sample was completely inactivated in 5 minutes, and showed a 1.47 log-unit reduction (almost a 30-fold reduction) in infectious titer by one minute. When tested at 56°C, the sample was inactivated in 30 minutes (but still present at 10 minutes albeit with a 2.97 log-unit reduction in infectious viral titer), and when tested at 37°C, the sample was inactivated in 2 days (at 1 day, the viral titer had had reduced by 3.58 log-units, roughly a 3800-fold decrease). Finally, at 22°C, the sample was still viable by 7 days (but showed a significant 3.4 log-unit reduction by this point) and completely inactive by 14 days. Figure 1.11 illustrates all of the data reported for the stability of SARS-CoV-2 in transport medium at the five temperatures tested. At room temperature (22°C), Chin et al. (2020) demonstrated that SARS-CoV-2 remained stable for a wide range of pH values (3-10), with similar log-unit reductions reported after incubation for 1 hour at each pH tested.

Far-UVC light (specifically 222 nm light at 2 mJ/cm2) has been shown to efficiently inactivate airborne aerosolized viruses. At this dosage and wavelength, for example, UVC light was found to inactivate over 95% of aerosolized H1N1 influenza virus (Welch et al., 2018). At the same time, the study found that this treatment was relatively safe even when applied to biological surfaces. Radiation with a wavelength of 222 nm did not damage mammalian cells because of the strong absorbance in the outer layers of the skin and eyes, whereas broad spectrum radiation at 254 nm, more commonly used in some sterilization protocols, is associated with potential damage in tissues. The study authors recommend the use of this particular wavelength in overhead lamps in public spaces as a means of limiting the transmission of a wide range of microbial infections of both bacterial and viral origin. [NB: Light of this wavelength should be tested for its potential efficacy in inactivating SARS-CoV-2.] Buananno et al. (2020) report that 1.7 and 1.2 mJ/cm2 dosages of 222 nm light inactivated 99.9% of aerosolized alpha coronavirus 229E and beta coronavirus OC43. The authors go on to suggest that the use of low intensity far-UVC light may have efficacy in inactivating a broad range of coronaviruses, including SARS-CoV-2.


Figure 1.11: Percentage of Original SARS-CoV-2 Titer Remaining over Time at 5 Temperatures (Adapted from Chin et al., 2020)


It follows that SARS-CoV-2 cannot persist outdoors for more than a handful of days, which has yet to be quantified by any studies. In contrast, however, protection from Far-UVC, trapping in cold temperatures, and isolation from oxygen extend its ability to persist possibly even up to years under the right conditions, which is how samples are stored in certain laboratories.

Soaps, Surfactants, and Detergents

Good personal hygiene, such as washing with soap, mild surfactants, and/or detergents, is effective in inactivating several dangerous pathogens or mechanically removing them from the body or contaminated items. These compounds consist of amphipathic (both hydrophilic and hydrophobic) pin-shaped molecules, which is key to their protective action, and rupture the membrane of several types of bacteria and viruses, including coronaviruses like SARS-CoV-2. The charged, polarized, hydrophilic (water-loving) head is attracted to water molecules while the non-polar hydrophobic (water-fearing) tail mixes with oils and fats. When these molecules are suspended in water, they create a solvation shell through hydrogen bonding with water molecules. This water cage has an ice-like crystal structure and can be characterized according to the hydrophobic effect. The structure of SARS-CoV-2 lipid membranes resembles the double-layered cage or micelles. These are studded with important proteins that allow viruses to infect cells. Pathogens wrapped in lipid membranes include several coronaviruses, hepatitis B/C, herpes viruses, ebolaviruses, the Zika virus, and numerous bacteria that attack the intestines and respiratory tract. Soap physically denatures the virion when the water-shunning tails of the molecules wedge themselves into the lipid membrane (Figure 1.12).


Figure 1.12: Soap-Enveloped Virion Inactivation


Electrokinetic Inactivation

The stability of enveloped RNA viruses, such as SARS-CoV-2, can be influenced by electrostatic interactions (Forrey et al., 2009). Because positively charged capsid proteins package the negatively charged RNA, for instance, the overall positive charge of the capsid is known to limit the length of the viral genome (Belyi et al., 2006). Furthermore, the envelope protein of coronaviruses can generate transmembrane voltage-dependent ion conductive pores, a vulnerability which can be targeted through electrokinetic disruption (Verdia-Baguena et al., 2012). Many other electrical properties that affect the structural integrity of the virion make it vulnerable to inactivation through electrical mechanisms.

Sen et al. (2020) tested the ability of an FDA-approved wireless electroceutical wound dressing to disrupt the infectivity of SARS-CoV-2 through electrokinetic destabilization of the virion. Specifically, the authors tested how the fabric could affect the zeta potential of the virion, a parameter that affects the adsorption and stability of the virus in a colloidal dispersion. The fabric tested was made from polyester that had been printed with alternating dots of Zn and Ag metal. Using the metals as a redox couple, the fabric could generate a weak 0.5 V potential difference in the presence of an aqueous ionized environment, such as a bodily fluid. The fabric was tested against a control polyester fabric with no metallic deposition. The authors found that upon contact with the electroceutical fabric, the zeta potential was significantly attenuated, and this effect was augmented with longer contact time. Moreover, upon contact with the electroceutical fabric, the virus lost its ability to infect cells, a protective effect which was not demonstrated by the control fabric. The authors suggest that the reduction in zeta potential may have led to defects in the structural integrity of the virus, which resulted in the loss of infectivity. These results suggest that further research into the potential use of such electroceutical materials in the inactivation of SARS-CoV-2 are warranted.

  1. The case fatality rate (CFR) of an epidemic is the ratio of the total fatalities among the total of confirmed infected cases (by laboratory testing, for example), which is a meaningful measurement of the severity of the epidemic only after fatalities have ceased. A provisional CFR or PCFR during an on-going epidemic should be used with caution and for relative comparison, as it may differ from the CFR by a wide margin. However, while a PCFR introduces an inherent uncertainty into the denominator of the ratio, it may be partially corrected by using the total infection count from a date prior to that of the total fatalities using a time frame equal to the average time to death from initial infection of the corresponding disease, which is approximately 14 days for COVID-19. The CFR may be contrasted with the infection fatality rate (IFR), which is the fraction of fatalities among the infected, not just among those confirmed to be infected, and the mortality rate (MR), which is the fraction of fatalities of a disease among the general population.
  2. Reported death counts of New York City include probable deaths attributed to COVID-19.
  3. Evidence is emerging that the first COVID-19 death in the U.S. may have occurred as early as February 6, 2020.
  4. For instance, for those individuals infected on an international conveyance who would then disembark, any subsequent secondary infections would not be considered as part of the cluster tied to the ship.
  5. The Flu pandemic of 1918-2020, or Spanish Flu, gets its colloquial name, a misnomer, from the fact that in Spain, a neutral country during WWI, print and broadcast media were not censored in their reporting of the ravaging illness for reasons likely concerning wartime morale. The confounding effect of censorship in the U.S., U.K., France, Germany and Austria-Hungary, Italy, and Russia, combined with the public dissemination of reports of the flu in Spain gave the false impression that the origin and epicenter of the pandemic occurred and existed in Spain.

Chronology, Data, and Observations

“Measure what is measurable, and make measurable what is not so.”

―Galileo Galilei, Astronomer (1564-1642)

Observations and calculations from a mathematical, probabilistic, and statistical points-of-view are essential in understanding and modeling the progression of COVID-19 infections, for establishing proper responses, and realizing the actual reach, scope, and range of the corresponding pandemic. These include, but are not limited to, modeling daily and cumulative time series of infections, deaths, and testing rates, computing times to doubling and order-of-magnitude increases for forecasting, detailing the particular sequence of most impacted locales by case fatality rates or other changing variables, studying the efficacy of proposed experimental treatments and potential vaccines, and comparing governmental responses by locales to determine safest and most effective actions. It remains difficult, however, to separate the effect of testing rates on actual infection rates (likely much greater than the reported cases), as both are increasing simultaneously.

Chronology

The varying timelines of the course of COVID-19, mutations of SARS-CoV-2, and other practical considerations of healthcare robustness and infrastructure for reporting confound accurate estimation of infection rates, case fatality rates, and mortality rates[1], too, especially if the virus is virulent. Despite these inherent difficulties, we summarize the generally accepted chronology of events:

  1. On December 29, 2019, four cases of viral pneumonia of unknown etiology were linked to the Huanan Seafood Market.
  2. On December 30-31, 2019, the Chinese Center for Disease Control (CDC) and the WHO were informed of an outbreak of a viral pneumonia in Wuhan, Hubei Province, China.
  3. On January 6, 2020, Chinese CDC activated a Level 2 Emergency Response.
  4. On January 7-10, 2020, Chinese scientists identified the second novel coronavirus responsible for the aforementioned outbreak, called nCoV-2019, and released the genome sequence.
  5. On January, 11, 2020, China reported its first death, a 61-year old male who had visited the Huanan Seafood Market.
  6. On January 21, 2020, China reported the first confirmed human-to-human transmitted infection due nCoV-2019 (now SARS-CoV-2).
  7. On January 23, 2020, the Chinese government imposed a strict Stay-in-Place order for the residents of Wuhan, China.
  8. On January 30, 2020, the WHO declared a Global Health Emergency for the growing number of nCoV-2019-related clusters and outbreaks in several locations in and outside of China.
  9. On February 11, 2020, the WHO defined the new name of the disease, COVID-19.
  10. On February 26, 2020, the first case of human-to-human transmission in the U.S. was documented involving an individual from California who had not traveled recently. Similar cases were reported in the states of Oregon, Washington, and New York.
  11. On February 29, 2020, the first U.S. COVID-19 death was reported, which differs from the first confirmed COVID-19-related death believed to have been on February 6, 2020.
  12. By March 17, 2020, all 50 U.S. states had reported COVID-19 cases. On the same day, the state of California declared a Shelter-in-Place order. The daily U.S. reported cases of COVID-19 infections continued to follow those of Italy and France closely but were delayed by 7 to 14 days. By April 1, 2020, the U.S. exceeded the continent of Europe in terms of cases.
  13. On March 18, 2020, China reported no new COVID-19 infections.
  14. On March 19, 2020, Italy surpassed China in COVID-19-related deaths.
  15. On March 20, 2020, New York City became the epicenter of the COVID-19 pandemic with 50% of the U.S. reported cases.
  16. On March 22, 2020, the number of reported deaths in the U.S. exceeded 100.
  17. On March 26, the U.S. became the global epicenter of the COVID-19 Pandemic surpassing China with reported COVID-19 cases.
  18. By March 27, 2020, the number of reported cases in the U.S. exceeded 100,000, which quadrupled to 400,000 by April 8, 2020.
  19. By April 2, the number of reported COVID-19 cases worldwide exceeded 1 million.
  20. By April 8, 2020, there were 1,518,518 reported cases worldwide with 88,495 fatalities (5.8% PCFR). The U.S. led all other countries with 434,927 reported cases (29% of global total) and third to only Italy and Spain with 14,788 reported deaths (17% of global total).
  21. By April 15, 2020, nearly 9,300 U.S. healthcare workers had contracted COVID-19, with 55% believed to have been exposed in the workplace, and with 27 deaths.
  22. By April 23, 2020, there were 50,234 reported deaths from COVID-19 in the U.S.
  23. By April 27, 2020, the number of reported cases in the U.S. exceeded 1 million and accounted for approximately ⅓ of the corresponding global count.
  24. By May 8, 2020, the number of COVID-19 reported cases exceeded 4 million worldwide with 265,852 fatalities (~7% PCFR). The U.S. led all other countries with over 1.2 million reported cases (33% of global total) and 74,810 related deaths (28% of global total).
  25. By May 14, 2020 and June 7, 2020, the number of global COVID-19 reported deaths exceeded 300,000 and 400,000, respectively.
  26. By May 21, 2020, the number of global COVID-19 reported cases exceeded 5 million. Milestones for global and U.S. total cases and deaths are tabulated below. By May 27, 2020, the number of reported COVID-19 deaths in the U.S. exceeded 100,000. Milestones of global and U.S. death counts are tabulated below. On July 24, 2020, California surpassed New York in terms of total cases.

Current Data, Trends, and Models

Several variables account for the worldwide growth characteristics of COVID-19 infections and deaths including the basic reproduction number, number and frequency of seeding events, and virulence of SARS-CoV-2 among other fundamental features. Moreover, the sequence of locations (e.g., continent, country, state/province, county/parish/zone, ZIP code/neighborhood) and demographics (e.g., ethnicity, race, sex, age group, BMI group, etc.) that are most impacted by COVID-19 may depend on population size, population density, in-bound and out-bound travel and tourism frequency, timing of governmental responses including public advisories and orders, the frequency of individuals to gather into large groups, the demographic of employment in certain sectors, the latter two of which may be roughly measured by entertainment revenue, and finally the propensity for citizens to heed public advisories and orders, among other known and unknown variables.

General Definitions

A cluster is a statistically significant set of illnesses coupled spatially and temporally (i.e., several cases in close proximity in a short period of time) with a suspected common origin. If the number of such cases is sufficiently large, which is often at the discretion of a local government or the WHO, then a cluster is classified as an outbreak. An epidemic is a specific outbreak caused by an infectious disease that may cover a city, state, or country, or continent, but is otherwise confined. Not all outbreaks are caused by infectious disease (e.g., influenza, COVID-19, etc.). A pandemic is a wide-scale epidemic or cluster of epidemics that may cover several countries or continents and is sustained by seeding events, which are opportunities for several new infections to occur.

The WHO categorizes pandemics into six phases depending on zoonotic origin, acceleration and type of infection spread, locales involved, and virulence of the underlying illness:

  1. Phase 1 involves the identification of a zoonotic pathogen with a high likelihood to transfer to humans but otherwise remains in the (animal) reservoir(s);
  2. Phase 2 involves the identification of a case or sporadic cases of confirmed animal-to-human transmission of a previously identified zoonotic pathogen and corresponding reservoir(s);
  3. Phase 3 involves sporadic cases or limited clusters of animal-to-human transmission and limited human-to-human transmission (e.g., patient infecting family, caregivers, and/or first responders) not sufficient to sustain community-level spread;
  4. Phase 4 involves several clusters or outbreaks of confirmed human-to-human transmission able to sustain community-level spread; Phase 4 indicates a significant increase in spread risk and consultation with the WHO is merited;
  5. Phase 5 involves human-to-human transmission through community-level outbreaks in at least two countries of increasing size and severity. Designation of WHO Phase 5 indicates a global pandemic is imminent and preparation is necessary; and,
  6. Phase 6 is the highest level of the WHO pandemic phases in terms of scale, reach, and severity.

The CDC categories pandemics into five categories depending on a severity index (PSI) as measured by the case fatality rate (CFR):

  1. Category 1 involves an epidemic with a CFR less than 0.1%;
  2. Category 2 involves an epidemic with a CFR of 0.1%-0.5%;
  3. Category 3 involves an epidemic with a CFR of 0.5%-1.0%;
  4. Category 4 involves an epidemic with a CFR of 1.0%-2.0%; and,
  5. Category 5 involves an epidemic with a CFR greater than 2.0%.

Epidemics and pandemics may be further subdivided into waves and episodes. A wave is defined by the rapid growth and decline of daily infection activity to near nominal levels (or when WHO issues an “At-Ease” Order). A wave may have several peaks and plateaus. In similar fashion, an episode is defined by the rapid (usually exponential) growth followed by slower (usually sub-exponential) growth or even stabilization of cumulative infection activity. Epidemics and pandemics often involve several waves each with several episodes in the cycles of acceleration of infection activity followed by saturation of infection activity, until a terminal plateau or asymptote of cumulative cases (or nominal daily cases) is achieved. A slowly-growing plateau may be intermittent, however, and an asymptote does not have to follow. If the infection activity is sustained, even if nominal, then the epidemic or pandemic is said to be endemic. The common cold and the seasonal flu are examples of endemic illnesses in the human species.

Global Trend

Since early March 2020, the COVID-19 Pandemic is designated WHO Phase 6, CDC PSI Category 5, the maximum of combined indications, and has not yet reached its first global peak, but has sustained at least two episodes within the first global wave. Figures 2.1a and 2.1b illustrate the global daily counts of COVID-19 reported cases and related deaths from December 31, 2019 to date. The early spike in cases on February 12, 2020 in Figure 2.1a resulted from the change in diagnosis and reporting of COVID-19 cases in Wuhan, China. The origin of the apparent undulations in the daily time series data (Figures 2.1a/b) is unclear but may be consistent with periodicity of testing and/or reporting or a dynamical feature of the underlying coupled system of differential equations similar to the Kermack-McKendrick Model. In particular, in Figure 2.1a/b, one may clearly see a small local wave corresponding to a closed epidemiological system of infections in Wuhan, China, followed by a second larger wave, the first global wave, consisting of at least two episode

Figure 2.1a: Daily Global COVID-19 Reported Cases with a 7-day Moving Average (December 31, 2020 - October 19, 2020)


Figure 2.1b: Relative Change in Global Daily Reported COVID-19 Cases (August 15, 2020 - October 18, 2020)


Table 2.0a: Significant Global Case Milestones in the COVID-19 Pandemic (October 19, 2020)
Milestone
Case Difference
Time Frame
Day Count [d]
Growth Rate [/d]
~0 to 1 million
1,015,843
12/1/2019 - 4/1/2020
122
8,327
1 to 2 million
1,000,503
4/1/2020 - 4/14/2020
13
76,962
2 to 3 million
1,017,825
4/14/2020 - 4/27/2020
13
78,294
3 to 4 million
1,015,118
4/27/2020 - 5/9/2020
12
84,593
4 to 5 million
984,156
5/9/2020 - 5/20/2020
11
89,469
5 to 6 million
1,073,713
5/20/2020 - 5/30/2020
10
107,371
6 to 7 million
956,997
5/30/2020 - 6/7/2020
8
119,625
7 to 8 million
1,035,385
6/7/2020 - 6/15/2020
8
129,423
8 to 9 million
903,299
6/15/2020 - 6/21/2020
6
150,550
9 to 10 million
1,034,110
6/21/2020 - 6/27/2020
6
172,352
10 to 11 million
1,127,154
6/27/2020 - 7/3/2020
6
187,859
11 to 12 million
972,157
7/3/2020 - 7/8/2020
5
194,431
12 to 13 million
880,105
7/8/2020 - 7/12/2020
4
220,026
13 to 14 million
1,146,254
7/12/2020 - 7/17/2020
5
229,251
14 to 15 million
897,172
7/17/2020 - 7/21/2020
4
224,293
15 to 16 million
1,109,840
7/21/2020 - 7/25/2020
4
277,460
16 to 17 million
984,657
7/25/2020 - 7/29/2020
4
246,164
17 to 18 million
1,063,369
7/29/2020 - 8/2/2020
4
265,842
18 to 19 million
1,013,547
8/2/2020 - 8/6/2020
4
253,387
19 to 20 million
780,143
8/6/2020 - 8/9/2020
3
260,048
20 to 21 million
1,064,949
8/9/2020 - 8/13/2020
4
266,237
21 to 22 million
976,573
8/13/2020 - 8/17/2020
4
244,143
22 to 23 million
1,073,001
8/17/2020 - 8/21/2020
4
268,250
23 to 24 million
955,160
8/21/2020 - 8/25/2020
4
238,790
24 to 25 million
1,112,749
8/25/2020 - 8/29/2020
4
278,187
25 to 26 million
1,022,066
8/29/2020 - 9/2/2020
4
255,517
26 to 27 million
867,531
9/2/2020 - 9/5/2020
3
289,177
27 to 28 million
958,407
9/5/2020 - 9/9/2020
4
239,602
28 to 29 million
1,205,144
9/9/2020 - 9/13/2020
4
301,286
29 to 30 million
823,132
9/13/2020 - 9/16/2020
3
274,377
30 to 31 million
1,175,253
9/16/2020 - 9/19/2020
3
329,510
31 to 32 million
988,530
9/19/2020 - 9/23/2020
4
266,913
32 to 33 million
1,067,652
9/23/2020 - 9/26/2020
3
342,383
33 to 34 million
1,027,150
9/26/2020 - 9/30/2020
4
276,869
34 to 35 million
1,107,475
9/30/20202 - 10/3/2020
3
319,601
35 to 36 million
958,803
10/3/2020 - 10/6/2020
3
280,005
36 to 37 million
840,014
10/6/2020 - 10/9/2020
3
355,851
37 to 38 million
1,067,552
10/9/2020 - 10/12/2020
3
310,767
38 to 39 million
932,300
10/12/2020 - 10/15/2020
3
364,642
39 to 40 million
1,093,925
10/15/2020 - 10/18/2020
3
370,252
40,264,218
12/1/2019 - Present
322
124,888



Figure 2.1c: Global Average Daily Rate of Reported COVID-19 Cases (October 19, 2020)


Figure 2.1d: Daily Global COVID-19 Reported Deaths with a 7-Day Moving Average (January 22, 2020 - October 19, 2020)


Significant global case and death milestones are given in Tables 2.0a/b.

Figure 2.1e: Cumulative Global COVID-19 Reported Cases, Recoveries, and Deaths (January 22, 2020 - October 19, 2020)


Table 2.0b: Significant Global Death Milestones in the COVID-19 Pandemic (October 19, 2020)
Milestone
Case Difference
Time Frame
Day Count [d]
Growth Rate [/d]
~0 to 50,000
50,128
1/11/2019 - 4/1/2020
81
619
50,000 to 100,000
52,196
4/1/2020 - 4/9/2020
8
6,525
100,000 to 150,000
47,837
4/9/2020 - 4/16/2020
7
6,834
150,000 to 200,000
53,182
4/16/2020 - 4/24/2020
8
6,648
200,000 to 250,000
49,519
4/24/2020 - 5/3/2020
9
5,502
250,000 to 300,000
51,739
5/3/2020 - 5/13/2020
10
5,174
300,000 to 350,000
45,922
5/13/2020 - 5/23/2020
10
4,592
350,000 to 400,000
51,785
5/23/2020 - 6/4/2020
12
4,315
400,000 to 450,000
54,108
6/4/2020 - 6/16/2020
12
4,509
450,000 to 500,000
43,589
6/16/2020 - 6/25/2020
9
4,843
500,000 to 550,000
54,645
6/25/2020 - 7/7/2020
12
4,554
550,000 to 600,000
46,794
7/7/2020 - 7/16/2020
9
5,199
600,000 to 650,000
50,188
7/16/2020 - 7/26/2020
10
5,019
650,000 to 700,000
48,626
7/26/2020 - 8/3/2020
8
6,078
700,000 to 750,000
54,402
8/3/2020 - 8/12/2020
9
6,045
750,000 to 800,000
46,455
8/12/2020 - 8/20/2020
8
5,807
800,000 to 850,000
49,957
8/20/2020 - 8/30/2020
9
5,551
850,000 to 900,000
50,131
8/30/2020 - 9/8/2020
10
5,013
900,000 to 950,000
49,070
9/8/2020 - 9/17/2020
9
5,452
950,000 to 1,000,000
1,000,000 to 1,050,000
975,457
1/11/2019 - Present
255
3,807



Figure 2.1f: Global Average Daily Rate of Reported COVID-19 Deaths (October 19, 2020)


Figure 2.1g: Time Series of Global Provisional Case Fatality Rates (January 22, 2020 - October 19, 2020)


Figure 2.1g illustrates the provisional case fatality rate (PCFR) computed by the fraction of cumulative deaths to cumulative cases, while the adjusted PCFR attempts to correct for the fact that the pandemic is currently on-going. The true case fatality rate falls within the interval defined by the two curves. However, by extrapolating linear regression lines from 14-day fits of both PCFR curves, we compute a convergence to a projected CFR of approximately 3.67%. Incorporating the CDC factor of 6-24 (15 avg.) for COVID-19 infections and a 50% increase for COVID-19-related deaths, the average conclusion of several studies, we provisionally conjecture a final CFR of approximately 0.367%.

Figure 2.1h: Weekday Distribution of COVID-19 Reported Deaths (January 22, 2020 - October 19, 2020)


Figure 2.1h illustrates the non-uniformity of reported deaths across the days of the week, which results from a stable 7-day periodicity observed in the corresponding time-series data.

Figure 2.1i: Daily Global Reported COVID-19 Recoveries with a 7-Day Moving Average (January 22, 2020 - October 19, 2020)


Figure 2.1j: Daily Global Reported Severe COVID-19 Cases with a 7-Day Moving Average (January 22, 2020 - October 19, 2020)


Figure 2.1k: Current Global COVID-19 Reported Closed and Active Cases including Mild / Moderate and Severe / Critical Cases (January 22, 2020 - October 19, 2020)


Universal Features

Figures 2.2a-d illustrate the daily and cumulative counts of reported cases and related deaths for Earth, U.S., New York State, and New York City. Assuming a single wave of the pandemic, the cumulative data of reported cases and related deaths by world, continent, country, state/province, and county/parish/zone, collectively locale, initially grow exponentially and level-off to an approximately horizontal asymptote during each phase according to a Generalized Logistic Distribution. Daily reported cases and deaths initially grow exponentially, reach a maximum, then wane slowly (e.g., with heavy tail) according to a Fréchet Distribution.

As of May 18, 2020, a simple Generalized Logistic Distribution modeling of the time series of the global cumulative case count (above 50k cases) yields an estimate of an asymptotic total of 256 million infections (adjusted R2 > 0.9997), that is, 3.30% of the current global population (i.e., infection rate) as a result of the COVID-19 Pandemic. With a provisional case fatality rate of 6.92%, this projection leads to an estimate of 17.4 million deaths worldwide (0.21% mortality rate). This model assumes no vaccine or effective treatment and only one single wave, the current one, with a single episode, but can model and incorporate multiple episodes and waves independently. However, a second episode emerged globally in June 2020 and a third in October 2020, so these estimates must be interpreted as lower bounds.

Figure 2.2a: Fréchet Distribution Modeling of Daily COVID-19 Reported Cases for Earth, U.S., New York State, and New York City (May 22, 2020)


Figure 2.2b: Generalized Logistic Distribution Modeling of Cumulative COVID-19 Reported Cases for Earth, U.S., New York State, and New York City (May 21, 2020)


Figure 2.2c: Fréchet Distribution Modeling of Daily COVID-19 Reported Deaths for Earth, U.S., New York State, and New York City (May 22, 2020)


Figure 2.2d: Generalized Logistic Distribution Modeling of Cumulative COVID-19 Reported Deaths for Earth, U.S., New York State, and New York City (May 21, 2020)


As of 2020, the population of the Earth is approximately 7.78 billion, growing at a yearly rate of 1.05-1.10% or at least 81 million per year, with an urban population of 56.2%, a median age of 30.9 years, and a land population density of about 59.9 km-2 excluding Antarctica and all bodies of water. The U.S. population is approximately 331 million, while that of New York (NY) and New York City (NYC) are 19.4 million and 8.4 million, respectively. Similar estimates have been computed using a General Logistic Model for the U.S., NY, and NYC (adjusted R2 > 0.9995), and are given in Tables 2.1a and 2.1b. In particular, this model determines that 22.8% of the current U.S. population have become infected or will become infected with COVID-19 (i.e., infection rate) and 1.27-2.14% have succumbed or will succumb to the disease (i.e., mortality rate) corresponding to a case fatality rate of 5.57%.

Table 2.1a: Current/Projected COVID-19 Reported Cases in Four Sample Locations (2020)
Location
Cases
[M]
RC 10/1/20
[M]
RC 11/1/20
[M]
RC 12/1/20
[M]
RC 1/1/21
[M]
NYC
TBA
New York
U.S.
Earth



Table 2.1b: Current/Projected COVID-19 Reported Deaths in Four Sample Locations (2020)
Location
Current RD [M]
RD 10/1/20
[M]
RD 11/1/20
[M]
RD 12/1/20
[M]
RD 1/1/21
[M]
NYC
TBA
New York
U.S.
Earth


International Trends

To illustrate the rapid spread of COVID-19, global density maps of reported COVID-19 cases on January 14, January 29, February 28, March 29, April 28, and May 29, 2020 are given in Figures 2.3a-f. Leading countries in terms of COVID-19 reported cases (over 50k) and related deaths are given in Table 2.3b. Included in the same table are the corresponding populations and fraction of total global population, population densities by square mile, provisional case infection rates (PIR), provisional mortality rate (expressed as deaths per million population), and provisional case fatality rates. Of particular note, though no longer on the list, is the country of Ecuador, which added 11,536 reported cases in one day on April 24, 2020, constituting 10.9% of the new cases worldwide (105,825) that day.

Table 2.2: Descriptive Statistics of the COVID-19 Pandemic for 214 Affected Countries (October 20, 2020)
Cases
Active
Serious
Deaths
Tests
Top 10
28,334,552
6,399,681
49,237
762,470
337,136,526
Affected \ Top 10
12,300,023
3,251,426
23,537
360,273
407,811,564
Affected
40,634,575
9,651,107
72,774
1,122,743
744,948,090
Max
8,456,653
2,728,163
15,501
225,222
160,000,000
Mean
190,773
45,310
539
5,878
3,497,979
Median
10,691
1,442
32
240
221,782
Confidence (95%)
116,376
27,564
240
3,077
2,142,694



Figure 2.3a: Global Density Map of COVID-19 Reported Cases (January 14, 2020)


Figure 2.3b: Global Density Map of COVID-19 Reported Cases (January 29, 2020)


Figure 2.3c: Global Density Map of COVID-19 Reported Cases (February 28, 2020)


Figure 2.3d: Global Density Map of COVID-19 Reported Cases (March 29, 2020)


Figure 2.3e: Global Density Map of COVID-19 Reported Cases (April 28, 2020)


Figure 2.3f: Global Density Map of COVID-19 Reported Cases (May 29, 2020)


Figure 2.4a: Population Density of all Countries with Populations over 45M (2020)

<br Countries with at least 45M citizens and ordered by population density (per square mile) are given in Figure 2.4a, with Bangladesh, South Korea, India, Philippines, and Japan possessing the highest population densities in 2020.

Figure 2.4b: Median Age of all Countries with Populations over 45M (2020)

Countries with at least 45M citizens and ordered by median age are given in Figure 2.4b, with Japan (48), Italy (47), Germany (46), Spain (45), and South Korea (44) possessing the highest median ages in 2020.

Table 2.3a: Population, Median Age, and Urban Fraction of Leading Countries by COVID-19 Reported Cases (October 20, 2020)
Location
Population
Global Frac.
Density [km-2]
Median Age
Urban Frac.
USA
331,589,837
4.26%
36
38
83%
India
1,384,085,979
17.79%
466
28
35%
Brazil
213,015,720
2.74%
25
33
88%
Russia
145,953,533
1.88%
9
40
74%
Spain
46,760,297
0.60%
94
45
80%
Argentina
45,320,654
0.58%
17
32
93%
Colombia
51,045,852
0.66%
46
31
80%
France
65,317,424
0.84%
119
42
82%
Peru
33,109,207
0.43%
26
31
79%
Mexico
129,339,873
1.66%
67
29
84%
UK
67,993,982
0.87%
281
40
83%
South Africa
59,532,742
0.77%
49
28
67%
Iran
84,314,980
1.08%
52
32
76%
Chile
19,165,653
0.25%
26
35
85%
Iraq
40,488,958
0.52%
93
21
73%
Italy
60,434,663
0.78%
205
47
69%
Bangladesh
165,182,885
2.12%
1,269
28
39%
Germany
83,865,285
1.08%
241
46
76%
Indonesia
274,392,799
3.53%
151
30
56%
Philippines
110,017,637
1.41%
369
26
47%
Top 10
2,445,538,376
31.43%
54.38
31.4
55.8%
Earth \ Top 10
5,335,414,811
68.57%
63.16
31.1
55.3%
Earth
7,780,953,187
-
60.11
31.2
55.5%



Table 2.3b: Leading Countries by COVID-19 Reported Cases and Related Deaths with Provisional Infection (per thousand), Mortality (per million), and Case Fatality Rates (October 20, 2020)
Location
Cases
Deaths
PIR [/k]
PID [/km2]
PMR [/M]
PCFR
USA
8,456,653
225,222
25.503
0.924
679.22
2.66%
India
7,594,736
115,236
5.487
2.554
83.26
1.52%
Brazil
5,251,127
154,226
24.651
0.628
724.01
2.94%
Russia
1,415,316
24,366
9.697
0.086
166.94
1.72%
Spain
1,015,795
33,992
21.723
2.036
726.94
3.35%
Argentina
1,002,662
26,716
22.124
0.366
589.49
2.66%
Colombia
965,883
29,102
18.922
0.871
570.11
3.01%
France
910,277
33,623
13.936
1.662
514.76
3.69%
Peru
870,876
33,820
26.303
0.680
1,021.47
3.88%
Mexico
851,227
86,167
6.581
0.438
666.21
10.12%
UK
741,212
43,726
10.901
3.064
643.09
5.90%
South Africa
705,254
18,492
11.846
0.581
310.62
2.62%
Iran
534,631
30,712
6.341
0.328
364.25
5.74%
Chile
493,305
13,676
25.739
0.663
713.57
2.77%
Iraq
430,678
10,317
10.637
0.992
254.81
2.40%
Italy
423,578
36,616
7.009
1.440
605.88
8.64%
Bangladesh
390,206
5,681
2.362
2.998
34.39
1.46%
Germany
373,731
9,899
4.456
1.072
118.03
2.65%
Indonesia
365,240
12,617
1.331
0.202
45.98
3.45%
Philippines
359,169
6,675
3.265
1.205
60.67
1.86%
Top 10
28,334,552
762,470
11.586
0.630
311.78
2.69%
Earth \ Top 10
12,300,023
360,273
2.305
0.146
67.52
2.93%
Earth
40,634,575
1,122,743
5.222
0.314
144.29
2.76%



Figure 2.4c: All Affected Countries with at least 80,000 COVID-19 Reported Cases ordered by Provisional Infection Rate [per 1,000 people] (September 16, 2020)


All countries with at least 80,000 COVID-19 reported cases ordered by provisional infection rate, the fraction of infections per thousand population are given in Figure 2.4c.

Figure 2.4d: All Countries with at least 80,000 COVID-19 Reported Cases ordered by Provisional Infection Density [km</nowiki>-2] (September 16, 2020)


All countries with at least 80,000 COVID-19 reported cases ordered by provisional infection density, the fraction of infections per square kilometer are given in Figure 2.4d.

Figure 2.4e: All Countries with at least 80,000 COVID-19 Reported Cases ordered by Provisional Mortality Rate (per million population) (September 16, 2020)


All countries with at least 80,000 COVID-19 reported cases ordered by provisional mortality rate, the fraction of deaths per million population are given in Figure 2.4e.

Figure 2.4f: All Countries with at least 80,000 COVID-19 Reported Cases ordered by Case Fatality Rate (September 16, 2020)


All countries with at least 80,000 COVID-19 reported cases ordered by provisional case fatality rate, the fraction of deaths per infected are given in Figure 2.4f.

Table 2.3c: Leading Countries by COVID-19 Reported Case Fractions (October 20, 2020)
Location
RC / Total
Active / Total
Serious / Total
Rec / Total
RD / Total
USA
20.81%
35.69%
21.30%
18.43%
20.06%
India
18.69%
9.80%
12.29%
22.54%
10.26%
Brazil
12.92%
5.43%
11.43%
15.68%
13.74%
Russia
3.48%
4.12%
3.16%
3.60%
2.17%
Spain
2.50%
0.00%
2.55%
0.00%
3.03%
Argentina
2.47%
2.25%
6.04%
2.69%
2.38%
Colombia
2.38%
0.90%
2.91%
2.91%
2.59%
France
2.24%
10.08%
2.88%
0.35%
2.99%
Peru
2.14%
0.69%
1.50%
2.63%
3.01%
Mexico
2.09%
1.91%
3.60%
2.07%
7.67%
UK
1.82%
0.00%
0.81%
0.00%
3.89%
South Africa
1.74%
0.67%
0.75%
2.13%
1.65%
Iran
1.32%
0.95%
6.56%
1.44%
2.74%
Chile
1.21%
0.19%
1.06%
1.56%
1.22%
Iraq
1.06%
0.74%
0.62%
1.22%
0.92%
Italy
1.04%
1.75%
1.10%
0.85%
3.26%
Bangladesh
0.96%
1.03%
N/A
1.02%
0.51%
Germany
0.92%
0.90%
1.17%
0.99%
0.88%
Indonesia
0.90%
0.83%
N/A
0.97%
1.12%
Philippines
0.88%
0.55%
2.15%
1.04%
0.59%
Top 10
69.73%
70.87%
67.66%
70.90%
67.91%
Earth \ Top 10
30.27%
29.13%
32.34%
29.10%
32.09%



Figure 2.4g: Countries ordered by Global Fraction of Reported COVID-19 Cases (September 16, 2020)


Figure 2.4h: Countries ordered by Global Fraction of Reported COVID-19 Deaths (September 16, 2020)


Table 2.3d: Leading Countries by COVID-19 Severity Fractions (October 20, 2020)
Location
Active / RC
Serious / Active
Serious / RC
Rec / RC
Rec Decile
USA
32.26%
0.57%
0.18%
65.08%
7
India
9.86%
1.19%
0.12%
88.62%
9
Brazil
7.91%
2.00%
0.16%
89.16%
9
Russia
22.26%
0.73%
0.16%
76.02%
8
Spain
N/A
N/A
N/A
N/A
N/A
Argentina
17.15%
2.55%
0.44%
80.18%
9
Colombia
7.13%
3.07%
0.22%
89.86%
9
France
84.67%
0.27%
0.23%
11.64%
2
Peru
6.09%
2.06%
0.13%
90.03%
10
Mexico
17.15%
1.79%
0.31%
72.72%
8
UK
N/A
N/A
N/A
N/A
N/A
South Africa
7.30%
1.06%
0.08%
90.07%
10
Iran
13.57%
6.58%
0.89%
80.68%
9
Chile
2.96%
5.26%
0.16%
94.27%
10
Iraq
13.20%
0.80%
0.11%
84.41%
9
Italy
31.64%
0.59%
0.19%
59.72%
6
Bangladesh
20.23%
N/A
N/A
78.32%
8
Germany
18.47%
1.23%
0.23%
78.88%
8
Indonesia
17.35%
N/A
0.00%
79.19%
8
Philippines
11.75%
3.70%
0.43%
86.39%
9
Top 10
19.12%
0.91%
0.17%
74.72%
8
Earth \ Top 10
18.11%
1.06%
0.19%
70.64%
8
Earth
18.81%
0.95%
0.18%
73.49%
8



Figure 2.4i: Leading Countries ordered by Testing Rate (September 16, 2020)


Testing rate is the fraction of tests relative to the population of a country. All countries with at least 80,000 COVID-19 reported cases ordered by testing rate are given in Figure 2.4i.

Figure 2.4j: Leading Countries ordered by Positivity Rate (September 16, 2020)


Testing rate is the fraction of tests relative to the population of a country. All countries with at least 80,000 COVID-19 reported cases ordered by testing rate are given in Figure 2.4j.


Table 2.3e: Leading Countries by COVID-19 Reported Laboratory Tests (October 20, 2020)
Location
Tests
Tests / Pop
Test Den [/km2]
Tests / Total
Positivity Rate
USA
127,057,107
38.32%
13.890
17.06%
6.66%
India
95,083,976
6.87%
31.980
12.76%
7.99%
Brazil
17,900,000
8.40%
2.142
2.40%
29.34%
Russia
54,300,208
37.20%
3.316
7.29%
2.61%
Spain
15,503,165
33.15%
31.081
2.08%
6.55%
Argentina
2,626,406
5.80%
0.960
0.35%
38.18%
Colombia
4,467,051
8.75%
4.026
0.60%
21.62%
France
13,765,883
21.08%
25.141
1.85%
6.61%
Peru
4,249,458
12.83%
3.320
0.57%
20.49%
Mexico
2,183,272
1.69%
1.123
0.29%
38.99%
UK
29,922,135
44.01%
123.681
4.02%
2.48%
South Africa
4,565,980
7.67%
3.764
0.61%
15.45%
Iran
4,540,455
5.39%
2.788
0.61%
11.77%
Chile
3,929,612
20.50%
5.285
0.53%
12.55%
Iraq
2,644,770
6.53%
6.089
0.36%
16.28%
Italy
13,639,444
22.57%
46.371
1.83%
3.11%
Bangladesh
2,178,714
1.32%
16.737
0.29%
17.91%
Germany
19,276,507
22.99%
55.303
2.59%
1.94%
Indonesia
4,092,595
1.49%
2.259
0.55%
8.92%
Philippines
4,398,498
4.00%
14.752
0.59%
8.17%
Top 10
337,136,526
13.79%
7.497
45.26%
8.40%
Earth \ Top 10
407,811,564
7.64%
4.828
54.74%
3.02%
Earth
744,948,090
9.57%
5.755
-
5.45%



Table 2.3f: General Logistic Model Start Date and Projected Reported Cases for Several Leading Countries (2020)
Location
Start Date
(2020)
RC on 10/1/20
[M]
RC on 11/1/20
[M]
RC on 12/1/20
[M]
RC on 1/1/21
[M]
USA
TBA
India
Brazil
Russia
Peru
Colombia
Mexico
Spain
South Africa
Argentina
France
Chile
Iran
UK
Bangladesh
Saudi Arabia
Iraq
Pakistan
Turkey
Italy


Table 2.3g: General Logistic Model Start Date and Projected Reported Deaths for Several Leading Countries (2020)
Location
Start Date
(2020)
RD on 10/1/20
[M]
RD on 11/1/20
[M]
RD on 12/1/20
[M]
RD on 1/1/21
[M]
USA
TBA
India
Brazil
Russia
Peru
Colombia
Mexico
Spain
South Africa
Argentina
France
Chile
Iran
UK
Bangladesh
Saudi Arabia
Iraq
Pakistan
Turkey
Italy


The time series of the cumulative COVID-19 reported cases and related deaths for the previously leading four countries, namely, U.S., Spain, Italy and France, from February 1, 2020 to April 14, 2020, are given in Figures 2.5a and 2.5b, respectively. This illustrates the meteoric rise of reported cases and related deaths of the U.S. compared to these countries. The growth of cases in the U.S. is consistent with that of the continent of Europe as a whole rather than any individual country therein, as discussed in the Introduction.

Figure 2.5a: Time Series of Cumulative COVID-19 Reported Cases for U.S., Spain, Italy and France (February 1, 2020 - April 14, 2020)


Figure 2.5b: Time Series of Cumulative COVID-19-Related Deaths for U.S., Spain, Italy and France (February 1, 2020 - April 14, 2020)


Sustained Exponential Growth

In contrast to the cumulative growth features seen in larger countries with a high number of cases that appear to follow a general logistic distribution model, however, several relatively small countries with a relatively smaller number of cases (under 100,000) exhibited multi-phase and sustained exponential growth. For example, the time series of the cumulative number of reported COVID-19 cases for the Central American country of Guatemala from March 21, 2020 to April 26, 2020 and April 10, 2020 to June 15, 2020 are given in Figures 2.6a/b. For the former time frame, the exponential fit has an amplitude of 14.708 reported cases and a growth rate of 0.0948 day-1 (R2 > 0.9951), the latter of which leads to doubling and order-of-magnitude times of approximately 7.3 days and 24.3 days, respectively. For the latter time frame, the exponential fit has an amplitude of 131.54 reported cases and a growth rate of 0.0674 day-1 (R2 > 0.9953), the latter of which leads to doubling and order-of-magnitude times of approximately 10.3 days and 34.2 days, respectively. On June 30, 2020, however, the sustained exponential trend in COVID-19 cases broke, and daily cases began to subside as expected, which is consistent with a general logistic distribution model.

2.6a.png
Figure 2.6a/b: Cumulative COVID-19 Reported Cases in Guatemala (April 3 - April 25, April 10 - June 15, 2020)


Domestic Trends

Nationwide Data


Table 2.4a: Descriptive Statistics of the COVID-19 Pandemic for the U.S. (October 20, 2020)
Cases
Active
Serious
Deaths
Tests
Top 10 States
4,664,476
1,375,558
-
130,305
67,486,614
Affected \ Top 10
3,555,869
1,229,185
-
89,535
58,021,513
Affected States + DC
8,220,345
8,220,345
-
219,840
125,508,127
Max
880,871
406,557
-
33,497
17,042,408
Mean
161,183
52,095
-
4,311
2,460,944
Median
107,748
23,984
-
2,180
1,480,763
Confidence (95%)
54,111
20,233
-
1,686
845,325

Beginning when the total of reported cases in the U.S. reached 100, from March 3-24, 2020, the cumulative reported cases could be well-modeled using a simple exponential fit with amplitude of 84.387 cases and growth rate constant of 0.295 day-1 yielding a doubling rate of ~2.4 days and an order of magnitude increase every ~7.8 days. Similarly, from March 14, 2020 through April 1, 2020, the number of deaths could be well-modeled using an exponential fit with an amplitude of 2.327 and rate constant equal to 0.258 day-1 yielding a doubling rate of ~2.7 days and an order of magnitude increase every ~8.9 days. These doubling times have since significantly increased.

After these periods of exponential growth, both the cumulative reported cases and cumulative reported deaths in the U.S. began increasing only approximately linearly, much less rapidly than during the exponential phase. This rate decline is likely due to a convolution of Stay-at-Home orders/advisories, social distancing measures, and mask adoptions, resulting in fewer seeding events with a certain degree of under-testing and test backlog in several laboratories combined.

Figure 2.7a: Time Series of COVID-19 Reported Cases in the U.S with a 7-Day Moving Average (January 1, 2020 - October 1, 2020)


Figure 2.7a: Time Series of COVID-19 RepoTable 2.4b: Significant U.S. Case Milestones in the COVID-19 Pandemic (September 23, 2020)


Milestone
Case Difference
Time Frame
Day Count [d]
Growth Rate [/d]
~0 to 0.5 million
512,010
1/20/2020 - 4/10/2020
81
6,321
0.5 to 1 million
502,927
4/10/2020 - 4/27/2020
17
29,584
1 to 1.5 million
504,041
4/27/2020 - 5/16/2020
19
26,528
1.5 to 2 million
491,591
5/16/2020 - 6/7/2020
22
22,345
2 to 2.5 million
495,921
6/7/2020 - 6/25/2020
18
27,551
2.5 to 3 million
538,674
6/25/2020 - 7/6/2020
11
48,970
3 to 3.5 million
504,468
7/6/2020 - 7/14/2020
8
63,059
3.5 to 4 million
479,296
7/14/2020 - 7/21/2020
7
68,471
4 to 4.5 million
472,188
7/21/2020 - 7/28/2020
7
67,455
4.5 to 5 million
538.470
7/28/2020 - 8/6/2020
9
59,830
5 to 5.5 million
496,871
8/6/2020 - 8/15/2020
9
55,208
5.5 to 6 million
468,528
8/15/2020 - 8/26/2020
11
42.593
6 to 6.5 million
494,070
8/26/2020 - 9/7/2020
12
41,173
6.5 to 7 million
512,873
9/7/2020 - 9/20/2020
13
39,452
7,097,937
1/20/2020 - Present
246
28,850



Figure 2.7b: Time Series of COVID-19 Reported Deaths in the U.S. with 7-Day Moving Average (February 20, 2020 - October 1, 2020)


Figure 2.7c: Weekday Distribution of U.S. Reported COVID-19 Deaths


Table 2.4c: Significant U.S. Death Milestones in the COVID-19 Pandemic (September 23, 2020)
Milestone
Case Difference
Time Frame
Day Count [d]
Growth Rate [/d]
~0 to 25,000
26,201
2/29/2020 - 4/12/2020
43
609
25,000 to 50,000
24,962
4/12/2020 - 4/23/2020
11
2,269
50,000 to 75,000
25,088
4/23/2020 - 5/6/2020
13
1,930
75,000 to 100,000
24,776
5/6/2020 - 5/23/2020
17
1,457
100,000 to 125,000
24,128
5/23/2020 - 6/22/2020
30
804
125,000 to 150,000
25,301
6/22/2020 - 7/26/2020
34
744
150,000 to 175,000
25,212
7/26/2020 - 8/18/2020
23
1,096
175,000 to 200,000
24,654
8/18/2020 - 9/15/2020
28
881
205,471
2/29/2020 - Present
206
997



Figure 2.7d: U.S. COVID-19 Case to Death Ratio (February 20, 2020 - August 24, 2020)


Figure 2.7e: U.S. Provisional Case Fatality Rates (February 29, 2020 - October 1, 2020)


Figure 2.7f: Daily U.S. COVID-19 Laboratory Tests (February 1, 2020 - August 24, 2020)


Figure 2.7g: U.S. COVID-19 Laboratory Test Positivity Rate (February 15, 2020 - August 24, 2020)


Figure 2.7h: Current U.S. COVID-19 Hospitalizations (March 18, 2020 - August 24, 2020)


Figure 2.7i: U.S. COVID-19 Hospitalization Rate (March 22, 2020 - August 24, 2020)


Figure 2.7j: U.S. COVID-19 ICU / Hospitalizations / Ventilation Rates (March 26, 2020 - August 24, 2020)


Statewide Data

During the rapid growth of COVID-19 reported cases and related deaths in the U.S., both state and federal guidelines and mandates had yet to appreciably abate the looming crisis. In fact, state governments were entirely responsible for their own management of their outbreaks, which were occuring with different start times, growth rates, and severities measured by the number of clusters. Several states instituted Stay-at-Home (or Shelter-in-Place) advisories and orders, but on different dates (Figure 2.8). South Carolina was the last state to adopt a Stay-at-Home advisory or order on April 7, 2020, while Arkansas, Iowa, Nebraska, and North Dakota have yet to take similar actions.

Figure 2.8: Timeline of U.S. State Stay-at-Home Advisories / Orders (April 7, 2020, Adapted from KFF)


As of July 14, 2020, New York, California, Florida, and Texas led the other contiguous U.S. states in terms of reported cases and related deaths. On July 12, 2020, the state of Florida posted a record breaking 15,300 new COVID-19 cases for the highest single-day increase in the U.S. since the beginning of the COVID-19 pandemic. The fact that population density alone does not account for the observed sequence of the most severely impacted U.S. states is evidenced in Table 2.5a, where current populations, population densities, reported cases, reported deaths, and current provisional case fatality rates for the leading U.S. states are given. A similar table for leading U.S. counties and boroughs of New York City in terms of reported cases are given in Tables 2.6 and 2.7, respectively.

Table 2.5a: Population, U.S. Population Fraction, Population Density, Median Age, and Urban Fraction of Several Leading States (October 20, 2020)
State
Population
U.S. Frac.
Density [km-2]
Median Age
Urban Frac.
California
39,512,223
12.04%
97.93
36.8
95.0%
Texas
28,995,881
8.83%
42.86
34.8
84.7%
Florida
21,477,737
6.54%
154.64
42.2
91.2%
New York
19,453,561
5.93%
159.38
39.0
87.9%
Illinois
12,671,821
3.86%
88.13
38.3
88.5%
Georgia
10,617,423
3.23%
71.28
36.9
75.1%
North Carolina
10,488,084
3.20%
83.29
38.9
66.1%
Tennessee
6,829,174
2.08%
63.94
38.8
66.4%
Arizona
7,278,717
2.22%
24.74
37.9
89.8%
New Jersey
8,882,190
2.71%
466.33
40.0
94.7%
Pennsylvania
12,801,989
3.90%
110.47
40.8
78.7%
Ohio
11,689,100
3.56%
110.45
39.4
77.9%
Louisiana
4,648,794
1.42%
41.54
37.2
73.2%
Wisconsin
5,822,434
1.77%
41.51
39.6
70.2%
Alabama
4,903,185
1.49%
37.38
39.2
59.0%
Virginia
8,535,519
2.60%
83.45
38.4
75.5%
South Carolina
5,148,714
1.57%
66.13
39.6
66.3%
Michigan
9,986,857
3.04%
68.20
39.8
74.6%
Missouri
6,137,428
1.87%
34.47
38.7
70.4%
Indiana
6,732,219
2.05%
72.55
37.9
72.4%
Top 10
166,206,811
50.64%
76.25
38.0
86.9%
U.S. \ Top 10
162,032,712
49.36%
23.25
38.7
74.7%
U.S.
328,239,523
35.88
38.3
80.9%



2.8.1.png


Table 2.5b: Leading States by COVID-19 Reported Cases and Related Deaths with Provisional Infection (per thousand pop.), Mortality (per million pop.), and Case Fatality Rates (October 20, 2020)
State
Cases
Deaths
PIR [/K]
PID [/km2]
PMR [/M]
PCFR
California
880,871
17,001
22.29
2.18
430.27
1.93%
Texas
877,077
17,599
30.25
1.30
606.95
2.01%
Florida
756,727
16,025
35.23
5.45
746.12
2.12%
New York
521,215
33,497
26.79
4.27
1,721.90
6.43%
Illinois
350,748
9,496
27.68
2.44
749.38
2.71%
Georgia
341,310
7,657
32.15
2.29
721.17
2.24%
North Carolina
247,172
3,939
23.57
1.96
375.57
1.59%
Tennessee
232,061
2,922
33.98
2.17
427.87
1.26%
Arizona
231,897
5,830
31.86
0.79
800.97
2.51%
New Jersey
225,398
16,339
25.38
11.83
1,839.52
7.25%
Pennsylvania
188,409
8,571
14.72
1.63
669.51
4.55%
Ohio
183,685
5,082
15.71
1.74
434.76
2.77%
Louisiana
175,982
5,766
37.86
1.57
1,240.32
3.28%
Wisconsin
173,891
1,600
29.87
1.24
274.80
0.92%
Alabama
173,485
2,789
35.38
1.32
568.81
1.61%
Virginia
166,828
3,457
19.55
1.63
405.01
2.07%
South Carolina
164,609
3,661
31.97
2.11
711.05
2.22%
Michigan
164,123
7,363
16.43
1.12
737.27
4.49%
Missouri
163,221
2,681
26.59
0.92
436.83
1.64%
Indiana
149,166
3,960
22.16
1.61
588.22
2.65%
Top 10
4,664,476
130,305
28.06
2.14
783.99
2.79%
U.S. \ Top 10
3,555,869
89,535
21.95
0.51
552.57
2.52%
U.S.
8,220,345
219,840
25.04
0.90
669.75
2.67%



2.8.2.png


2.8.3.png


2.8.4.png


2.8.5.png


2.8.6.png


Table 2.5c: Leading U.S. States by COVID-19 Reported Case Fractions (October 12, 2020)
State
Cases / Total
Active / Total
Serious / Total
Rec / Total
Deaths / Total
California
10.72%
15.61%
-
7.73%
Texas
10.67%
4.36%
-
8.01%
Florida
9.21%
9.43%
-
7.29%
New York
6.34%
2.94%
-
15.24%
Illinois
4.27%
3.22%
-
4.32%
Georgia
4.15%
6.54%
-
3.48%
North Carolina
3.01%
1.41%
-
1.79%
Tennessee
2.82%
0.89%
-
1.33%
Arizona
2.82%
7.20%
-
2.65%
New Jersey
2.74%
1.20%
-
7.43%
Pennsylvania
2.29%
1.27%
-
3.90%
Ohio
2.23%
1.06%
-
2.31%
Louisiana
2.14%
0.32%
-
2.62%
Wisconsin
2.12%
1.36%
-
0.73%
Alabama
2.11%
3.70%
-
1.27%
Virginia
2.03%
5.54%
-
1.57%
South Carolina
2.00%
3.01%
-
1.67%
Michigan
2.00%
1.81%
-
3.35%
Missouri
1.99%
4.79%
-
1.22%
Indiana
1.81%
1.41%
-
1.80%
Top 10
56.74%
52.81%
-
59.27%
U.S. \ Top 10
43.26%
47.19%
-
40.73%



Table 2.5d: Leading U.S. States by COVID-19 Severity Fractions (October 20, 2020)
State
Active / RC
Serious / Active
Serious / RC
Rec / RC
Rec Decile
California
46.15%
-
-
51.92%
6
Texas
12.94%
-
-
85.05%
9
Florida
32.46%
-
-
65.42%
7
New York
14.71%
-
-
78.86%
8
Illinois
23.90%
-
-
73.39%
8
Georgia
49.93%
-
-
47.83%
5
North Carolina
14.87%
-
-
83.53%
9
Tennessee
10.04%
-
-
88.70%
9
Arizona
80.86%
-
-
16.63%
2
New Jersey
13.89%
-
-
78.86%
8
Pennsylvania
17.61%
-
-
77.84%
8
Ohio
15.01%
-
-
82.23%
9
Louisiana
4.79%
-
-
91.94%
10
Wisconsin
20.35%
-
-
78.73%
8
Alabama
55.60%
-
-
42.79%
5
Virginia
86.48%
-
-
11.45%
2
South Carolina
47.57%
-
-
50.20%
6
Michigan
28.77%
-
-
66.74%
7
Missouri
76.46%
-
-
21.90%
3
Indiana
24.63%
-
-
72.71%
8
Top 10
29.49%
-
-
67.72%
7
U.S. \ Top 10
34.57%
-
-
62.91%
7
U.S.
31.69%
-
-
65.64%
7



Table 2.5e: Leading States by COVID-19 Reported Laboratory Tests (October 20, 2020)
Location
Tests
Testing Rate
Test Den [/km2]
Tests / Total
Positivity Rate
California
17,042,408
43.13%
42.24
13.58%
5.17%
Texas
8,078,858
27.86%
11.94
6.44%
10.86%
Florida
5,746,529
26.76%
41.38
4.58%
13.17%
New York
12,982,175
66.73%
106.36
10.34%
4.01%
Illinois
6,824,237
53.85%
47.46
5.44%
5.14%
Georgia
3,633,927
34.23%
24.40
2.90%
9.39%
North Carolina
3,640,086
34.71%
28.91
2.90%
6.79%
Tennessee
3,371,897
49.37%
31.57
2.69%
6.88%
Arizona
1,948,792
26.77%
6.62
1.55%
11.90%
New Jersey
4,217,705
47.48%
221.44
3.36%
5.34%
Pennsylvania
2,458,579
19.20%
21.22
1.96%
7.66%
Ohio
3,930,940
33.63%
37.14
3.13%
4.67%
Louisiana
2,594,376
55.81%
23.19
2.07%
6.78%
Wisconsin
1,877,704
32.25%
13.39
1.50%
9.26%
Alabama
1,321,900
26.96%
10.08
1.05%
13.12%
Virginia
2,583,644
30.27%
25.26
2.06%
6.46%
South Carolina
1,768,755
34.35%
22.72
1.41%
9.31%
Michigan
4,688,396
46.95%
32.02
3.74%
3.50%
Missouri
2,406,589
39.21%
13.52
1.92%
6.78%
Indiana
2,533,863
37.64%
27.31
2.02%
5.89%
Top 10
67,486,614
40.60%
30.96
53.77%
6.91%
U.S. \ Top 10
58,021,513
35.81%
8.33
46.23%
6.13%
U.S.
125,508,127
38.24%
13.72
-
6.55%



2.8.7.png


2.8.8.png


Purported Sources of Misinformation from Florida

It has been claimed by several scientists and the several U.S. news media outlets that politicians from the State of Florida are covertly misrepresenting COVID-19 laboratory testing data to skew results for political purposes. This involves two major concerns. Firstly, the positivity rate, which is the ratio of positive tests to total tests, may be misleading in the manner in which it is used and discussed by officials in Florida. Suppose an individual receives three negative tests before receiving one positive test. The positive test is counted only once in the numerator of the positivity rate, while all four tests are counted in the denominator, which could give the impression that there are fewer confirmed COVID-19 cases if a significant portion of the population takes more than a single COVID-19 test. Thus, the positivity rate may not give an accurate representation of the percentage of COVID-19 positive individuals among those who test for the disease. Secondly, in the State of Florida both molecular (RT-PCR) and antigen laboratory test counts are combined, but these methods have very different error rates due to differing specificity in their action. In fact, antigen laboratory tests, while accurate, have a higher false negative rate. RT-PCR laboratory tests, on the other hand, are more adapted to detecting an active infection of SARS-CoV-2. In the best case scenario, both tests would be given to confirm or rule out COVID-19.

Countywide Data



Figure 2.9: Time Series of Cumulative COVID-19 Reported Cases in


Several U.S. Counties (April 14, 2020)


Table 2.6: Leading U.S. Counties by Reported Cases and Deaths with New York City for Comparison (July 17, 2020)
City/County
State
Population
(% State)
Pop. Density [mi-2]
Reported Cases
Reported Deaths
PCFR
New York
NY
1,628,701 (8.38%)
72,056
219,670
21,941
9.99%
Los Angeles
CA
10,105,518 (25.55%)
2,488
100,772
3,326
3.30%
Cook
IL
5,180,493 (40.66%)
5,480
90,122
4,554
5.05%
Maricopa
AZ
4,329,580
1,219
45,178
746
1.65%
Nassau
NY
1,358,343 (6.99%)
4,763
41,780
2,692
6.44%
Suffolk
NY
1,481,093 (7.62%)
1,632
41,339
2,026
4.90%
Miami-Dade
FL
2,744,878
13,512
35,221
1,296
2.77%
Westchester
NY
967,612 (4.98%)
2,250
34,797
1,557
4.47%
Harris
TX
4,664,159
3,657
30,729
376
1.22%
Philadelphia
PA
1,584,138 (12.37%)
30,578
25,991
1,595
6.14%
Middlesex
MA
1,614,714 (23.39%)
5,113
23,946
1,862
7.78%
Wayne
MI
1,753,893 (17.55%)
2,866
22,686
2,713
11.96%
Dallas
TX
1,382,270
4,069
20,737
353
1.70%
Suffolk
MA
803,907
13,882
19,795
1,007
5.09%
Bergen
NJ
936,692 (10.51%)
4,070
19,638
1,985
10.11%
Hudson
NJ
676,061 (7.59%)
14,483
19,009
1,449
7.62%



Table 2.7a: New York Boroughs/Counties by Reported Cases and Related Deaths (July 20, 2020, Adapted from NYC.gov)
Borough
County
Population
(% NYC)
Pop. Density [mi-2]
Reported
Cases
Reported Deaths
PCFR
Queens
Queens
2,278,906 (27.13%)
21,460
68,145
7,128
10.46%
Brooklyn
Kings
2,582,830 (30.75%)
37,137
62,972
7,199
11.43%
The Bronx
Bronx
1,432,132 (17.05%)
34,653
49,646
4,816
9.70%
Manhattan
New York
1,628,701 (19.39%)
72,033
30,317
3,137
10.35%
Staten Island
Richmond
476,179 (5.67%)
8,112
14,639
1,075
7.34%
Unknown
-
-
-
19
33
-
8,398,748
28,188
225,738
23,388
10.36%


As of May 5, 2020, there were 5,359 deaths in New York City attributed to a probable COVID-19 infection. A probable COVID-19 death is defined if the decedent was a resident of NYC, had no known positive laboratory test for the presence of SARS-CoV-2, but resulted in a death certificate that lists COVID-19 or equivalent as a cause of death. From March 11 to April 16, 2020, there were an additional 9,447 deaths in New York City not known to be confirmed or probable COVID-19-related deaths.

Table 2.7b: New York City ZIP Codes by Reported Cases and Related Deaths (July 20, 2020, Adapted from NYC.gov)
ZIP
Code
Neighborhood
Population
(% NYC)
Pop. Density
[mi-2]
Reported
Cases
Reported Deaths
PCFR
11368
Corona
109,931 (1.30%)
41,798.9
4,737
409
9.03%
10467
Van Cortlandt Park
96,279 (1.14%)
41,215.3
3,496
300
8.68%
11373
Elmhurst
92,876 (1.10%)
61,022.3
3,323
292
8.70%
11219
Borough Park
92,858 (1.10%)
62,657.2
3,124
219
7.25%
10469
Pelham Gardens
71,508 (0.85%)
28,915.5
3,082
333
11.23%



Table 2.7c: NYC COVID-19 Reported Cases and Deaths by Age Group (June 18, 2020, Adapted from NYC.gov)
Age Group
Cases
% Total Cases
Deaths
% Total Deaths
PCFR
<18
5,801
2.79%
14
0.06%
0.24%
18-44
76,261
36.65%
825
3.72%
1.08%
45-64
75,494
36.28%
4,909
22.11%
6.50%
65-74
25,672
12.34%
5,346
24.16%
20.89%
>75
24,415
11.73%
11,028
49.68%
45.17%
Unknown
454
0.22%
59
0.27%
13.00%
Subtotal
208,097
97.59%
22,199
81.21%
10.67%
Probable
5,136
2.41%
5,136
18.79%
-
213,233
20,237
12.82%


As of May 11, 2020, the median age for a COVID-19 positive individual in NYC is 51 with 87,910 (47.69%) cases are younger than 50 years of age, 95,997 (52.08%) cases are aged 50 or older, and 412 (0.22%) of unknown age. Reported cases and related deaths by sex in NYC are given in Table 2.7d. Recorded and probable deaths in NYC by ethnicity are given in Table 2.7e.

Table 2.7d: NYC COVID-19 Reported Cases and Related Deaths by Sex (May 11, 2020, Adapted from NYC.gov)
Sex
Reported Cases
(% Total)
Reported Deaths
(% Total)
Probable Deaths (% Total)
PCFR
Female
9,658 (41.9%)
Male
13,416 (58.1%)
Unknown
9 (<0.01%)
23,083



Table 2.7e: NYC COVID-19-Related Deaths by Race and Ethnicity (May 21, 2020, Adapted from NYC.gov)
Race / Ethnicity
Reported Deaths
(% Total)
Probable Deaths
(% Total)
Related Deaths
(% Total)
Black / African American
3,870 (30%)
1,251 (32.6%)
5,121 (30.2%)
Hispanic / Latinx
4,112 (30.4%)
1,108 (28.9%)
5,220 (30.7%)
White / Caucasian
3,572 (27.9%)
1,091 (28.5%)
4,663 (27.5%)
Asian / Pacific Islander
1,019 (7.6%)
359 (9.4%)
1,378 (8.1%)
Unknown
576 (4.1%)
24 (0.6%)
600 (3.5%)
14,162
5,378
19,540



Figure 2.10a: NYC County Infection Density as a Function of Median Income (Adapted from N.Y. Times)


Figure 2.10b: NYC County Infection Density as a Function of Household Size (Adapted from N.Y. Times)

.


A Modified-SEIR Model

We model universal curves of reported COVID-19 daily reported infections and related deaths using a modified epidemiological Susceptible-Exposed-Infectious-Recovered (SEIR) Model.[2] Using currently available data, we determine optimized constants and apply this framework to reproducing the infection and death curves for California (the state with the largest population) and New York (the state with highest population density).

Figure 2.11: Parameter Relationships in the Proposed CSPDR Model


It is helpful to define various sets that appear in the model as time-dependent variables. In the early stages of an epidemic or pandemic, the vast majority of individuals are susceptible to infection, while few individuals have recovered from an infection. However, there are intermediate steps between infecting others with asymptomatic or pre-symptomatic, becoming symptomatic, getting tested and confirmed to be positive, all or most of which allow for further spread of the pathogen by the contagious. This is further adjusted by the removal of contagious individuals from the contagious population through self-isolation or hospitalization and complicated by imperfect testing protocols with lab saturation and false negatives. To account for these variables, we first group those intermediates into a symptomatic set S. We make the assumption that recovery rates are identical across all populations, and note that we may ignore the population of individuals who have been exposed to the pathogen but are not yet contagious. Our proposed CSPDR Model (pronounced “C-Spider”) then describes the flow of individuals from the contagious set C to the symptomatic set S to the positively confirmed set P to the deceased set D, with the possibility that at any point an individual may move to the recovered set R. This results in an initial system of coupled ODEs:

where we assume that contagious individuals are able to expose others at a variable infectivity rate (related to the basic reproduction number R0 of the pathogen), both of whom may become symptomatic, then self-isolate and/or are admitted to the hospital (thus discounting their ability to further infect), at fixed rate . From there, symptomatic individuals may be tested and, if tested, a portion are confirmed to be positive with rate , at which point they either recover from the disease to the point where they are no longer contagious at rate or succumb to disease at rate . We ignore infections to healthcare workers, which is small compared to community spread. Similarly, although handling the deceased has been demonstrated to infect the healthy in certain isolated instances, it is at such a small rate that we may safely ignore any linear terms in D in the last ODE of the system. Finally, we add an elaboration for the rate ,

to account for the time dependence of the infection rate, rendering the ODE system inhomogenous and not amenable to analytical or matrix-exponentiation techniques. We use a logistic function[3] in time with fixed width parameter set to 1 day to interpolate between two extremal constant values for the infectivity rate before and after a Stay-at-Home advisory or order plus a learned time delay to reflect the response between the order and its consequence on the mitigation of pathogen spread. The difference in the two infection rates indicates the effectiveness of the order on reducing the growth of new infections. Note that if there were no symptomatic set, the set of infected and positively-confirmed individuals would be directly proportional to the set of contagious individuals at any point in time. Even with an incubation period, this would imply a sudden change in growth rates for newly confirmed cases across (in a log-scaled graph, a sharp point), which is not observed in the data, but only rather a smooth transition.

Figure 2.12: CSPDR Model Curves and Experimental data of U.S. COVID-19 Daily Reported Cases and Related Deaths including the Predicted Contagious and Symptomatic Populations


We expect newly reported cases and deaths to vary up to from their underlying values based on simple count statistics, although the resulting number ignores other systematic influences on our data such as time-varying testing rates or imperfect tests. To obtain a more realistic spread from our predicted parameters, we perform bootstrap sampling (i.e., sampling training data with replacement and adding samples from a Normal Distribution with a Standard Deviation of to the recorded values), fit the rate parameters and C0 using a discrete ODE solver, and minimize the Least-Squares Error over newly confirmed cases and deaths after the 100th case and death has been identified, respectively.

The symptomatic set is able to aggregate ejections from the contagious set and can exceed the contagious set if sufficiently many infected individuals self-isolate. Newly positive confirmations are proportional to the current size of the symptomatic pool . The deceased set shows additional flattening and a delayed response from the positive pool, which is supported by the observed data. Over time, the growth rate of newly confirmed cases appear to align with the growth rate of the contagious. This is apparent in the observed U.S. reported cases and deaths, as in Figure 2.12.

For the California data, we find that the daily infection rates have diminished from approximately 41% to 16% from March 1, 2020 to April 15, 2020 with the infection-to-confirmed rate averaging 0.71% (12% of 5.9%) and the new fatality to new case rate averaging 0.43%. These numbers align with a doubling time of 1.7-4.4 days, and a case fatality rate (integrated over 2 weeks) of ~6%.

Figure 2.13a: CSPDR Model Curves and Experimental Data of U.S. COVID-19 Daily Reported Cases and Related Deaths in California


For the New York data, we find that the daily infection growth rates have diminished from approximately 74% to 29% from March 1, 2020 to April 15, 2020, with the daily case fatality rate averaging 0.42%. These numbers align with the California data cutting the exposure rate by about half before and after the Shelter-in-Place order which, due to exponential growth, has an enormous impact on total cases as well as the case fatality rate.

As can be seen in Figures 2.14a and 2.14b, the infection growth rates are substantially higher for New York, the former reflecting the higher population density and greater ease with which disease can spread in the busy and dense metropolitan of New York City. Moreover, our expectations for these rates are bimodal, reflecting the two periods of intense spreading before March 1, 2020 and after March 15, 2020. Note also that the death rate, as indicated by the slope of the curve in Figure 2.14a, is currently higher than one might expect, compared to the infection rate two weeks prior. This suggests that testing rates are higher for deaths than otherwise in the state of New York.

Figure 2.13b: CSPDR Model Parameter Estimates for California


The fact that the contagious-to-positive rate (is higher in the state of New York than in California remains unexplained. However, although the contagious and symptomatic numbers are hidden variables in our model, we only have observations for reported cases and deaths, and this rate is less informative and is strongly defined by two elements: the infection rates, and the sharpness of the change in infection rates before and after a Stay-at-Home advisory or order, which is noticeably stronger in New York than California. There could be many reasons for this, including changes to the testing process (corroborated by the current disparity between the case fatality rate from expectations). For instance, the two sharp slopes in infection rates suggests that testing may have been lagged between March 1-15, 2020. Note that in this study of the available COVID-19 data for California and New York, we see that the CSPDR Model cannot account for the repeatedly suggested “[continuing] flattening of the curve” well after the Stay-at-Home advisory or order date for either state, indicating that the corresponding daily infection rates have either stabilized or begun to decrease slightly or that testing is increasingly delayed. Lastly, examining the U.S. data in comparison to individual state data, we see that the former behavior better mimics California rather than New York, likely reflecting its lower overall population density. However, while the initial contagious daily growth rate reflects that of California, the current daily infection growth rate is far lower.

Figure 2.14a: CSPDR Model Curves and Experimental Data of U.S. COVID-19 Daily Reported Cases and Related Deaths in New York


Figure 2.14b: CSPDR Model Parameter Estimates for New York


Figure 2.15a: CSPDR Model Curves and Experimental Data of COVID-19 Daily Reported Cases and Related Deaths in the U.S.


Figure 2.15b: CSPDR Model Parameter Estimates for the U.S. The time shift is in reference to the New York Shelter-in-Place Order of March 20, 2020.


Bayesian Inference / Convolution Model

Symptoms of Infection

A COVID-19 infection appears to be influenza-like in its symptoms, if present at all, usually lasting about 2-3 weeks, and may include, but are not limited to, a fever (83-99%), dry and unproductive cough (59-82%), loss of appetite (40-84%), fatigue (44-70%), shortness of breath (31-40%), sputum/mucus-productive cough (28-33%), muscular ache and pain (11-35%), in addition to scratchy or sore throat, runny nose, swollen lymph nodes, skin rashes, headache, sneezing, diarrhea, nausea, vomiting, dizziness, sensory loss, conjunctivitis, and inflammation in fingers and toes.

Table 2.8: Common Symptoms of Some Human Coronaviruses Diseases (June 19, 2020)
Disease
Fever
Dry Cough
Dyspnea
Diarrhea
Sore Throat
Ventilation
MERS-12
98%
47%
72%
26%
21%
24.5%
SARS-04
>99%
29-75%
40-42%
20-25%
13-25%
14-20%
COVID-19
87.9%
67.7%
18.6%
3.7%
13.9%
4.1%


A report from ENT U.K. indicates that a loss of sense of smell and taste (anosmia or hyposmia) may be a characteristic symptom of COVID-19, and supporting evidence continues to grow. The report highlights that over ⅔ of current COVID-19 patients in Germany, for example, report this particular symptom. The report goes on to state that a growing number of doctors have been reporting a surge in isolated cases of loss of smell or taste in regions more heavily impacted with COVID-19, suggesting the symptom may be an indicator of asymptomatic and otherwise undiagnosed individuals carrying the virus. Complications that may require clinical intervention include unilateral or bilateral pneumonia, blood clots, sepsis, and, in severe cases, heart damage, cardiac arrest, stroke, septic shock, Acute Respiratory Distress Syndrome (ARDS) requiring assistive ventilation. In the most severe cases, similar to influenza and typically involving individuals with underlying pulmonary conditions, COVID-19 may result in death.

The WHO defines four degrees of a COVID-19 infection:

  1. Mild (does not typically require clinical intervention)

No fever or low-grade fever of 100 °F (37.8 °C) or lower, some respiratory symptoms (dry and unproductive cough), and some aches and pains.

  1. Moderate (may require clinical intervention)

Mild COVID-19 symptoms with fever above 100 °F (37.8 °C), lethargy, some shortness of breath, and possibly early pneumonia.

  1. Severe (often requires clinical intervention)

Moderate COVID-19 symptoms with fever above 104 °F (40 °C), significant difficulty of breathing. Approximately 20% of moderate cases progress to severe. Approximately 14% of severe cases require supplemental oxygen.

  1. Critical (requires emergency intervention)

Severe COVID-19 symptoms with further complications, including ARDS. Approximately 6% of severe cases become critical and may experience septic shock, which may lead to organ failure, and even death. Some patients have also experienced blood clots, sometimes culminating in strokes.

To see the rapid progression of the pandemic, as of March 15, 2020, there were 159,757 reported cases of COVID-19 worldwide, including 75,958 recoveries and 5,960 deaths, with 3,045 reported positive cases in the U.S. Roughly 8% of active cases worldwide (~75k, as of March 14, 2020) were severe or critical, the rest were considered mild or moderate. Today, these counts are orders of magnitude higher (See Introduction). In particular, on March 3, 2020, the WHO reported that the worldwide provisional case fatality rate of SARS-CoV-2 was 3.4%, which doubled to 6.92% by May 17, 2020, then settled back to approximately 4% by August 24, 2020, and accounts for mostly older individuals (65+), the immunocompromised, and/or those with pre-existing conditions such as diabetes, heart disease, high blood pressure, and Chronic Obstructive Pulmonary Disease (COPD). How generalizable this figure will be depends on the rate at which asymptomatic but infected individuals are tested and the adequacy of treatment of infected and symptomatic patients.

Transmission Factors

SARS-CoV-2 is a highly contagious virus whose modes of transmission remain under scrutiny. It is particularly concerning that individuals infected with SARS-CoV-2 may become contagious before developing symptoms, a time when some first become aware of a possible infection. The time window in which the infected remain most contagious is a public health concern, which has consequences that extend to recommendations concerning quarantine practices for the sick and their contacts as well as to safety guidelines for healthcare workers that face exposure risk. For this reason, defining the latency period, the time frame from initial exposure to the virus to the time when the infected become contagious, the communicability period, the time frame the infected can spread the virus to others, and the incubation period, the time frame from initial exposure to the development of symptoms, is of the utmost importance. Figure 2.16 illustrates these three time frames in a typical disease progression.

We define these periods for COVID-19 and explore different possible routes of transmission including contact, fomite, aerosol droplet, and airborne transmission. The nature of asymptomatic cases and how they may contribute to disease transmission are considered as well.

Figure 2.16: Disease Incubation, Contagion, and Symptomatic Periods


Incubation and Communicability Periods

A study of 181 well-described symptomatic cases of COVID-19 estimated the median incubation period of SARS-CoV-2 to be 5.1 days, with 95% of symptomatic patients developing symptoms between 2 and 14 days (Lauer et al., 2020). More specifically, the authors estimate that 2.5% cases became symptomatic within 2.2 days and 97.5% became symptomatic within 11.5 days. Figure 2.17 illustrates the estimated cumulative distribution of the proportion of symptomatic cases with respect to days since infection.

A March 2020 study of 9 SARS-CoV-2 infected individuals found that study subjects were most contagious one week before and during the first week of being symptomatic, when the virus is shedding at its maximum rate (Woelfel et al., 2020). The authors also note that in some instances of infected individuals, viral RNA was detected in sputum samples after subjects had no detectable symptoms of the disease. For this reason, Woelfel et al. note that “[b]ased on the present findings, early discharge with ensuing home isolation could be chosen for patients who are beyond Day 10 of symptoms with less than 100,000 viral RNA copies per ml of sputum. Both criteria predict that there is little residual risk of infectivity, based on cell culture.” In a study of 73 patients, SARS-CoV-2 has also been detected in fecal matter up to 12 days after respiratory tests are negative for COVID-19 infection (Xiao, F., et al., 2020).

Figure 2.17: Cumulative Distribution of Proportion of Symptomatic Cases with Respect to Days Since Infection (Adapted from Laurer et al., 2020)


Basic Reproduction Number

The effective reproduction number, R, for an infection is the average number of people directly infected by a single case of the infection within a population. The basic reproduction number R0 of a pathogen is the value of R when the entire population is susceptible to a disease and no control measures have yet been implemented. The basic reproduction number of several pathogens and their corresponding diseases, including current estimates for SARS-CoV-2 and COVID-19, is given in Table 2.9.

Table 2.9: Basic Reproduction Numbers of Several Pathogens (October 2020)
Pathogen
Disease
Transmission
Incubation [d]
R0
Measles virus (MeV)
Measles
Aerosol
9-12
12-18
Varicella-zoster virus
Chickenpox
Aerosol
9-21
10-12
Mumps orthorubulavirus
Mumps
Respiratory Droplets
14-18
10-12
Polio virus
Polio
Fecal-Oral Route
7-14
5-7
Rubella virus (RuV)
Rubella
Respiratory Droplets
14-21
5-7
Variola major
Smallpox
Respiratory Droplets
7-17
3.5-6
SARS-CoV-2
COVID-19
Respiratory Droplets, Biofluids, Aerosol
2-14
(avg. 5.1)
0.5-6
HIV
AIDS
Biofluids
>14
2-5
Various
Common Cold
Respiratory Droplets
1-3
2-3
Influenza A (H1N1)
Spanish Flu (1918)
Respiratory Droplets
1-3
1.4-2.8
Ebola virus (EBOV)
EVD
Biofluids
1-42
1.5-1.9
Influenza A (H1N1)
Swine Flu (2009)
Respiratory Droplets
1-3
1.4-1.6
Influenza A-C (various)
Seasonal Flu
Respiratory Droplets
1-3
0.9-2.1
SARS-CoV-1
SARS-02
Respiratory Droplets
1-10
0.19-1.08
MERS-CoV
MERS-12
Respiratory Droplets
2-14
0.3-0.8


Contact and Droplet Transmission

When the aerosolized respiratory droplets of an infected individual come in contact with the mucus membrane (e.g., mouth, nose, or eyes, etc.) of another individual, transmission may occur. Due to gravity, however, larger respiratory droplets have a shorter range in the distance they can travel. Therefore, the largest (and most infectious) respiratory droplets that may be produced from talking, coughing, or sneezing are distance-limited. The CDC recommendation of maintaining a minimum distance of 6 feet (~2 meters) from other individuals was based on this semi-ballistic understanding of the dynamics of respiratory droplets, which limits the range of their flow in air. However, certain effects can enhance the range in which these droplets can distribute, especially favorable air flow and increased force in the emission of the droplets. For this reason, shouting, singing, and increased indoor ventilation (e.g., fans, HVAC, etc.), which enhance the force of emission or permit a wider range of fluid flow have all been associated with enhanced transmission, particularly in indoor settings, where dispersion is limited.

In a March, 2020 article in JAMA that uses a new model for respiratory emissions, Bourouiba describes sneezes, coughs, and related exhalations as turbulent gas clouds carrying clusters of respiratory droplets of various sizes. The air encapsulating the droplets limits dispersion or evaporation much longer than would be anticipated with rarefied droplets. Moreover, the forward momentum of the cloud can propel droplets farther. Bourouiba estimates that all droplet sizes within such a gas cloud could travel as far as 23-27 feet (7-8 meters). For this reason, the current CDC recommendation for maintaining 6 foot separation from others may need to be revised, as it possibly underestimates the distance that respiratory droplets can travel.

It should also be noted that stool samples taken from infected patients have also tested positive for SARS-CoV-2 (Holshue et al., 2020), which may suggest that the virus can be transmitted via the fecal-oral route. Thus, contact with infected stool could pose as another viable transmission mode.

Finally, it is important to discern pre-symptomatic individuals, those that will become symptomatic but are still in the incubation period of disease progression, from asymptomatic individuals, those who never develop symptoms of the disease (See Asymptomatic Cases and Transmission). While asymptomatic transmission has not yet been confirmed, some studies have established that infected individuals can transmit SARS-CoV-2 to others 2-3 days before onset of symptoms, and they may be most contagious up to 24 hours before notable symptoms appear (Pan et al., 2020). To complicate matters, the sputum of infected individuals may continue to act as contagion vectors for several days after the clearance of most symptoms (Woelfel et al., 2020). Some data suggest that SARS-CoV-2 may continue to shed for as long as 37 days after the onset of symptoms (Zhou, F. et al., 2020).

Face Masks and Other Methods for Limiting Droplet Transmission

The use of face masks has shown to be an effective way of limiting respiratory droplet dispersion into the surrounding environment, thereby lowering the risk of SARS-CoV-2 transmission. Previous studies have provided evidence that widespread use of face masks has reduced the spread of COVID-19. As a result, various government agencies have recommended and in some cases mandated the use of them in an effort to limit the spread of the disease. However, the use of face masks has been a complicated issue, particularly in the earliest stages of the epidemic, when their supply was severely limited. N95 respirators and surgical masks in particular were often recommended solely for use by medical personnel who needed them most urgently during a time of severe shortage. As a result, many medical agencies, including the CDC, recommended the use of low-cost fabrics and textiles, such as bandanas and other common cotton and polyester clothing items, for the fabrication of such masks. Despite these recommendations, natural questions concerning the efficacy of such fabrics for limiting droplet transmission, as well as how proper fitting may affect such efficacy, have arisen.

Fischer et al. (2020) tested the efficacy of 14 commonly available masks and mask alternatives and one patch of mask material to limit respiratory droplet dispersion using a fairly simple experimental setup. An individual wearing one of these masks spoke in the direction of a laser operating inside a dark enclosure. Any droplets that successfully disseminated through the emitted light would scatter the light, and a cell phone was used to video record this scattering. Droplet counts in the video were then determined using a computer program. These videos were compared to a video of a control setup, where a speaker wearing no mask spoke in the direction of the laser. The 14 mask types tested were as follows: a surgical 3-layer mask (Surgical), an N95 mask with an exhalation valve (Valved N95), a knitted mask (Knitted), a 2-layer polypropylene apron mask (PolyProp), a cotton-polypropylene-cotton mask (Poly/Cotton), a 1-layer Maxima AT mask (MaxAT), a 2-layer cotton pleated style mask (Cotton2), a 2-layer cotton Olson style mask (Cotton4), a 2-layer cotton pleated style mask (Cotton3), a 1-layer cotton pleated style mask (Cotton1), a gaiter type neck fleece (Fleece), a double-layer bandana (Bandana), a 2-layer cotton pleated style mask (Cotton5), a fitted N95 mask with no exhalation valve (Fitted N95). The mask material tested was a swath of polypropylene mask material (Swath). Figure 2.18 shows the relative droplet count recorded from a person speaking into a mask made of each of these materials when compared to wearing no mask (None). The researchers note that with the use of some masks, particularly the neck fleece, larger droplets appeared to be broken up into a large number of smaller droplets, which may explain the high count of droplets for the neck fleece. As smaller droplets may remain airborne for a longer period of time, this effect may have the undesired consequence of increasing possible transmission.

Figure 2.18: Relative Droplet Count Recorded During Use of 14 Masks and One Swath of Mask Material When Compared to No Mask Use (Adapted from Fischer et al., 2020)


Evidence for Airborne and/or Fomite Transmission

Though it is not clear if SARS-CoV-2 can be transmitted through airborne means, respiratory droplets can remain in the air for extended periods of time and can land on surfaces rendering them fomites, or inanimate vectors. In fact, SARS-CoV-2 can remain airborne from 30 minutes to 3 hours, and can remain active for 4 hours on copper, 24 hours on cardboard, and 2-3 days on plastic and stainless steel (van Doremalen et al., 2020), though it is unclear if contact with such fomites can cause transmission.

Chin et al. (2020) tested 5 µL samples of viral culture incubated at 22℃ and at 65% humidity on various surfaces. They reported that no infectious virus could be recovered from printing and tissue papers after 3 hours, but on wood or cloth, the period until no detection was longer, at 2 days of incubation. The authors also note that SARS-CoV-2 is more stable on smooth surfaces. When samples were incubated on glass, paper money, and the inner layer of surgical masks, the infectious virus could be recovered by Day 2 but not by Day 4, and when incubated on stainless steel and plastic, the infectious virus was detectable by Day 4 but not by Day 7. Finally, when left on the outer layer of a surgical mask, infectious virus could still be recovered at 7 days of incubation.

Based on the results of five studies that found evidence for SARS-CoV-2 transmission through bioaerosols in hospitals, Harvey Fineberg, the Chair of the Standing Committee on Emerging Infectious Diseases and 21st Century Health Threats, published a letter on April 1, 2020 to the National Academy of Sciences, Engineering, and Medicine detailing the possibility that the virus could also be spread through an infected individuals’ exhalation. Since the virus can remain in air up to 3 hours, this raises the possibility of increased transmission in indoor spaces through a shared air supply. In contrast, outdoor transmission through bioaerosols is less likely, as bioaerosols will disperse more readily in open air.

In an effort to quantify viral concentration on possible hospital room fomites and in the air, Santarpia et al. (2020) conducted a study of the University of Nebraska hospital rooms of 13 COVID-19 patients in isolation. Room surfaces and the patient’s personal items were first tested by RT-PCR for detectable presence of SARS-CoV-2. Of the 163 samples tested, 126 tested positive for the virus. Of the personal items collected (which included cell phones, iPads, laptops, reading glasses, exercise and medical equipment), 81.3% tested positive, with a mean concentration of 0.217 copies/µL of recovered liquid sample. 83.3% of cell phone samples were positive for the virus with a mean concentration of 0.172 copies/µL. 64.7% of remote controls were positive with a mean concentration of 0.230 copies/µL. 81% of toilet samples were positive with a mean concentration of 0.252 copies/µL. All floor samples beneath patient beds tested positive for the virus, with a mean concentration of 0.447 copies/µL. The sample with the highest concentration of virus at 1.75 copies/µL was taken from the air handling grate in the hospital biocontainment unit, where only patients with severe COVID-19 were kept (mild cases were kept in the quarantine unit). Of the five biocontainment unit air grate samples, 80% tested positive with a mean concentration of 0.819 copies/µL. Air samples were also tested for SARS-CoV-2. 63.2% of in-room air samples tested positive with a mean concentration 2.86 copies/L of air. 66.7% of air samples taken from hallways outside the hospital rooms tested positive with a mean concentration of 2.59 copies/L of air. The air sample with the highest concentration of virus (at 19.17 and 48.21 copies/L of air) came from the biocontainment unit when a patient was receiving oxygen through a nasal cannula. It should be noted that while air samples were being collected from rooms, patients were never observed to cough. These data suggest the widespread presence of viral RNA on surfaces and in air samples of patient rooms, demonstrating the possibility for transmission through bioaerosols and fomites.

Asymptomatic Cases and Transmission

One study conducted with the entire Italian city of Vò (~3,300 individuals) indicates that approximately 42.5% of COVID-19 infected individuals were entirely asymptomatic (Lavezzo et al., 2020). As of July, 2020, it still remains unclear what percentage of asymptomatic carriers of SARS-CoV-2 are contagious.

Long et al. (2020) conducted an assessment of 37 asymptomatic individuals who were confirmed to have SARS-CoV-2 infections and compared them to a larger cohort of symptomatic COVID-19 patients. The asymptomatic group had a significantly longer period of viral shedding with a median duration of 19 days (IQR of 15-26 days), compared to a median duration of 14 days in patients experiencing mild symptoms. While the period of viral shedding was longer in asymptomatic individuals, the authors note the finding should not be interpreted to suggest these patients were any more contagious, as many other factors may contribute to communicability.

Some limited evidence for fomite/airborne transmission from an asymptomatic carrier does exist, however. Liu, J., et al. (2020) describe a cluster of 71 COVID-19 cases that developed in Heilongjiang Province, China, attributed to one asymptomatic carrier (confirmed by IgG antibody test on April 10, 2020). The carrier had returned from international travel in the U.S. on March 19, 2020, a full 8 days after the last known COVID-19 diagnosis in the province, and quarantined at home for two weeks, then tested negative by RT-PCR and antibody tests on March 31 and April 4, 2020, respectively. On April 9, 2020, four new cases since March 11, 2020 were identified, which were contact-traced to a neighbor of the asymptomatic carrier. It is believed that the carrier transmitted the virus to the neighbor through fomite and/or airborne transmission in a common elevator, as the two parties had no other form of contact. All other individuals in the cluster were also traced to the same neighbor.

Pathophysiology

In late 2019, during its early emergence in Wuhan, China, COVID-19 was observed as an unexplained outbreak of acute respiratory disease tied to increased rates of pneumonia in the region. The subsequent classification of the disease as a coronavirus closely related to SARS-CoV-1, which also uses the ACE2 receptor for cellular entry, drove further research into the systems most likely to be targeted by the virus. While ACE2 receptors are abundant in many organ systems, the predominance of respiratory symptoms in severely affected patients drove many early studies to focus on the SARS-CoV-2’s disruption of the respiratory system, in particular a chain of events affecting this system that could lead to Acute Respiratory Distress Syndrome (ARDS) and in some cases, septic shock and death. However, autopsy reports revealed that other systems were often implicated in the progression of the disease. The widespread prevalence of blood clotting, strokes, neurological symptoms as well as damage to other organs such as the heart and kidneys came to be known as hallmarks of the disease’s progression in some of the most severe and long-lasting cases.

An understanding of the mechanisms underlying how COVID-19 contributes to respiratory distress, coagulopathy, and the disruption of other organ systems is essential for the effective treatment of affected patients both acutely, chronically, and in recovery. Moreover, the severity and presence of COVID-19 symptoms varies widely between individuals. To effectively treat a newly diagnosed patient, it is thus crucial to identify what pre-existing risk factors, whether genetic or otherwise, may drive these differences. In this way, patients can be treated both preemptively and safely, maximizing their chance of survival and minimizing their chance of long-term deleterious effects. Furthermore, genetic variations in the virus itself may cause different pathophysiology, and such variants are only growing in number as the virus continues to mutate and evolve. The growing complexity in the multivariate system that determines the exact disease course in an affected patient underscores the need for precise, personalized treatments that address a patient’s unique risk factors directly.

Pulmonary Pathophysiology

Severe cases of COVID-19 infection are characterized by various mechanisms, the first of which is pulmonary damage from the virus itself. As the viral RNA replicates and assembles its structural protein, which occurs predominantly in the alveolar cells of the lower respiratory tract, the newly emerging viral particles lyse the host cells. Cumulative damage can lead to structural damage in the alveolar cells of the lungs. The primary alveolar cells affected are pneumocytes I and II. These cells line the pulmonary alveoli, small sacs where gas exchange takes place in the lungs, specifically where blood becomes oxygenated.

However, the primary mechanism driving severe COVID-19 disease originates from an overactive inflammatory response to the infection itself. Mononuclear leukocytes are the early initiators of the inflammatory response in the lungs. The infiltration of these white blood cells to the site of infection results in the earliest stage of the inflammatory response: a release of cytokines, such as interleukin-1 and interleukin-6. The cytokines stimulate nearby blood capillaries to vasodilate and become more permeable to fluids passing through them, including blood plasma, which can move into the interstitial space between the blood vessels and alveoli. This fluid will increase pressure on the alveolar cells, potentially leading to their collapse. Cytokines released by the immune cells also trigger more immune cells, including neutrophils and T-cells, to infiltrate the alveolus, initiating a cascading inflammatory effect, known as a “cytokine storm”. Recruited neutrophils then release reactive oxygen species and proteases that are intended to eliminate the virus but also damage nearby alveolar cells. As these cells undergo apoptosis (cell death), they generate debris in the alveolus, preventing gas exchange from occurring. Blood oxygenation then decreases, resulting in hypoxemia (low oxygen in blood). This chain of events is the mechanism behind acute respiratory distress syndrome (ARDS), which is the leading driver of mortality in COVID-19 infection. ARDS is also often characterized by partial collapse of the lung. In some severe cases, the inflammation from the lungs may carry over into the bloodstream, leading to systemic inflammatory response syndrome (SIRS). SIRS can potentially progress into acute organ injury, septic shock, and multi-system organ failure.

Figure 2.19: Colorized SEM of apoptotic human cell infected with SARS-CoV-2 (Adapted from NIAID)


Vascular Pathophysiology and Coagulopathy

Blood clots and large-vessel strokes are complications that may also be tied to COVID-19. From March 23, 2020 to April 7, 2020, Oxley et al. report that five patients under the age of 50 were treated at Mt. Sinai Hospital for large-vessel ischemic stroke. All tested positive for SARS-CoV-2. The five patients represented a considerable jump from the average 0.73 patients under 50 treated for large-vessel stroke for any two week period in the previous twelve months at the same hospital. Li et al. (2020) report in a retrospective study of 221 COVID-19 patients treated in Wuhan, China that 5% of the patients developed acute ischemic stroke. For those patients exhibiting signs of cerebrovascular disease, levels of D-dimer were also significantly elevated, at 6.9 mg/L versus 0.5 mg/L for patients without these symptoms (ibid.). C-reactive protein was also significantly elevated for this group. D-dimers are a fibrin degradation product and are indicative of the presence of blood clots.

Wichmann et al. (2020) report an increased risk of deep vein thrombosis in severe COVID-19 infections, as revealed from the first 12 consecutive autopsies of patients performed at a university hospital in Germany, where autopsies were mandated for patients testing positive for SARS-CoV-2 by RT-PCR. Deep vein thrombosis was confirmed in the autopsies of 58% (7/12) patients, all of whom were not suspected to have this condition before death. For these patients, DVT was found in both legs. In six of nine of the men studied, thrombosis was also found in the prostatic venous plexus. Furthermore, the authors report that pulmonary embolism was the direct cause of death in 4 of the 12 cases.

While discussing a case study of a 72-year-old patient with COVID-19, Escher et al. (2020) suggest a potential mechanism for the increased risk of coagulopathy that has been observed in these critically ill patients. Six days after the patient initially had been admitted into the hospital, he had developed ARDS, acute renal insufficiency, and mental confusion and was transferred to the ICU where he was observed for 29 days. Levels of D-dimers steadily increased over this time, beginning at 0.69 mg/L (relatively normal) and peaking at 20.63 mg/L on Day 21. On the same day, the patient was found to have elevated von Willebrand Factor (VWF), at 520% the level of what would be considered normal. VWF is released from the sub-endothelial layer below the lumen of blood vessels when the endothelial layer is damaged, and it is also indicative of thrombosis. At these high levels, the VWF measurement was suggestive of massive endothelial damage.

Escher et al. (2020) note that the endothelial cells that line the blood vessels express the ACE2 receptor, which is the cellular entrypoint of SARS-CoV-2. Aside from the potential damage to the endothelial cells that could be caused from the virus reproducing and lysing these cells, if SARS-CoV-2 is bound to ACE2, ACE2 can no longer catalyze the conversion of angiotensin-II into angiotensin-1,7 (see ACE2 Receptor), which leads to vasodilation, increased blood pressure, and increased vascular permeability. Moreover, the increased levels of angiotensin-II can stimulate catalytic activity of NADPH oxidase, an enzyme that catalyzes the conversion of oxygen into superoxide radical, a reactive oxygen species (ROS) that can build up in endothelial cells. Build-up of ROS can lead to intracellular oxidative stress, which can cause cell death. In mice, ACE2 has been shown to attenuate the oxidative stress caused by increased NADPH oxidase activity induced by elevated angiotensin-II levels, as well as atherosclerosis (Lovren et al., 2008). Thus, the decreased availability of ACE2 catalytic sites due to SARS-CoV-2 infection may be mediating the damage to the endothelial cells, leading to a cascade of events that result in increased blood clot formation and potentially stroke. Furthermore, for patients with dysfunctional super oxidase dismutase (SOD) or other enzymes that catalyze the conversion of ROS into non-reactive oxygen species, such as oxygen or water, risk of oxidative stress may be especially heightened during SARS-CoV-2 infection. This may explain why COVID-19 patients with diabetes and cardiovascular disease have heightened risk for worse clinical outcomes. In a 2014 retrospective report, Umapathi T. et al. note a similar incidence of ischemic stroke (roughly 5%) in patients diagnosed with SARS-CoV-1 infection. Risk of deep vein thrombosis and pulmonary embolism was similarly elevated for patients critically ill with SARS-02. Given that SARS-CoV-1 also gains cellular entry through the ACE2 receptor, a similar mechanism leading to elevated risk of cerebrovascular disease may be at work with SARS-CoV-2.

Central Nervous System Involvement

There are studies in the emerging COVID-19 literature that suggest some advanced cases in the infected Chinese population exhibited potential spread to the nervous system (Li et al., 2020). There is also evidence that with some at-risk individuals viruses, toxins, and other pathogens can be transported from peripheral infection sites (e.g. lungs) to the brainstem causing neurological symptoms. Infection with SARS-CoV-1 has been reported to be associated with similar neurological pathologies. Additionally, ACE2 expression has been observed within some autonomic regions of the brain, consistent with the possibility of SARS-CoV-2 infection in this locale, possibly explaining the breathing pathology of some patients.

Since aerosol transmission via nasal and oral droplets is a primary contributor to infection, and the nasal cavity cells that are targets for the virus are under investigation, a recent study looking at the involvement of immune cells in the nasal transmission of SARS-CoV-2 has shown that T cells interacting with the brain’s innate immune cells, microglia, protect the brain from viral transmission via the nasal route to the brain (Moseman et al., Science Immunol, 2020). However, the investigators point out that one’s genomics may generate risk for mutations that do not always lead to this kind of protection. In addition, the virus’s ability to infect a variety of cells adds to the risk for systemic disease in some involving any target of the olfactory, respiratory, and GI system. Transmission to the brain through the olfactory nerve may be possible. While the pathway has not been definitively shown for SARS-CoV-2, it was demonstrated in an animal model using transgenic mice infected with SARS-CoV-1 (McCray et al. 2007). The transgenic mice expressed human ACE2 receptors, and after infection with the SARS-CoV-1 virus, researchers observed that the infection had spread to the olfactory bulb, which later progressed into the subcortical and cortical regions of the brain. McCray et al. suggest that spread to the brainstem, which is not connected to the olfactory bulb, may occur via specific neurotransmitter pathways or by other means. Neurons express the ACE2 receptor, suggesting that such a pathway may be possible. Moreover, one of the most common neurological symptoms of COVID-19 is anosmia, the loss of smell, suggesting that the involvement of the olfactory bulb as a route of infection of the central nervous system may also be at play as it is in SARS-CoV-1.

Song et al. (2020) successfully demonstrated that SARS-CoV-2 can replicate in two-week old human neural progenitor cells (hNPCs) propagated in cell culture, and this replication is accompanied by an increase in hNPC cell death. To test the potential of SARS-CoV-2 to infect the central nervous system (CNS), the research team created human-induced pluripotent stem-cell-derived brain organoids, as a means of studying a 3-dimensional model of SARS-CoV-2 infection in neurons. They observed infection in nine-week old organoids within 24 hours of infection, and organoid infection was associated with elevated rates of neuronal death. The researchers also studied the effect the virus had on the enrichment of various cellular mechanisms in the cell post-infection. The authors observed that SARS-CoV-2 upregulated pathways related to cell division, organelle division, and various metabolic processes, which included electron transport coupled proton transport, such as electron transfer from cytochrome c to oxygen, and electron transfer from NADH to ubiquinone. This hypermetabolic state may contribute to the ability of the virus to efficiently replicate. The authors also noted the presence of a locally hypoxic environment in regions with close proximity to SARS-CoV-2 infected cells.

Song et al. (2020) were able to inhibit SARS-CoV-2 infection by incubating the organoids with anti-ACE2 blocking monoclonal antibodies before cells received exposure to the virus, demonstrating that ACE2 is a requirement for infection in brain organoids. The team also tested if cerebrospinal fluid isolated from a COVID-19 patient with acute encephalopathy could successfully neutralize the virus in the organoids, as the fluid had detectable levels of IgG antibodies specific to the spike protein. The fluid successfully blocked infection in the organoids; the result pointed to the ability of the CNS to be infected and develop a robust antibody response following infection. Perhaps the most striking result of the study was an in vivo demonstration of SARS-CoV-2 infection in murine neurons overexpressing human ACE2. Using this mouse model, the authors successfully demonstrated that the neuroinvasion by the virus was associated with elevated mortality in the animals.

Concerning CNS involvement in COVID-19, Korainik and Tyler, 2020 (in press) point out that there are post-infection necrotic encephalopathies, vascular changes that can contribute to stroke, and a variety of other neurological disorders that constitute a global health crisis, according to the authors. Furthermore, it is possible that viral infection of the GI tract could contribute to spread to the brainstem, as has been proposed for flu and coronaviruses. Not only could the virus be directly transmitted to the brain, but it may also spread through the CNS by hijacking the connectome, a pathway exploited by a variety of pathogens. These issues have been discussed (Candelario and Steindler, 2014; Candelario et al., 2020) with regard to the role of microvesicle and exosome transmission of viruses and other pathogens. Both mechanisms can preempt the need for direct viral transmission/transport to the CNS; instead the pathogen reaches the brain by concentrating inside exosomes and other microvesicles, from which it is released. Once released, the virus can spread its infectious contents via extracellular and cell-to-cell spread. Knowing these cell and molecular bases for route of entry and system-to-system transmission will help future researchers better design precision therapies that can target particular virus-cell, cell-cell and multisystem interactions. A recent prospective study of patients hospitalized for COVID-19 in New York (Frontera et, Neurology, 2020) found that 13.5% exhibited neurological disorders that appeared to be associated with severity of illness, e.g. hypoxia and vascular and blood abnormalities, rather than direct viral effects on the CNS. This was a prospective, multicenter observational study that demands larger studies in the future in order to confirm this observation. With a history and literature on influenza (e.g. the H5N1 1918 flu) and other viruses (e.g. Zika) showing neurotropism in still to be determined ways, the heterogeneous phenotype of COVID-19, across all age groups as the number of worldwide infections rise, warrant additional studies to determine the precise omically-driven risks for acute and latent CNS risks from SARS-CoV-2 infection.

Cardiovascular Pathophysiology

As many as 1 in 5 COVID-19 patients may experience cardiac injury during their hospital stay (Shi, S. et al., 2020). Many COVID-19 patients have experienced heart failure and cardiac arrest without any sign of severe respiratory symptoms. Factors that may be contributing to increased heart failure may be tied to the inflammation characteristic of moderate to critical COVID-19. The increased systemic inflammation can contribute to the movement of already existing arterial plaques, which can lead to cardiac arrest. Furthermore, systemic inflammation can cause inflammation of the myocardium itself, which can deleteriously impact heart function. It is also possible that the virus can directly infect the heart muscle, since ACE2 receptors are also abundant in the cell membranes present in this tissue. If so, the virus may be contributing to enhanced apoptosis in the heart muscle directly, much as it does to alveolar pneumocytes.

Puntmann et al. (2020) conducted a study on 100 recently recovered COVID-19 adult patients from the University Hospital Frankfurt between April and June 2020 to determine the frequency of cardiac involvement in patients who had recovered from the disease. Subjects underwent cardiac magnetic resonance to assess and determine these effects. The authors report that independent of previous conditions and severity of COVID-19 infection, 78% of subjects showed signs of cardiac involvement and 60% showed ongoing myocardial inflammation.

Immunity and Reinfection

Antibody response to viruses is highly variable over time and is also affected by a variety of factors, such as the severity of the initial infection. Understanding how concentrations of blood-circulating SARS-CoV-2 reactive antibodies vary over time is particularly important for describing the potential for COVID-19 immunity that may be conferred onto recovered patients. If antibody response is not sufficient, reinfection may be possible, whereas a strong antibody response that lasts for an extended period of time may provide longer-term protection against the virus. Moreover, understanding the SARS-CoV-2 epitope can provide greater insight into vaccine development and design; in particular, more highly conserved regions of the epitope may be useful to target in such efforts as a means of conferring some immunity to future emergent coronaviruses. Understanding the adaptive immune response is also highly relevant, as prior infections with endemic coronaviruses may offer some protection against severe SARS-CoV-2 infection. Le Bert and collaborators (Nature, 2020) have shown that some uninfected individuals possess memory T cells with the potential to recognize SARS-CoV-2. This is suggested to arise from recognition of protein fragments from previous exposures to common cold viruses. One area of intensive investigation is understanding the cell and molecular contributions to initial and long term immunity; should not be in introduction to this section.... needs to be moved and elaborated ontoward that cause, we are beginning to better understand distinctive myeloid signatures as described by Mann et al.. (2020) who showed, “...shifts in neutrophil to T cell ratio, elevated serum IL-6, MCP-1 and IP-10, and most strikingly, modulation of CD14+ monocyte phenotype and function..”

SARS-CoV-2 Epitope

Characterizing the protein structure of the SARS-CoV-2 epitope, the region of the virus that binds to an antibody, may provide further insight into the development of an effective vaccine. Of particular interest are vulnerable regions that neutralizing antibodies, antibodies that can deactivate the pathogenicity of the virus, can target. Because the receptor binding domain (RBD), located on the S1 domain of the spike glycoprotein, binds strongly to the ACE2 receptor, the host cellular entrypoint of the virion, the RBD and neighboring regions serve as promising targets for neutralizing antibodies. Meanwhile, the fusion peptide, located on the S2 domain of the spike glycoprotein, near the N-terminus, is another promising target, since its exposure is necessary for fusion to the host cell membrane to occur. For this reason, epitopes of the spike glycoprotein, where the RBD and fusion peptide are both located, have been studied intensively. Nevertheless, the spike protein may not be the most ideal target for vaccine design since the protein is not as well conserved as other protein components of the virion, such as the nucleocapsid protein (N protein). Moreover, the N protein is also highly immunogenic, and so the identification of its epitopes warrants further study.

CR3022 is a human monoclonal antibody that is specific to the receptor binding domain of SARS-CoV-1. In February 2020, Tian et al. published results showing CR3022 was able to bind with high affinity to a portion of the SARS-CoV2 RBD outside of the ACE2 binding region of the RBD. Yuan et al. (2020) determined the crystal structure of the SARS-CoV-2 RBD in complex with the CR3022 antibody and found that the SARS-CoV-2 epitope (the region of the RBD that was buried by CR3022) consisted of 28 amino acids, and most intermolecular attraction resulted from hydrophobic interactions between the epitope and antibody. Furthermore, 24 residues of this 28 amino acid sequence were shared between SARS-CoV-1 and SARS-CoV-2. This structural similarity likely accounts for the ability of the antibody to bind to the SARS-CoV-2 RBD, although it does so with significantly less affinity than it does to SARS-CoV-1 RBD (Contrast the dissociation constant with SARS-CoV-2, which was 115 nM, to the lower constant with SARS-CoV-1, which was 1 nM).

In March 2020, Zheng, M. and Song, L. published results concerning the Spike protein epitopes of MERS-CoV, SARS-CoV-1, and SARS-CoV-2. The authors note that the three proteins had differing degrees of antigenicity, with MERS-CoV spike protein showing the greatest ability to elicit antibody response, followed by the SARS-CoV-2 spike protein, which was then followed by the SARS-CoV-1 spike protein. While 75.5% of the amino acid sequence of SARS-CoV-2 spike protein is conserved with SARS-CoV-1, the vast majority of the sites on the SARS-CoV-2 spike protein that produced the greatest antigenic response were in the non-conserved regions. More specifically, the non-conserved epitopes made up 85.3% of all SARS-CoV-2 spike epitopes and 85.7% of RBD epitopes.

Poh et al. (2020) identified two distinct peptides sequences of the spike glycoprotein that were recognized by IgG antibodies in convalescent COVID-19 blood sera. Using blood sera collected from six convalescent COVID-19 patients and five recovered SARS-02 patients, the group tested the immunogenicity of various peptide sequences from the SARS-CoV-2 and SARS-CoV-1 spike proteins in both groups. Two peptide sequences, one in close proximity to the SARS-CoV-2 RBD and another that overlapped with the fusion peptide sequence, were associated with a strong IgG response in the blood sera of COVID-19 patients but not of SARS-02 patients. Furthermore, the group confirmed that sera depleted of antibodies targeting these two regions had a significantly reduced ability to neutralize SARS-CoV-2 pseudovirus infection, suggesting that antibodies targeting these two epitopes are responsible for the majority of the anti-spike-neutralizing response. In particular, two of the COVID-19 patients’ blood sera contained antibodies that responded to a SARS-CoV-1 peptide that contains the SARS-CoV-1 fusion peptide, a region more conserved among coronaviruses. The finding suggests that this epitope containing the fusion peptide may function as a broader antibody target for various coronaviruses.

A July, 2020 publication in Nature reported on the isolation of 19 potently SARS-CoV-2 neutralizing monoclonal antibodies from 5 patients affected with severe COVID-19 (Liu, L., et al.). Of these 19, nine were able to effectively inhibit 50% of the virus in vitro at very low concentrations, between 0.7 and 9 ng/mL. The epitopes of the 19 neutralizing antibodies were then studied. About half of the antibodies targeted the RBD of the spike protein, and the other half targeted the N-terminal domain. Using cryo-electron microscopy, the researchers also found that two of the antibodies had quaternary epitopes found at the site of the three RBDs of the spike trimer, specifically when all the RBDs were in the “down” conformation. These antibodies likely neutralized the virus by locking the RBDs of the spike trimer in the inactive down confirmation, rendering the virion unable to effectively bind to the ACE2 receptor.

The Kinetics of SARS-CoV-2 Specific Antibodies and Memory Immune Cells

SARS-CoV-2 neutralizing antibodies show some binding affinity to the S1 Protein of SARS-CoV-1, which is enhanced when binding to the S1, RBD, and S2 regions of the SARS-CoV-2 protein (Wu, F. et al., 2020). While there is limited binding to SARS-CoV-1 proteins, the SARS-CoV-2 antibodies cannot inhibit SARS-CoV-1 infection and have only been confirmed to inhibit SARS-CoV-2 infection. A group of 175 COVID-19 patients with mild disease were tested for the presence of SARS-CoV-2 antibodies throughout their stay at the Shanghai Public Health Clinical Center. Wu, F. et al. report that all patients showed low levels of SARS-CoV-2 neutralizing antibodies before Day 10 of infection. Between Days 10 and 15, antibody levels steadily rose until they leveled off around Day 15. After discharge from the clinic, patients received follow-up antibody tests and antibody levels did not significantly differ from levels at the time of discharge. Titers of neutralizing antibodies were most strongly correlated with S2 and RBD levels. Researchers also observed that SARS-CoV-2 neutralizing antibodies concentrations varied significantly between patients. In 30% of the patients studied, levels of antibody titer were below the detection limit, demonstrating that a significant proportion of recovered patients may not develop high levels of antibodies. Furthermore, middle-aged and older patients (aged 40-59 and 60-85 years, respectively) showed significantly higher levels of SARS-CoV-2 specific antibodies than younger patients (aged 15-39 years). SARS-CoV-2 neutralizing antibodies titers were also negatively correlated with blood lymphocyte count and positively correlated with C-reactive protein levels.

Figure 2.20: Antibody Titer Kinetics as a function of COVID-19 Severity (Adapted from Okba et al., 2020)


Okba et al. (2020) developed serological assays for detecting antibodies specific to a variety of SARS-CoV-2 proteins and protein domains. Figure 2.20 illustrates the antibody titers detected in serum collected from two patients with mild COVID-19 (green and black) and from one patient with severe COVID-19 (red) on different days after the onset of symptoms. Detectable antibody titer levels are indicated by the horizontal dotted line. Antibody response was tracked for spike protein (A), the S1 subunit of the spike protein (B), the N-terminal domain of S1 (C), the receptor binding domain (D), the SARS-CoV-1 N protein (E), and the presence of SARS-CoV-2 neutralizing antibodies was tracked over time (F) as well. Detectable levels of antibodies specific to the spike protein, the S1 domain of the spike protein, the receptor binding domain, and the SARS-CoV-1 N protein, which is 90% similar to the SARS-CoV-2 N protein, were found in all patients by day 21 after symptom onset. All three patients also developed SARS-CoV-2 neutralizing antibodies.

A review of over 1,281 scientific articles concerning antibody mediated immune response to coronaviruses revealed numerous key findings concerning the kinetics of antibody protection and the correlation between severity of disease and antibody production for coronaviruses that affect humans. The median time to detection of antibodies was 12 days for SARS-CoV-1, 11 days for SARS-CoV-2, and 16 days for MERS-CoV (Huang, A. et al., 2020). IgG antibodies were found to decline over time and were typically detectable for at least one year but up to three years after initial infection. Furthermore, increased symptom severity was associated with a longer duration of detectable antibodies. Humans could be re-infected with a human coronavirus one year after onset of symptoms, but generally the severity of the symptoms were significantly reduced (ibid.). Also, SARS-CoV-1 and MERS-CoV have demonstrated the ability to induce an antibody response from antibodies produced after prior endemic human coronavirus infections, such as HCoV-229E. Human coronavirus seroprevalence rises sharply during childhood and remains steady throughout adulthood.

Long et al. (2020) tracked 285 hospitalized COVID-19 patients and tested them for the presence of IgG and IgM antibodies on different days after the onset of symptoms. The researchers report that 100% of tested patients had detectable IgG antibodies specific to SARS-CoV-2 at around 17-19 days after symptom onset, and this number remained steady the following week of testing (Figure 2.21). The percentage of patients testing positive for IgM antibodies reached a maximum of 94.4% at 20-22 days after symptom onset. This percentage began to slowly decline after Week 3, while the percentage of patients testing positive for IgG antibodies after Week 3 remained steady. The median time after initiation of symptoms to detection of IgG and IgM antibodies in all patients was 13 days. As found in previous studies, IgM and IgG titers were higher in patients with more severe disease, but the differences were not statistically significant except for the IgG titers in patients after two weeks of symptom onset. For 26 patients tested every three days, Long et al. report that IgG titers leveled off for an average of 6 days after the first positive measurement. For these 26 patients, nine first tested positive for both antibodies on the same day, seven patients tested positive for IgM antibodies first and ten patients tested positive for IgG antibodies first.

Long et al. (2020), a team composed of many of the same researchers that conducted the previous study, conducted a clinical and immunological assessment of 37 asymptomatic individuals who were confirmed to have SARS-CoV-2 infections by RT-PCR. The asymptomatic group, which comprised roughly 20% of the patients assessed in the study, was compared to a larger group of 141 symptomatic COVID-19 patients. The asymptomatic group had a significantly longer period of viral shedding with a median duration of 19 days (IQR of 15-26 days), compared to a median duration of 14 days in patients experiencing mild symptoms. While the period of viral shedding was longer in asymptomatic individuals, the authors note the finding should not be interpreted to suggest these patients were any more contagious, as contagiousness is associated with various other contributing factors. Despite asymptomatic participants experiencing no characteristic symptoms, abnormal radiological findings specific to one lung were still found in two-thirds of all asymptomatic individuals, while one-third of the group showed abnormalities in both lungs. Symptomatic individuals showed higher overall levels of 18 pro- and anti-inflammatory cytokines, demonstrating that a reduced inflammatory response was associated with asymptomatic disease progression.

Immunological testing revealed further distinguishing characteristics between the two groups but also a common trend of a steady decline in antibody titers in the early months of recovery. In the early acute phases of disease progression, IgG levels in the symptomatic group were significantly higher, although rates of their detection were roughly similar between the two groups. However, rates of IgM detection were approximately 10% lower in asymptomatic patients tested 3-4 weeks after exposure. Patients in both groups were tested for antibody levels in the early convalescent phase, defined as 8 weeks after hospital discharge. While the symptomatic group continued to show significantly higher levels of IgG levels at this stage, both symptomatic and asymptomatic patients experienced a substantial decline in detectable IgG levels. The median percentage of IgG decline was 71.1% for the asymptomatic group and 76.2% in the symptomatic group. For neutralizing serum antibody levels, a decline in levels was detected in 81.1% of all asymptomatic patients tested and in 62.2% of the symptomatic individuals tested. For the asymptomatic patients showing a decline in levels, the median percentage decrease was 8.3%, compared to 11.3% for the corresponding group of symptomatic patients. Moreover, in this early convalescent stage, 40% of asymptomatic individuals became completely seronegative for IgG, as did 12.9% of symptomatic individuals tested. The result is striking, as previous studies have revealed that sustained IgG levels are maintained for at least one year after SARS-CoV-1 infection and may last well over two years for many individuals. The study of SARS-CoV-2 antibody prevalence during convalescence should continue to be prioritized, as these results have urgent consequences that affect potential reinfection rates.

Figure 2.21: IgM and IgG Prevalence in COVID-19 Patients as a function of Days from Symptom Onset (Adapted from Long et al., 2020)


Seow et al. (2020) corroborated these results, demonstrating both a positive correlation between neutralizing antibody response and disease severity and a steady decline in antibody titers that begins after a peak in serum antibody titers is reached, estimated at around 5 weeks after the onset of symptoms. A total of 65 individuals with COVID-19, including 59 hospitalized patients and 6 healthcare workers, were tested for antibody response at specific times over the course of 94 days following the onset of symptoms. Figure 2.22 illustrates how disease severity (higher severity score is associated with a more severe clinical course) is a determinant of serum-neutralizing antibody concentrations in the initial days following the onset of symptoms, where higher disease severity is associated with elevated antibody response. However, regardless of disease severity, declines in antibody response were detected in patients tested after 40 days from the onset of symptoms. Moreover, some patients with low severity scores reached undetectable levels of neutralizing antibodies during the latter portion of the study. Similar results were observed in the participants’ blood sera for IgM, IgA, and IgG antibodies specific to the SARS-CoV-2 spike protein, nucleocapsid protein, and receptor binding domain.

Figure 2.22: Neutralizing Antibody Levels vs. Time after Onset of Symptoms (Adapted from Seow et al., 2020)


With in silico studies, machine learning, and a focus on the potential rapid repurposing of existing drugs to treat as well as prevent COVID-19, some new insights have been gained on a most important systems biological understanding of distinct immunity (e.g. Low plasma levels of IFN-alpha during infection) and other elements of disease severity (Arunachalam et al., 2020). Initial studies of antibody kinetics of the mildly symptomatic and whether modest antibody response can affect protection against future infections have commenced (Rijkers et al., 2020). One study comparing antibody response in serum (439) and saliva (128 patients) from convalescent patients 3-115 days post-symptom onset (Isho B et al. Science Immunol, 2020). Even though this is a relatively small patient study, the investigators found the presence of IgG responses up to three months. Thus, IgG responses may serve as a good surrogate measure of systemic immunity to SARS-CoV-2, supporting a notion from other studies of antibody persistence and decay (Iyer, A. S. et al., Science Immun, 2020) where circulating RBD antibodies showed to be good biomarkers of previous and recent infection for, again, at least up to three months.

Crotty et al. (2020) analyzed longer-term kinetics of SARS-CoV-2 immunity in 185 COVID-19 patients between the ages of 19 and 81 from the United States. They represented a wide range of disease severity and were recruited from multiple sites in the U.S. Only 7% of the patients had been hospitalized, and 97% of the patients experienced a symptomatic version of the disease. Of these 185 subjects, 147 provided a single blood sample between 6 and 240 days post-symptom onset, of which 41 provided blood samples after 6 months post-symptoms onset. The remaining 38 subjects provided samples over a duration of several months. The researchers found that spike IgG antibodies were stable over 6+ months, with an estimated half life of 140 days post-symptom onset and a 95% confidence interval of 89-329 days. Spike IgG titers were heterogeneous among subjects, with a wide range and a median of 575, a finding that corroborated results of previous studies. The best fit curve for decay of antibody tiers was a linear decay model, which the authors attributed possibly to the titer heterogeneity among subjects. The nucleocapsid IgG antibody titers had a half life of approximately 67 days, with a 95% confidence interval of 49-105 days. Furthermore, 98% of subjects were seropositive for IgG spike at 1 month post-symptom onset and 90% remained seropositive between 6-8 months post-symptom onset. The estimated half life for RBD specific IgG antibodies was 83 days, with a 95% confidence interval of 68-123 days. 90% of subjects were seropositive for SARS-CoV-2 neutralizing antibodies 6-8 months post-symptom onset.

In addition to measuring the kinetics of IgG antibody decay in COVID-19 patients, Crotty et al. analyzed the levels of spike-specific memory B cells, as well as SARS-CoV-2 CD4+ T helper cells and CD8+ T killer cells over time. SARS-CoV-2 specific memory B cells increased over the first 150 days post-symptoms onset and then flattened. RBD-specific B memory cells were present as early as 16 days post-symptom onset and steadily increased over 4-5 months. Nucleocapsid-specific B cells exhibited a similar behavior. The researchers also identified SARS-CoV-2 CD8+ memory T cells in 155 subjects and found that these cells recognized spike protein, membrane protein, nucleocapsid protein, and ORF3a. 61% of participants had circulating levels of these cells within one month post-symptom onset. This level had dropped to 50% by 6 months post-symptom onset. The half-life of SARS-CoV-2 CD8+ cells was 166 days for the subjects in the study. The most common proteins recognized by SARS-CoV-2 CD4+ cells in the subjects were the spike protein, the membrane protein, the nucleocapsid protein, ORF3a, and nsp3. Approximately 35% of the subjects at one month post-symptom onset had greater than 1% SARS-CoV-2 specific CD4+ cells, showing a robust response early on. The levels of these cells steadily declined over 6 months, with a half life of 166 days post-symptoms onset. The percentage of subjects with SARS-CoV-2 specific CD4+ cells at 1 month post-symptom onset was 94%, which declined to 89% at 6 months post-symptom onset. Overall, these results show robust immune memory responses even at prolonged periods following infection.

Reinfection

On February 14, 2020, Taiwan News reported that a doctor from Hubei Province, who wished to retain anonymity, claimed that it is possible for patients to become reinfected with SARS-CoV-2. The source claimed that various patients would recover and would then have a more precipitous decline upon reinfection because the medication they had been treated with had damaged heart tissue. The doctor also suggested that antibodies may have enhanced the severity of the second reinfection (implying that antibody-dependent enhancement may have been at work). As of March 20, 2020, there were no credible sources or peer-reviewed research showing that these reports were, in fact, true. It is important to note that the sensitivity of RT-PCR for detecting viral shedding of SARS-CoV-2 is fairly low, reportedly as low as 59% (Ai et al., 2020). Sensitivity is a measurement of the proportion of all people who have the disease that will test positive on a diagnostic test. Thus, with a test with 59% sensitivity, 41% of people who do have the virus will test negative (while only 59% will test accurately as positive). With a diagnostic test with such low sensitivity, it is not uncommon for infected individuals being tested to get strings of positives, negatives, followed by positives again. This may at least partially explain the so-called “reinfections” that were reported by this news source.

However, reinfection with a virus is actually possible during a very brief window after initial infection occurs, though it is rare. Antibody protection begins after exposure to an antigen, often shortly after recovery from illness spurred on by some pathogen. The initial antibody produced by the spleen is IgM (Immunoglobulin M), which is the largest antibody produced, limiting its abundance in blood serum. IgM is the first line of immunity for patients and is present in the lymph and blood. Guo et al. (2020) report that for 81 COVID-19 patients studied, the median duration of IgM detection was 5.1 days. IgM production begins to steadily decrease after the first weeks of its production, dropping to undetectable levels as soon as two months after initial infection. For this reason, immunoassays which detect IgM in serological tests indicate that the patient was recently exposed to an antigen, perhaps within the last few days or months. IgG is a much smaller antibody that is produced in plasma B-lymphocyte cells. It is the most abundant antibody in the lymph and bloodstream and is responsible for longer-term immunity. Guo et al. (2020) report in their study of 81 COVID-19 patients that the median time until detection of IgG antibodies was 14 days after initiation of symptoms, although there are no long-term immunity data yet available. For SARS-CoV-1 infection, IgG titers reach their maximum levels at 4 months after infection and taper off for as long as three years after infection. Because its production is delayed, it is possible that reinfection can occur when a patient is re-exposed to the virus during the brief window between the drop in IgM production and the initiation of IgG production.

On March 19, 2020, a research team led by Qin and Bao published results of a study, which has not yet been peer-reviewed, supporting that rhesus macaques were not able to become reinfected with SARS-CoV-2 after initial infection with the coronavirus. Researchers infected four rhesus macaques, an old world monkey that is close both genetically and physiologically to humans, with SARS-CoV-2. The animals showed symptoms that included weight loss and moderate interstitial pneumonia. The monkeys were tested for viral shedding by Reverse Transcription Polymerase Chain Reaction (RT-PCR). Samples were taken using nasal/pharyngeal swabs and anal swabs, and researchers found that the total viral load reached their highest levels in all four animals at three days post-infection. Also of note, viral replication was detected in the nose, pharynx, lung, gut, spinal cord, heart, skeletal muscle, and bladder. After 14, 21, and 28 days post-infection, the monkeys were tested for antibodies, and researchers found increasing production of antibodies during this period. At 28 days after infection, the animals were tested for viral load, and no detectable virus was found in any of the remaining animals (their chest X-rays were also normal by this point). On this day, the monkeys were reinfected with the same strain of the virus, and besides a very brief spike in temperature, none of the monkeys demonstrated the symptoms observed during primary infection. Five days after reinfection, the animals were tested for viral shedding using RT-PCR 96 times, and all results came back negative for the virus. One animal was sacrificed and further studied; no viral replication was observed in any tissue layer, and no pathological damage was found in the lung tissue. The data suggests that the animals developed immunity to this specific strain of the virus, which protected them from reinfection.

Chandrashekar et al. (2020) corroborated these results in a study performed on nine rhesus macaques that were inoculated with a range of titers of SARS-CoV-2. Viral RNA was detected in both bronchoalveolar lavage (BAL) and nasal swabs. In these animals, viral load peaked on Day 2 and was undetectable in BAL by Day 10-14 and in nasal swabs by Day 21-28. All animals developed an antibody response specific to the SARS-CoV-2 spike protein and all developed SARS-CoV-2 neutralizing antibodies. Thirty-five days after initial infection, the animals were inoculated again with the same dose of the virus that they had initially received. These animals showed a significantly reduced level (a 5-log10 reduction) in median viral titer in BAL and in nasal swab samples, suggesting that the animals had developed a robust antibody response that strongly inhibited secondary infection. Deng et al. (2020) have shown a similar primary exposure protection against reinfection in rhesus macaques.

On April 16, 2020, the Korean Centers for Disease Control and Prevention (KCDC) announced that 141 people who had previously tested positive, then been cleared of the virus, had tested positive again. By May 6, this number had grown to 350 suspected reinfections. At a press conference the following day, Oh (Myoung-Don), a South Korean infectious disease expert who leads the central committee for emerging disease control, said the RT-PCR tests were detecting ribonucleic acid of the inactivated virus. The nucleic acid, which is amplified by RT-PCR, can remain in the body for months after the active infection has subsided, and because RNA from the active or inactive forms of the virus are indistinguishable in an RT-PCR test, the reports of reinfection likely resulted from false positives. The KCDC published a press release on May 19, 2020 summarizing results stemming from the epidemiological contact investigation of a subset of 285 individuals from the 447 suspected cases of reinfection that had amassed by May 15, 2020. A total of 790 contacts of the 285 investigated individuals were contacted and monitored for a 14-day period, and none had reported a newly confirmed case from exposure during the potential re-infection period alone. Furthermore, neutralizing antibody protection was detected in all 285 suspected reinfections. Also of note was that 108 of the 285 individuals were tested for virus isolation in cell culture, and none of these laboratory results came back positive. This suggests that the original RT-PCR tests that detected a possible reinfection were instead detecting RNA from the inactivated virus, as Oh (Myoung-Don) had indicated over a week before.

It is still unknown whether the virus can be reactivated or if reinfection is possible by a secondary source. If the virus is indeed capable of reactivation, it is not known under what conditions (external and internal stimuli, humidity, temperature) this can occur. Furthermore, it remains to be understood whether the dormant or active form of SARS-CoV-2 can damage the human body over a given period of time.

Pre-Existing Immunity

Grifoni et al. (2020) found evidence of T-cell reactivity with epitopes of SARS-CoV-2 in individuals with no previous exposure to SARS-CoV-2. The researchers collected 20 blood samples from donors between 2015 and 2018, over a year preceding the emergence of SARS-CoV-2. The group tested the reactivity of the samples’ CD4+ T cells with spike and non-spike specific epitopes and found that CD4+ T cell response was detectable in both cases for some of the samples. Moreover, in over 50% of the samples tested, a non-spike specific CD4+ T cells response was above the limit of detection. While the mechanism driving this response is largely unknown, the authors hypothesized that exposure to endemic respiratory “common cold” coronaviruses, such as HCoV-OC43, for example, may confer some adaptive T-cell immunity. However, it is important to note that concentrations of spike RBD specific IgM, IgG, and IgA antibodies in these samples were significantly lower than in recovered COVID-19 patients (p < 0.0001).

Mateus et al. (2020) identified a total of 142 T-cell SARS-CoV-2 epitopes, 66 of which were from the spike protein. The research team used blood samples collected from 18 donors between the years 2015 and 2018, well before SARS-CoV-2 was established as a zoonotic virus and worldwide pandemic; donors were still tested and confirmed to all be seronegative for SARS-CoV-2. The donors’ peripheral blood mononuclear cells (PBMCs) were tested for activity in the presence of a wide array of peptide sequences isolated from SARS-CoV-2. The researchers found that CD4+ T cells made up the vast majority of cells responding to SARS-CoV-2 peptide stimulation, representing 93.2% of such activity identified in the sampled PBMCs. In 4.5% of such cases, CD8+ T cells were identified as the active cell type, and in 2.3% of cases, both cell types were identified. Thus, the SARS-CoV-2 T-cell epitopes primarily stimulate CD4+ T cell response, a result established by previous studies. Moreover, each donor’s PBMCs recognized an average of 11.4 epitopes, and 40 of the 142 epitopes were recognized by more than two donors. After confirming that almost all 18 donors were seropositive for three widely circulating human coronaviruses (HCoV-NL63, HCoV-OC42, and HCoV-HKU1), the team further investigated the possibility for reactivity to SARS-CoV-2 in these unexposed individuals to be attributable to cross-reactivity for widely circulating endemic coronaviruses to which donors may have been exposed. Such epitope cross-reactivity was confirmed in 24% of the T-cell lines, with a stronger antigenic response to the other human coronavirus peptide sequences. The finding confirms that some adaptive T-cell immunity may be acquired from previous exposure to other endemic human coronaviruses.

There is a great deal of interest in the relationship of disease phenotype to age. Even though we do not have enough information to understand why children are reportedly less vulnerable to COVID-19 than adults, and especially older adults, it is possible that children’s immune systems may harbor cell and molecular agents that might help prevent infection as well as contribute to a milder form of disease (except for the case of Kawasaki-like disease described in to date a relatively small number of children). Because their innate and adaptive immune responses are more immature, they may contribute less to cytokine storms and other immune responses that may contribute to ARDS. Toward that end, Pierce et al. (2020) compared hospitalized adult and young patients with COVID-19 to determine any differences in immune system responses that may contribute to the differential in disease severity between the two groups. They found that circulating IL-17A and IFN-γ levels during the first week of hospitalization were more elevated in younger patients but that three weeks later, circulating CD4+ were substantially higher in older patients. Neutralizing antibody titers were positively correlated with age. These data support that adults have a robust adaptive immune response, and so poor clinical outcomes of adult patients are not likely tied to adaptive immunity.

Herd Immunity

Herd immunity for a given population occurs when a large enough proportion of individuals in that population are no longer susceptible to an infectious disease, perhaps through vaccination or conferred immunity from a previous infection, thereby protecting the susceptible individuals within the population from infection. More precisely, herd immunity is reached when the proportion of non-susceptible individuals in a given population rises above the herd immunity threshold (HIT). At the HIT level, the effective reproduction number for an infectious disease, R, is equal to 1, meaning that one case of infection should generate on average only one secondary case within that population, a scenario known as the endemic steady state. As soon as the proportion of non-susceptible individuals rises above this level, R falls below 1, and the number of cases begins to decline.

In the simplest model, where it is assumed that everyone in a population mixes homogeneously and each individual is equally susceptible (when susceptible) and equally contagious (when contagious), and no control measure to contain the virus has been implemented, the value of R is dependent on only two values: the proportion of individuals in a population that are susceptible to the infection, S, and the basic reproduction number for a disease, R0 (see Basic Reproduction Number). In this scenario, R = R0S, and if we let p be the proportion of individuals in the population that are immune (i.e. not susceptible), then R = R0(1 - p). Then letting pc = the critical proportion of immune individuals in a population where the herd immunity threshold is reached, 1 = R0(1 - pc), and pc = 1 - 1/R0. When p > pc , the condition for herd immunity is met. Thus, for more infectious diseases with higher R0 values, the critical proportion of immune individuals required to reach herd immunity threshold will increase.

However, the above model is overly simplified, as it does not take into account a variety of factors which vary widely across the world, even on regional or local levels. Such factors include the implementation of non-pharmaceutical control measures to contain the spread of infection, such as the use of masks and mandatory lockdowns or quarantining. If control measures are included in the model, then letting q be the relative reduction in transmission rates due to such non-pharmaceutical control measures, and again, assuming homogeneous population mixing and equal rates of susceptibility and contagiousness, R = R0(1 - p)(1 - q), and in this instance, pc = 1- 1/(R0(1 - q)). As better control measures for containing the virus are implemented, q will increase, and pc will thereby decrease. However, as better control measures are implemented, it becomes more difficult to attain herd immunity, as more individuals in the population remain susceptible, and in the absence of a vaccine, herd immunity may not be achievable.

Effect of Population Heterogeneity

A variety of other factors can further affect the effective reproduction number, such as the variability of individual contact stemming from the age groups of the individual, or the variability of susceptibility and contagiousness within a population. Using an estimate of R0 = 2.5, Britton et al. (2020) used an age-stratified population model, where a population was divided into six age cohorts with heterogenous mixing assumed between any two cohorts, to predict the necessary pc required to reach COVID-19 herd immunity for a given population. The authors fit contact rates between groups to empirical data collected from a contact study. The average number of infectious contacts that an infected individual had in any one age cohort with another cohort was dependent both on contact rates for the two cohorts but also on the infectivity of the infected individual’s cohort and the susceptibility of the other age cohort. The model also used network models to account for variation in individual social activity levels within a particular age cohort. Assuming a small fraction of infected individuals on February 15 and the implementation of preventative measures one month later while the infected fraction was still small, Britton et al. used a scale factor, α, to scale down the subsequent generations of cases until June 30, when the scale factor was set back to 1, modeling the effect of a complete relaxation of preventative measures. Using these assumptions, the authors illustrated the possibility of reducing a herd immunity level down to ~43%, considerably lower than the 60% predicted by the simplest model that assumes homogeneous population mixing and uniform susceptibility/infectivity rates, as well as the implementation of no preventative measures.

The authors note that this low value can be attained because the majority of immune individuals will be in younger age groups, which are assumed to contain people that are more socially active and less vulnerable to the disease. Super-spreaders with more contacts will thus get infected early on, transmitting the infection amongst each other and becoming immune, and as this happens, the susceptible population will diminish rapidly, considerably slowing the spread of the disease after this stage. The model is only meant to be illustrative that pc can be further reduced from the simplest model assuming homogeneous mixing and should not be taken as an estimate. The model of course has several short-comings, as it assumes that superspreaders are concentrated within the same set of individuals, and it does not consider the impact of super-spreading events, nor does it consider spatial heterogeneities, which may also have substantial impact on herd immunity thresholds. Figure 2.23 illustrates the predicted cumulative fractions of the population infected as a function of days after February 15 for four different levels of α, each corresponding to different levels of preventive measures taken during the March 15-June 30, 2020 period. Note that only when preventative measures are too strict (when α is lowest, as seen in the purple curve), a strong second surge is predicted once restrictions are lifted. However, some degree of preventative measures show a considerable reduction in herd immunity threshold (as seen in the yellow curve where α = 0.6) with no observable strong second surge.

Figure 2.23: Cumulative Fraction of Infected Individuals Predicted in a Model as a Function of Days after February 15 for Different Levels of Preventive Measures Taken when R0 = 2.5 (Adapted from Britton et al., 2020)


Factors Concerning Severity

A striking feature of COVID-19 is that its symptomatic manifestation and progression is incredibly variable between patients. Soon after the initial emergence of the disease, a variety of factors were identified as potential risk factors for greater severity, including increased age and pre-existing conditions such as cardiovascular disease or diabetes. Meta-analyses of large groups of patients have been incredibly helpful in identifying and quantifying such risk factors. Machine learning models (Shrock et al., 2020) will become more important for predicting disease severity in different patient populations as we better understand how differences in viral spike proteins, through viral epitope profiling, along with characterizing distinct patient antibody responses, together contribute to distinct disease phenotypes. We outline below some of the most significant of these results. Further research has also unveiled a wide range of molecular factors that may be associated with heightened disease severity, such as elevated levels of circulating C-reactive protein and Interleukin-6. Moreover, certain molecular markers have been linked to specific clinical progressions of the disease, including elevated D-dimer in patients experiencing coagulopathy. Identifying these molecular markers will assist medical professions in delivering the most relevant and personalized treatment course that optimizes patient survival. Finally, the identification of genomic variants that may contribute to heightened disease severity has also uncovered certain populations with heightened risk. An understanding of the associations these variations have with clinical disease progression will also aid in more personalized and effective treatments.

Age, Sex, Pre-Existing Conditions, and Clinical Course

The severity of COVID-19 infection appears to depend on a number of factors. Substantial data appears to indicate a direct proportionality of severity with age, with the most severe and critical cases occurring in the 65+ population. As of writing, there have been very few reported deaths in patients aged 0-24, and the infection rates of this population are the lowest for all other age groups. Preliminary data from Wuhan, China showed that out of 45,000 diagnosed cases, less than 1% were under 9 years of age. Furthermore, just over 1% of cases were patients aged 10-19. Tables 2.10a-c compare U.S. reported and coded deaths resulting from seasonal flu (J09-J11), COVID-19 (U07.1), pneumonia (J12.0-J18.9), and a comorbity of the latter two from February 1, 2020 to May 9, 2020 by sex, age, and state, respectively. These data do not represent the total number of reported deaths from these diseases in this period.

Table 2.10a: U.S. Reported and Coded Deaths of the Seasonal Flu, COVID-19, Pneumonia, and all Causes by Sex (May 9, 2020)
Sex
Seasonal Flu
Pneumonia + COVID-19
COVID-19
Pneumonia
All Causes
Male
3,135
13,636
30,387
42,098
431,692
Female
2,975
10,510
24,473
36,167
403,884
Unknown
0
1
1
1
31
6,110
24,147
54,861
78,266
835,607


Pre-existing conditions, such as hypertension, diabetes, cardiovascular disease, chronic respiratory disease, and cancer also elevate mortality risk. Roughly 10.5% percent of affected patients with cardiovascular disease, 7.3% of patients with diabetes, 6.3% percent of patients with chronic respiratory illness, 6.0% with hypertension, and 5.6% with cancer succumb from a critical COVID-19 infection (American College of Cardiology Bulletin, 2020). For individuals with Type 1 or Type 2 diabetes, elevated blood sugar level is associated with diabetic complications, as well as weakened immunity and increased viral-induced inflammation. Consequently, diabetics with A1C levels between 6 and 7 are only considered to be at slightly elevated risk of developing COVID-19 severe complications over the general population, while increased A1C levels are associated with increased risk of COVID-19 severity. Blood sugar management should be prioritized during this time to lower the risk of severe COVID-19 complications. Smoking is also associated with elevated risk. There is no current data to suggest that pregnant women are at an increased risk of contracting SARS-CoV-2; however, since pregnant women are more susceptible to other respiratory infections, they are considered at-risk for COVID-19.

Table 2.10b: U.S. Recorded and Coded Deaths of the Seasonal Flu, COVID-19, Pneumonia, and all Causes by Age Group (May 9, 2020)
Age Group
Seasonal Flu
Pneumonia + COVID-19
COVID-19
Pneumonia
All Causes
<1
11
1
4
37
4,258
1-4
35
2
2
33
832
5-14
43
0
6
41
1,223
15-24
42
21
59
157
7,452
25-34
135
170
388
556
15,975
35-44
214
387
973
1,240
22,887
45-54
529
1,154
2,772
3,310
44,291
55-64
1,136
2,996
6,725
9,537
106,004
65-74
1,334
5,155
11,524
16,378
164,217
75-84
1,357
6,788
14,930
21,649
206,121
>84
1,274
7,473
17,478
25,328
262,347
6,110
24,147
54,861
78,266
835,607


Zhu, L. et al. (2020) conducted a retrospective analysis of 7,337 hospitalized COVID-19 patients from Hubei, China, of whom 952 had pre-existing type 2 diabetes. Their analysis found that patients with type 2 diabetes experienced a significantly higher case fatality rate (7.8%) when compared to patients without the disease (2.7%). Furthermore, having type 2 diabetes was associated with higher risk of multiple organ injury and with an elevated need for medical intervention. However, type 2 diabetic patients with well controlled blood glucose, where glycemic variability ranged between 3.9 and 10.0 mmol/L, was associated with a lower case fatality rate (1.1%) when compared to patients with poorly controlled blood glucose (11%). Patients with well-controlled blood glucose also needed less medical intervention and experienced less lymphopenia and had lower levels of D-dimer and C-reactive protein when compared to their poorly controlled blood glucose counterparts.

Though multiple death rates for different age groups have received intense media coverage, it is not apparent how weakened immune systems due to other illnesses among victims may have been factored into the cause of deaths. Similarly, if more than one serious health condition was at work among victims, it is not known how those causes of deaths were recorded thereby impacting analysis.

Table 2.10c: COVID-19 Hospitalization Ratio Rate Factor by Age, Race, and Ethnicity (March 1, 2020 - September 19, 2020, CDC)
Age \ Race & Ethnicity
Non-Hispanic American Indian or Alaska Native
Non-Hispanic Black
Hispanic or Latino
Non-Hispanic Asian or Pacific Islander
Non-Hispanic White
<18
3.5
5.6
7.5
1.9
1
18-49
7.8
5.8
8.4
1.7
1
50-64
6.2
5.2
5.7
1.6
1
65+
2.5
3.7
2.7
1.1
1
All (Age-Adjusted)
4.5
4.6
4.6
1.3
1



Table 2.10d: Cumulative U.S. Reported and Coded Deaths of the Seasonal Flu, COVID-19, Pneumonia, and all Causes for Several Leading States (February 1, 2020 - July 17, 2020, CDC)
Location
Seasonal
Flu
Pneumonia + COVID-19 - Seasonal Flu
COVID-19
Pneumonia - Seasonal Flu
All Causes
All Causes (Avg. 2017, 2018, 2019)
New York
1,170
13,302
31,592
19,790
110,224
71,301
NYC
961
7,867
20,423
10,449
51,295
24,900
New Jersey
121
6,685
13,749
9,202
50,742
34,285
New York \ NYC[4]
209
5,435
11,169
9,341
58,929
46,401
Massachusetts
162
2,781
7,701
4,827
34,964
27,749
Pennsylvania
209
2,578
7,130
5,600
64,260
63,000
California
577
3,690
6,671
12,386
131,701
125,430
Illinois
176
3,290
6,592
6,501
57,376
49,039
Michigan
240
2,728
5,552
5,394
51,004
44,740
Connecticut
73
890
3,938
1,595
15,291
14,703
Florida
310
1,944
3,840
8,002
102,808
96,989
Maryland
126
1,326
3,567
2,967
27,533
23,137
Texas
342
1,368
3,116
7,496
95,505
93,632
Louisiana
71
1,430
3,029
2,365
23,524
21,004
Indiana
131
1,102
2,641
3,169
32,433
30,597
Ohio
260
1,091
2,624
3,766
57,197
57,197
Georgia
110
1,087
2,446
3,089
40,603
39,041
Arizona
114
1,168
2,158
2,959
32,481
28,001
Top 10
3,164
39,214
90,332
76,264
645,903
550,373
U.S. \ Top 10
3,375
15,435
36,315
58,894
774,755
777,841
U.S.
6,539
54,649
126,647
135,158
1,420,658
1,328,214


Table 2.10d: U.S. Reported and Coded Death Fractions of the Seasonal Flu, COVID-19,
Pneumonia, and all Causes for Several Leading States (February 1, 2020 - July 17, 2020, CDC)


Location
Seasonal
Flu / All Causes
Pneumonia + COVID-19 - Seasonal Flu / All Causes
COVID-19 / All Causes
Pneumonia - Seasonal Flu / All Causes
Pneumonia + COVID-19 + Flu / All Causes
All Causes (2020) / All Causes (2017-19)
New York
1.06%
12.07%
28.66%
17.95%
13.13%
155%
NYC
1.87%
15.34%
39.81%
20.37%
17.21%
206%
New Jersey
0.24%
13.17%
27.10%
18.13%
13.41%
148%
New York \ NYC
0.35%
9.22%
18.95%
15.85%
9.58%
127%
Massachusetts
0.46%
7.95%
22.03%
13.81%
8.42%
126%
Pennsylvania
0.33%
4.01%
11.10%
8.71%
4.34%
102%
California
0.44%
2.80%
5.07%
9.40%
3.24%
105%
Illinois
0.31%
5.73%
11.49%
11.33%
6.04%
117%
Michigan
0.47%
5.35%
10.89%
10.58%
5.82%
114%
Connecticut
0.48%
5.82%
25.75%
10.43%
6.30%
104%
Florida
0.30%
1.89%
3.74%
7.78%
2.19%
106%
Maryland
0.46%
4.82%
12.96%
10.78%
5.27%
119%
Texas
0.36%
1.43%
3.26%
7.85%
1.79%
102%
Louisiana
0.30%
6.08%
12.88%
10.05%
6.38%
112%
Indiana
0.40%
3.40%
8.14%
9.77%
3.80%
106%
Ohio
0.45%
1.91%
4.59%
6.58%
2.36%
100%
Georgia
0.27%
2.68%
6.02%
7.61%
2.95%
104%
Arizona
0.35%
3.60%
6.64%
9.11%
3.95%
116%
Top 10
0.49%
6.07%
13.99%
11.81%
6.56%
117%
U.S. \ Top 10
0.44%
1.99%
4.69%
7.60%
2.43%
100%
U.S.
0.46%
3.85%
8.91%
9.51%
4.31%
107%



Figure 2.24: Weekly U.S. Deaths (Expected vs. Observed, January 1, 2017 - July 11, 2020)


A study of 3,615 patients admitted to hospital care during the month of March 4 to April 4, 2020 observed that increased body mass index (BMI) was positively correlated with increased likelihood of admission for acute and critical care, with a BMI in excess of 34 indicating a doubling or trebling, respectively (Lighter et al., 2020). These data are given in Table 2.11a. Note that approximately 40% of adults in the U.S. aged 60 or younger have a BMI of 30 or greater. Hazard ratios for in-hospital deaths derived from a detailed study with a population of 17,425,445 in the U.K. is given in Table 2.11b.

Table 2.11a: BMI Likelihood Factor of U.S. Acute and Critical Care Admission for COVID-19


BMI
Range
Acute Care Factor
Critical Care Factor
<30
Not Obese
1.0 (ref)
1.0 (ref)
30-34
Obese Class I
2.0
1.8
>34
Obese Classes II & III
2.2
3.6


A study of 416 hospitalized COVID-19 patients from Wuhan, China revealed that 19.7% of study subjects experienced cardiac injury during their hospital stay (Shi, S. et al., 2020). Cardiac injury during the clinical course of the disease was associated with increased need for support with both noninvasive mechanical ventilation (46.3% for patients with cardiac injury vs. 3.9% for subjects without cardiac damage) and invasive mechanical ventilation (22.0% for patients with cardiac injury vs. 4.2% for patients with no cardiac injury). Risk for serious complications was elevated in these patients as well: ARDS occurred in 58.5% of patients with cardiac injury vs. 14.7% of patients without cardiac injury, and risk of acute kidney injury and overall mortality was also considerably higher for patients experiencing cardiac damage. It should also be noted that patients experiencing cardiac damage were disproportionately older and had increased prevalence of comorbidities, such as hypertension.

Table 2.11b: BMI Hazard Ratio of U.K. In-Hospital Death for COVID-19
(Adapted from OpenSAFELY Collaborative)
BMI
Range
Hazard Ratio
<30
Not Obese
1.0 (ref)
30-34.9
Obese Class I
1.27 (1.18-1.36)
35-39.9
Obese Class II
1.56 (1.41-1.73)
>40
Obese Class III
2.27 (1.99-2.58)


Montopoli et al. (2020) have reported an association between androgen-deprivation therapy (ADT) for prostate cancer and SARS-CoV-2 infection. They note that TMPRSS2 is expressed in localized and metastatic prostate cancer and that its expression is under the regulation of the androgen receptor. In vitro studies have shown that androgen treatment induces TMPRSS2 expression in human lung cancer epithelial cells (A549) and androgen depletion reduces TMPRSS2 expression in mouse lung (Mikkonen et al., 2010). They posited that alteration of androgen levels might influence SARS-CoV-2 infectivity of human lung epithelial cells.

Table 2.11c: U.K. TPP Active Patients and Confirmed COVID-19 (CPNS) Deaths by Age
(Adapted from OpenSAFELY Collaborative)
Age Group
TPP Active
% Total
CPNS Deaths
% Total
18-<40
5,990,809
34.4%
40
0.70%
40-<50
2,875,561
16.5%
94
1.65%
50-<60
3,068,883
17.6%
355
6.25%
60-<70
2,405,327
13.8%
693
12.2%
70-<80
1,948,095
11.2%
1,560
27.5%
>80
1,136,770
6.5%
2,941
51.8%
17,425,445
5,683


Using data obtained from the Veneto Archive of COVID-19 subjects, the Tumor Registry Archive, and the Regional Medicines Technical Commission, Montopoli et al. compiled information on the incidence and course of COVID-19 infection in individuals without cancer, those with cancer, those with cancer of the prostate who were or were not receiving ADT. The primary endpoints of the study were frequency and severity (measured by hospitalization, admission to an intensive care unit, or death) of COVID-19 for these groups. Of a total male population of 2,399,783, SARS-CoV-2 positivity was seen in 4,532 (0.2%), compared with 430 of 127,368 (0.3%) male cancer patients (OR 1.79 (1.62-1.98) p<0.0001). For men with diagnoses of prostate cancer, 4 of 5,273 of those on ADT were SARS-CoV-2 positive compared with 114 of 37,161 men not on ADT (OR 4.05 (1.55-10.59) p = 0.0059). Of the cases of COVID-19 diagnosed in men receiving ADT, 3 of 4 were mild and there were no deaths, compared with 83 of 114 mild cases and 18 deaths in men with prostate cancer not receiving ADT. This is an interesting observation, which may be limited by variable testing of these populations, other comorbidities, and possible differences in social behavior which clearly warrant review of COVID-19 frequency and behavior in other populations before implementing trials of ADT as a preventive or treatment. It should also be noted that some recent trials have failed to show an effect of androgens on TMPRSS2 expression in murine lung (Baratchian et al., 2020) and have suggested that levels of current smoking (as distinct from prior pack-years) may have a rapid effect on expression of both TMPRSS2 and ACE2. It has also been suggested that androgen effects, if robustly confirmed, may be mediated through changes in immune reactivity rather than TMPRSS2 (Sharifi et al., 2020).

Kawasaki-like Disease (MIS-C)

Though younger age is associated with improved clinical outcome, reports of outbreaks of Kawasaki-like disease in children and young adults have substantially increased during the COVID-19 pandemic. Kawasaki disease is an illness that primarily affects children under the age of five and is characterized by three phases. The pathophysiology of the disease originates from inflammation in the blood vessels and manifests in symptoms that include rashes, fever, swollen lymph glands in the neck, strawberry tongue, and sore throat during its first phase. In the second phase of the illness (occurring about two weeks after the initiation of fever), patients may experience joint pain, worsening gastrointestinal symptoms, and peeling skin on the hands and feet. Serious complications that may arise if the disease goes untreated include aneurysm of the coronary arteries, inflammation of the myocardium or pericardium, arrhythmias, and other heart problems, which characterize the third phase of the disease.

Toubiana et al. (2020) describe an outbreak of Kawasaki-like disease in children, now called Multisystem Inflammatory Syndrome (MIS-C), admitted to the pediatrics department of a university hospital in Paris, France between April 27 and May 7, 2020. A total of 17 children were admitted for MIS-C in this period, compared to a mean of 1.0 cases per 2-week period during the 2018-2019 period, showing a pronounced spike in cases during the COVID-19 pandemic. The median patient age for the 2020 study period was 7.5 years (the age range was from 3.7 to 16.6 years), and it is notable that 59% of patients had a parent that originated from sub-Saharan Africa or the Carribean islands, and 12% had a parent born in Asia (considered a risk factor for the disease). Eleven of the 17 patients developed MIS-C shock syndrome and required support in the ICU, and twelve patients developed myocarditis. Fourteen patients showed evidence of SARS-CoV-2 infection (7/17 tested positive for viral RNA by RT-PCR and 14/17 tested positive by antibody detection). As of May 13, 2020, roughly 100 cases of the rare disease had been reported in the state of New York, including three cases where the patient died. Of these 100 cases, over 50% had tested positive for SARS-CoV-2 antibodies, further suggesting the spike in the inflammatory condition is associated with SARS-CoV-2 infection. Verdoni et al. (2020) conducted an observational cohort study on patients diagnosed with a Kawasaki-like disease at a health center in Bergamo, Italy between January 1, 2015 and April 20, 2020. Subjects were divided into two groups: 19 patients that presented with the disease before the COVID-19 pandemic had affected the region (January 1, 2015 until February 17, 2020) and 10 patients diagnosed during the height of the pandemic, from February 18 until April 20, 2020. Eight out of the ten patients were positive for SARS-CoV-2 IgG or IgM antibodies. The group diagnosed during the COVID-19 pandemic had a higher incidence of cases (10 cases per month vs. 0.3 cases per month before the pandemic), a higher mean age (7.5 years vs. 3.0 years), a higher incidence of cardiac involvement (6 of 10 vs. 2 of 19), a higher incidence of shock syndrome (5 of 10 vs. 0 of 19), and a higher incidence of macrophage activation syndrome (5 of 10 vs. 0 of 19). The authors report many other clinical and biochemical features that differentiated the two groups, and they highlight that the patients diagnosed during the pandemic tended to have a more severe disease course.

Consiglio et al. (2020) have reported results of immunologic profiling of children with MIS-C and compared this with patterns seen in normal children, children with Kawasaki disease (diagnosed prior to the emergence of SARS-CoV-2), and adults with COVID-19. They studied 41 children with COVID-19 (all mild), 13 with MIS-C, and 28 presenting with classic Kawasaki disease in the 2017-2018 period and compared these with normal children or adults with severe COVID-19. Principal component analysis of plasma proteins showed significant differences between adults and children with either Kawasaki disease or MIS-C, with higher levels of IL-8 and IL-7. The patterns of MIS-C and Kawasaki disease partially overlapped. Differences were also seen in T-cell subsets discriminating patients with MIS-C and Kawasaki disease, and in the level of IL-17 which was elevated in patients with Kawasaki disease but not MIS-C. They concluded that the “...differences in T-cell subsets and cytokine mediators place MIS-C at the intersection of Kawasaki disease and acute SARS-CoV-2 infection immune states in children as well as the hyperinflammation seen in adults with severe COVID-19”. The observation of autoantibodies targeting immune cell signalling and structural proteins in heart and blood vessels in MIS-C also suggested possible therapeutic strategies.

Molecular and Cellular Factors

It has been reported that patients with more severe COVID-19 have higher viral loads than patients with mild symptoms. In a study performed on 76 COVID-19 patients in a hospital in Nanchang, China, patients were tested for viral load using nasopharyngeal swabs. The samples taken from patients with severe COVID-19 disease had a mean viral load that was 60 times higher than the mean viral load for patients with mild symptoms (Liu, Y. et al., 2020). Viral clearance was also significantly shorter in mild cases, with 90% of mild patients testing negative for SARS-CoV-2 by 10 days after onset of symptoms, whereas all severe COVID-19 patients continued to test positive by this point. These data suggest that severe cases have a significantly longer viral shedding period.

A March 26, 2020 correspondence from the Oxford COVID-19 Evidence Service Team provides some possible evidence linking initial viral load of SARS-CoV-2 at time of initial exposure to severity of COVID-19 disease. Not only may exposure to a higher initial viral load increase the chance of transmission, but it may also be associated with worsened COVID-19 outcomes, putting healthcare workers who are regularly exposed to COVID-19 patients with higher viral loads at greater risk.

In a study of 40 COVID-19 patients in a German hospital, where 13 ended up requiring mechanical ventilation, levels of IL-6 (interleukin-6) were found to be strongly associated with both respiratory failure and need for mechanical ventilation (Herold et al., 2020). The authors reported that 92% of subjects with IL-6 levels above 80 pg/mL experienced respiratory failure, which was 22 times the rate experienced in patients below the 80 pg/mL threshold. Only one patient with IL-6 concentration below 80 pg/mL was reported to experience respiratory failure, and only one patient above this threshold did not require intubation, demonstrating a fairly high accuracy rate for the 80 pg/mL cut-off for predicting outcome.

A retrospective study of 150 patients in Wuhan, China, revealed that serum ferritin levels were elevated in hospitalized COVID-19 patients but even more so in patients that died from the illness than for those who survived (Ruan et al., 2020). The study found that in the 68 patients who died, the mean serum ferritin level was 1297.6 ng/mL, whereas for the 82 survivors, the mean ferritin level was reported as 614.0 ng/mL. In contrast, the normal range for blood serum ferritin is 21.8-274.7 ng/mL. High levels of ferritin are indicative of a variety of conditions, including inflammatory conditions, where particularly high ferritin levels may be a molecular marker associated with increased risk of death.

Patients infected with SARS-CoV-2 also show significantly reduced levels of Natural Killer (NK) and CD8+T cells (Zheng et al., 2020). This specific cell count reduction is likely tied to the upregulation of NKG2A, an inhibitory C-type lectin receptor expressed on the surface of NK and CD8+T cells, in patients with the virus (ibid.). Upregulation of the receptor results in inhibition of the cytotoxic activity of NK cells, an important line of defense against viral infection in the innate immune system. Zheng et al. (2020) found in their study of 68 hospitalized COVID-19 patients that neutrophil count was higher in severe cases of the disorder but that cytotoxic lymphocyte (CTL), NK, and CD8+T cell counts were dramatically decreased in patients with severe disease. Furthermore, the authors noted that NKG2A was significantly upregulated on NK cells and on CTLs in early stages of COVID-19 disease when compared to NK cells and CTLs from individuals without the disease. For patients that recovered, NK and CD8+T cells increased during convalescence, but the number of NKG2A+NK and NKG2A+CTL cells was reduced, further suggesting that NKG2A regulation is a key modulator of COVID-19 progression.

Diao et al. (2020) describe similar findings of reduced overall total T-cell count and specifically reduced CD8+ T cell and CD4+ T cell counts in COVID-19 patients, an effect that is amplified in patients over age 70 or for those requiring ICU care. The authors report that for COVID-19 patients with total T cell count below 800/µL, CD8+T cell count below 300/µL, and CD4+T cell count below 400/µL, survival rate was significantly reduced, even when no obvious symptoms were apparent. Furthermore, T cell count was found to be negatively associated with blood serum concentrations of IL-6, IL-10, and TNF-α. Diao et al. (2020) also report that CD4+T and CD8+T cells from COVID-19 patients had increasingly higher expression of PD-1 (Programmed Death Cell Protein 1) and TIM-3 as symptoms and disease progression became more severe, that is, progressing from asymptomatic to symptomatic to requiring ICU care. These cell membrane proteins are markers of T-cell exhaustion, a form of T-cell dysfunction that often occurs during chronic infection.

Coagulopathy is a common finding in patients with severe COVID-19 and is associated with increased mortality. D-dimer is a fibrin degradation product, that is, resulting from blood clot degradation, and is therefore a molecular indicator of clot formation. D-dimer levels are routinely tested when a patient is suspected to have deep vein thrombosis, pulmonary embolism, disseminated intravascular coagulation (DIC), or other coagulopathies. Several studies have reported that increased D-dimer levels are associated with increased risk of COVID-19 death. Tang, N. et al. (2020) report findings from 183 patients with confirmed COVID-19 and treated at Tongji Hospital in Wuhan, China. Of this pool of patients, 11.5% died expressing significantly higher levels of D-dimer and fibrin degradation product than found in survivors, with mean D-dimer levels of 2.12 mg/L in non-survivors compared to 0.61 mg/L in survivors. Patients who succumbed to the disease also had a significantly longer prothrombin time, and 71.4% of non-survivors showed signs of intravascular coagulation (blood clots) compared to 0.6% of survivors. In a study of 191 hospitalized COVID-19 patients from China, Zhou, F. et al. (2020) report that D-dimer levels in excess of 1 mg/L upon admission was associated with an 18-fold increased risk of death. These results are corroborated by results reported by Huang et al. (2020) who found that in 13 patients receiving ICU care, median D-dimer levels were 2.4 mg/L, significantly lower than the mean level of 0.5 mg/L reported in the 28 hospitalized COVID-19 patients that did not require ICU care.

In a study published on March 11, 2020, Wuhan and Shenzhen researchers have observed in a sample of 2,000 infected patients that individuals with type-O blood, regardless of age and gender, generally experience less severe COVID-19 symptoms, while those with type-A blood experience the worst symptoms. The researchers also remark that healthy individuals with type-O blood may have a lower risk of contracting COVID-19 for reasons not yet understood, while those with type-A blood may be more susceptible to infection and should take extra precautions.

Genetic Risk Factors

Ellinghaus et al. (2020) have sought to identify potential genetic risk factors that may determine some of the variation hitherto observed in COVID-19 severity. The group recruited hospitalized COVID-19 patients that had experienced respiratory failure. All participants were recruited from Spain or Italy, which at the time of the study were epicenters for the disease. They compared the genotypes of these severely affected patients to control participants from the same regions in an effort to identify over-represented genotypes in the severely affected group. The final subject pools were comprised of 835 patients and 1255 control subjects from Italy and 775 patients and 950 control subjects from Spain. A genome-wide comparison of the two groups was conducted, and the authors reported two genetic loci associated with COVID-19-induced respiratory failure. These were the rs11385942 insertion-deletion GA or G variant at the locus 3p21.31, where the GA allele was associated with heightened risk, and the rs657152 A or C single nucleotide polymorphism at locus 9q34.3, where the A allele was associated with elevated risk. Both loci were also identified in the Italian and Spanish subanalyses. The authors reported an additional 24 genomic loci that showed suggestive evidence for association with COVID-19-induced respiratory failure.

The authors then proceed to describe how the two allele variants associated with elevated risk may contribute to heightened disease severity. The 3p21.31 locus is made up of six genes, and the GA variant is associated with lowered expression of the CXCR6 gene and heightened expression of the SLC6A20 gene. Furthermore, the variant is associated with increased expression of the LZTFL1 gene in lung cells. The frequency of the variant associated with heightened risk was more strongly associated with patients receiving mechanical ventilation over receiving oxygen supplementation.

The second site associated with heightened COVID-19-induced respiratory failure was at the 9q34.2 locus, a site that includes the ABO blood group locus. The authors’ meta-analysis uncovered a heightened risk for disease severity for patients with Type A blood and a protective effect for those with Type O blood, an observation that had been previously corroborated in the literature. This elevated risk was only seen in the comparison of patients to control subjects, but no significant difference in the blood type distribution of patients receiving ventilation or oxygen supplementation was found.

Baillee et al. (2020) identify five gene loci associated with heightened risk of developing severe COVID-19. The group conducted a genomic analysis on 2,224 critically ill hospitalized COVID-19 patients from the U.K., across 208 intensive care units. They report five genome-wide genetic loci with significant association with severe disease. One was found on chromosome 12q24.13 in a gene cluster that encodes three antiviral restriction enzyme activators OAS1, OAS2, and OAS3. Another identified site was on chromosome 19p13.2, near the gene TYK2, the gene that encodes tyrosine kinase 2. A third locus associated with severe disease was on chromosome 19p13.3 in a locus inside the gene DPP9 that encodes dipeptidyl peptidase 9. Another locus of significance was on chromosome 21q22.1 in the IFNAR2 gene that encodes for an interferon receptor. Finally, the CCR2 gene, which encodes a chemotactic receptor for macrophages and monocytes, was found to be highly expressed in the lung tissue of severe COVID-19 patients.

Fatality, Recovery, and Potential for Lasting Damage

The case fatality rates for the untreated and unvaccinated for several pathogens and related diseases, including SARS-CoV-2 and COVID-19, is given in Table 2.12 and classified according to their highest status: Outbreak (O), Epidemic (E), or Pandemic (P). The daily death rate for COVID-19 is given alongside those for several endemic diseases in Table 2.13.

COVID-19 patients in ICU care are more likely to succumb to the disease, particularly those requiring mechanical ventilation (as opposed to non-invasive oxygen supplementation) and early estimates of fatality in these patients painted a grave picture, in some cases estimating fatality over 80%. However, this case fatality rate may be much lower than anticipated in the case of ICU patients treated in U.S. hospitals. Auld et al. (2020) conducted an observational cohort study of 217 critically ill hospitalized COVID-19 patients at 6 COVID-19 designated ICUs at three hospitals in Atlanta, GA from March 6, 2020 to April 17, 2020. Of these 217 patients, 165 required invasive mechanical ventilation, and the fatality rate for this group was 35.7%, with 4.8% still on ventilators by the end of the study. The fatality rate for the 217 critically ill patients studied was 30.9%, whereas 60.4% of the patients recovered enough to be discharged from the hospital.

In a March 28, 2020 retrospective, multi-cohort study of 191 COVID-19 patients published in the Lancet, Zhou et al. report that the median time from onset of symptoms until death in non-survivors was 18.5 days (with an IQR from 15.0-22.0 days). Typically, older patients who succumb, do so sooner, whereas younger patients do so later. For those patients that recovered and were discharged from the hospital, the median time from onset of symptoms until discharge was 22.0 days (IQR 18.0-25.0 days). The authors also report that all patients who succumbed to a COVID-19 infection had detectable viral load and severe lymphocytopenia (low circulating lymphocytes) until time of death.

A study of 416 patients with COVID-19 disease from Wuhan, China found that 19.7% of hospitalized patients experienced cardiac injury during their hospital stay. The fatality rate for study subjects with cardiac injury was reported as 51.2% versus the mortality rate for patients showing no cardiac injury at 4.5% (Shi, S. et al., 2020), showing that cardiac injury is a significant risk factor for death in COVID-19 patients. Furthermore, the CDC reports that from February 1, 2020 to July 11, 2020, approximately 43% of U.S. patients who succumbed to COVID-19 also had pneumonia, strongly indicating pneumonia as another contributing condition associated with COVID-19 mortality.

In late February, 2020, the WHO reported that the median time from onset of symptoms until recovery is approximately 14 days for mild cases, which comprise roughly 80% of all reported COVID-19 cases. The median time from symptom onset until recovery was reported as 3-6 weeks for moderate to severe cases. Over 80% of COVID-19-infected individuals make a full recovery within 2-3 weeks from the onset of COVID-19 symptoms.

However, many recovered patients that no longer test positive for viral RNA continue to experience lingering symptoms in the months after active infection and supposed recovery. Such persistent symptoms include low-grade fever, muscle weakness, fatigue, shortness of breath, rashes, cardiac irregularities, anosmia, and other neurological symptoms. Some of these symptoms may be indicative of longer-term tissue damage, such as pulmonary fibrosis, which has been observed in some individuals. Such tissue damage can result in reductions of 20-30% lung function in some cases. As COVID-19 is still very much a nascent zoonotic disease, long-term damage that may result from SARS-CoV-2 infection should be prioritized for close monitoring and study.

Table 2.12: Case Fatality Rates of Several Diseases for the Untreated/Unvaccinated (September 29, 2020)
Pathogen
Disease
Highest Status
Vaccine
CFR
Prions (various)
TSEs
O
N
100%
Lyssavirus
Rabies
O
Y
~99%
Variola major
Smallpox Disease
E
Y
~95%
Ebola virus (EBOV)
EVD
E
N
83-90%
HIV
AIDS
P
N
80-90%
Bacillus anthracis
Anthrax
O
Y
45-85%
Influenza A (H5N1)
Avian Flu
P
Y
~60%
Rickettsia prowazekii
Typhus Fever
E
Y
10-60%
Yersinia pestis
Bubonic Plague
P
Y
5-60%
Mycobacterium tuberculosis
Tuberculosis (TB)
P
Y
43%
MERS-CoV
MERS-12
O
N
~35%
Varicella-zoster virus
Chickenpox (neonatal)
O
Y
~30%
Salmonella enterica
Typhoid Fever
E
Y
10-30%
SARS-CoV-1
SARS-02
E
N
11%
SARS-CoV-2
COVID-19
P
N
3.1-3.3%
Vibrio cholerae
Cholera
P
Y
1.8-5%
Measles virus (MeV)
Measles
E
Y
1-3%
P. falciparum
Malaria
P
Y
~0.3%
Influenza A-C (various)
Seasonal Flu
O
Y
<0.1%
Influenza A (H1N1)
Swine Flu
P
Y
0.02%
Varicella-zoster virus
Chickenpox (children)
O
Y
0.001-0.02%


The average global daily deaths for several endemic diseases is given in Table 2.13.


Table 2.13: Daily Death Rates of Several Endemic Diseases and
COVID-19 for Comparison (September 29, 2020)
Disease
Average Daily Death Rate
COVID-19
3,834 (261 days)
Tuberculosis
3,014
Hepatitis B
2,430
Pneumonia
2,216
AIDS
2,110
Malaria
2,002
Shigellosis
1,644
Rotavirus
1,233
Seasonal Flu
1,027
Swine Flu (H1N1) 2009
743
Norovirus
548
Whooping Cough
440
Typhoid
396
Cholera
392
Measles
247
Rabies
162
Yellow Fever
82


The average daily death rate of COVID-19 is computed as the ratio of the cumulative reported COVID-19 deaths with respect to the known time frame of these reported deaths.

Diagnosis, Treatment, and Vaccine Candidates

“The physician must be able to tell the antecedents, know the present, and foretell the future―must mediate these things, and have two special objects in view with regard to disease, namely, to do good or to do no harm.”

―Hippocrates, Physician (c.460 - c.370 BCE)

Diagnostic Devices and Related Techniques

The use of effective diagnostic devices for the detection of SARS-CoV-2 in biofluids is essential for the rapid identification of COVID-19, which serves both to protect the newly diagnosed individual, who can be isolated and begin receiving prompt treatment for as long as the disease is manifested, and the susceptible general public, who can be informed of accurate case counts to take necessary or reasonable precautions to avoid unnecessary spread of disease. Furthermore, thorough contact tracing protocols may be carried out to further isolate potentially contagious carriers unaware of their exposure to the disease. For this reason, the regular and widespread use of reliable methods of SARS-CoV-2 detection through rapid laboratory testing, such as polymerase chain reaction or isothermal amplification, are paramount.

It is well documented that a large portion of COVID-19 carriers are asymptomatic and some may have never become aware of their infection, with some estimates as high as 40-80% of the infected in certain demographics. Others are in pre-symptomatic stages of the disease and may be contagious. Both groups pose a substantial public health risk. Therefore, the prevalence of symptoms should not be the only driver of testing. Even in locations with low documented caseloads, regular population screening for the prevalence of SARS-CoV-2 should be conducted on random subject pools to continually monitor for the prevalence of the virus.

No laboratory testing method is perfect, and the sensitivity and specificity of many diagnostic methods can leave much to be desired. Sensitivity, the percentage of actual positive cases that are correctly identified as positive for a given disease, is particularly low in polymerase chain reaction methods and can be substantially decreased in more rapid forms of such testing. Meanwhile, optimizing the specificity, the percentage of actual disease negative individuals that are correctly identified as negative, is also an important concern. Due to the slow speed and low sensitivity of some of the viral detection methods, other methods of diagnosis have been recommended to be used in conjunction with viral detection, such as the use of chest CT-scans. However, these methods are more likely to be used for symptomatic individuals, who are more likely to seek testing.

As previously noted, a substantial proportion of COVID-19 carriers are asymptomatic and may have recovered, having no prior knowledge of their infection. Due to low availability of testing during various stages of the pandemic, many symptomatic individuals who did contract COVID-19 did not meet necessary criteria for testing and were therefore not properly documented or reported. Serological assays, which detect the presence of SARS-CoV-2 directed antibodies in blood sera present an effective means of tracking COVID-19 cases in such individuals. For this reason, these methods are essential in tracking and documenting the actual prevalence of recent COVID-19 cases.

Polymerase Chain Reaction

Most current nucleic acid tests for SARS-CoV-2 utilize nasal or throat swabbing to collect samples to test for the presence of the SARS-CoV-2-RNA genome. Collected samples are first transported to a lab and must be processed to extract their RNA content. The presence of SARS-CoV-2 RNA is tested using a reverse transcriptase polymerase chain reaction (RT-PCR), a technique that is used to amplify a specific RNA sequence that may be present in the sample. It does so by rapidly copying target regions of the virus’s RNA that are present in known strains of the virus, such as the nucleotides that specifically code for proteins in the virus’s nucleocapsid. If the target region is present in the sample (which means the sample contains the virus), then the target sequence can be amplified and can be visualized using gel electrophoresis, for example, thereby confirming the presence of the virus and infection in the tested individual. Different versions of the RT-PCR tests simply amplify different target regions present in the viral genome, but they generally do so through a repeated process of thermal cycling, which often takes hours to complete.

There are several disadvantages of currently available PCR diagnostic methods. While PCR is effective for testing for the presence of SARS-CoV-2, the sensitivity of these tests can be quite low, as low as 59% found in a study of 1014 patients with COVID-19 (Ai et al., 2020). A more optimistic study measured the sensitivity of the test at 71% in a group of 51 patients (Fang et al., 2020). Regardless, the reported range of sensitivity for such methods (approximately 60-70%) is not optimal, leading to a substantial number of possible false negatives (~30-40% of people who have the virus that test negative on the RT-PCR test). The primary reason for low test sensitivity comes from the method of sample collection; if the swab does not pick up the virus because it comes in contact with too limited a sample, the virus cannot be amplified using PCR. Furthermore, sample collection is currently carried out by medical professionals using PPE that often needs replacement many times throughout the day. In an effort to preserve scarce PPE supplies that are needed to protect medical professionals treating patients, the U.S. government is working with several organizations to develop tests where samples can be taken at home and sealed for safe delivery to labs.

SalivaDirect Testing

On August 4, 2020, the FDA granted emergency use authorization for the use of a saliva-based test for COVID-19 that had been developed at the Yale School of Public Health with funding from the National Basketball Association and the National Basketball Players Association. A description of the test, its advantages compared with other tests, and initial results were provided in non-peer-reviewed fashion in medRxiv on August 4, 2020 (Vogels et al., 2020). This test, termed SalivaDirect, when compared with earlier tests using nasopharyngeal (NP) swabs, has multiple potential advantages: 1. Use of saliva rather than NP swabs simplifies collection, allows patients to collect their own specimens, and does not require PPE use by medical professionals during collection; 2. Handling of saliva without the need for preservatives for up to 7 days; 3. Avoidance of nucleic acid extraction, which in prior tests was often time consuming, expensive, and dependent on adequate supplies of specific reagents with the use of proteinase K and heat; 4. Use of dualplex RT-qPCT targeting the N1 region of the SARS-CoV-2 nucleocapsid with a human RNase P control. This allowed for reduction in the number of RT-qPCR tests to one per sample.

Tests were conducted with reagents obtained from multiple vendors for both proteinase K and RT-qPCR kits as well as RT-qPCR instruments were investigated, and the majority of combinations gave similar results with some variation in the lower limit of detection. Comparison of results of Ct values from the SalivaDirect test with the modified CDC assay (using nucleic acid extraction and singleplex RT-qPCR) showed slightly weaker detection with a false negative rate of 7.3%. False positives were not observed in 30 tested samples.

The advantages of this test include the simplicity of sample collection, speed, lack of dependence on a single vendor for reagents, and cost of less than $5.00 per test. Large trials are presently being conducted to compare the results of SalivaDirect with conventional nasal swab, nucleic acid extraction, and approved RT-qPCR assays. Tests are also ongoing to investigate the use of SalivaDirect as a tool for pooled assays, in which multiple saliva specimens from asymptomatic individuals are pooled and tested together, with individual testing performed only if the pooled sample tests positive. This technique, if prospectively validated, has the potential to greatly increase testing, with reduced costs and rapid reporting of results, which should be of great benefit in considerations of activities such as opening schools and businesses, not to mention attending basketball games (see Pooled Sampling).

Isothermal Amplification

Whereas RT-PCR is a slow process accomplished through a repetitive thermal cycling procedure (cycles of heating and cooling to denature and re-anneal new nucleotides to the template nucleic acid strands), isothermal amplification methods, such as Loop-Mediated Isothermal Amplification (LAMP) can amplify RNA segments specific to the virus at a constant temperature, usually around 60-65°C. Instead of using heat cycles to separate RNA strands, RT-LAMP uses a powerful polymerase to both add nucleotides and separate the strands of nucleic acid. In addition to a polymerase, RT-LAMP relies on specific primers as reagents to amplify different regions of a particular gene and to speed up the amplification process. As amplification progresses, a by-product of the process, pyrophosphate, is produced, which can bind to magnesium ions in solution to form a white precipitate that can be seen by the naked eye. Other methods, such as directly measuring the turbidity of the resulting solution, can also be used to confirm and quantify the amplified RNA. Because the process can all be done at one temperature, RT-LAMP provides a cheaper, faster, and more portable method of viral detection when compared to RT-PCR.

The speed of RT-LAMP methods can be further enhanced when used in conjunction with CRISPR-Cas12. Cas12 is an endonuclease that cleaves specifically targeted nucleic acid sequences. Broughton et al. (2020) discuss a novel detection method using such a combination that the team dubbed DETECTR (DNA Endonuclease-Targeted CRISPR Trans Reporter), which uses reverse transcription and RT-LAMP methods to amplify RNA regions of the virus. Cas12 is then used to identify specific sequences unique to the SARS-CoV-2 genome, which it cleaves at a site with a FAM-Biotin reporter molecule that enables visual detection of the virus. The authors reported ability to detect the virus with a strong visual signal in less than five minutes.

On March 28, 2020, the FDA approved under emergency use authorization a point-of-care rapid testing device that tests for the presence of SARS-CoV-2 in naso-pharyngeal or oro-pharyngeal swabs. The portable test known as IDNOW, which was produced by Abbott Laboratories, is claimed to deliver a positive result within 5-13 minutes and a negative result within 13 minutes. It is based on a platform that is already used for detecting other viruses such as influenza, and it uses isothermal amplification of the nucleotides that encode the RdRP, with a limit of detection of 125 copies/mL. Basu et al. (2020) tested the efficacy of IDNOW in comparison to the Cepheid Xpert Xpress SARS-CoV-2 RT-PCR test, which amplifies nucleotides that encode the N2 and E proteins. The latter test has a runtime of 45 minutes and a limit of detection of 250 copies/mL. A total of 202 samples were taken using nasopharyngeal swabs from a single patient, 101 of which were transported in viral transport medium and 101 on dry swabs. Abbott IDNOW had a significantly higher proportion of false negatives. It missed a third of the samples detected positive by Cepheid Xpert Xpress when using swabs in viral transport medium, and it missed over 48% of samples detected positive by Cepheid Xpert Xpress when using dry swabs. Overall, the Basu et al. indicate that the test has relatively low sensitivity.

Radiological Methods

Ai et al. (2020) report that noncontrast chest CT scans may be a more sensitive approach for COVID-19 diagnosis. Their study included 1,014 patients in Wuhan, China who took paired RT-PCR tests and underwent chest CTs (always one day apart at most). The findings highlight the increased sensitivity of the approach, reported as 97%, which used observations of ground-glass opacity, consolidation, thickening of the interlobular septa, and other features to determine diagnosis. Compared to RT-PCR, which was reported to have a sensitivity of 59% in this study, CT scanning presented a more reliable and faster approach as a diagnostic procedure that is relatively non-invasive. Radiologists analyzing scans were given data on the patient’s symptoms, which was used to make a final assessment in diagnosis. Unfortunately, the approach had relatively low specificity (25%), and the overall accuracy was approximately 65%.

Raptis et al. (2020) reviewed this and two other studies that had reported that CT scans might be sufficiently sensitive and specific to use for COVID-19 screening and/or diagnosis. They concluded that the existing reports were based on selected retrospective data, provided little data on patient characteristics and criteria for evaluating scans, and in some cases scan specificity showed marked variability among multiple observers. Clear criteria for training observers was not provided. They concluded that more data on well-defined patient populations, scanned and interpreted in uniform fashion, are needed before CT scans can be considered to have a role in COVID-19 screening or diagnosis and that at present their role is properly restricted to evaluation of complications of COVID-19 pneumonia or assessment of possible alternative diagnoses. These are in line with current recommendations from the CDC, the WHO, the American College of Radiology, and the Fleischner Society for Thoracic Imaging (Chou et al., 2020; Rubin et al., 2020).

Lin et al. (2020) reported a comparison of CT imaging findings of a cohort of patients with pneumonia due to COVID-19 (n = 52) and compared these with a group of patients with pneumonia due to influenza (n = 45). Scans were performed using three different scanners with differences in scan thickness and reconstruction thickness. All COVID-19 patients had positive detection of nucleic acid testing. It is important to note, however, that both the COVID-19 and influenza groups excluded patients with “hypertension, diabetes, tumor, chronic obstructive pulmonary disease, bronchiectasis, lung cancer, and other lung diseases,” which may have limited the study by excluding a large number of the sickest patients. Interpretation of images were performed by two chest radiologists who, if disagreeing on interpretation, reached a final decision by consensus. Inflammatory lesions were also analyzed using AI software (FACT version 1.7.0.1, Dexin Medical Imaging Technology), which could evaluate the volume and mean density of the lesions. There is no mention of blinding to diagnosis at the time of review, and presumably this was not done.

While statistically significant differences (p < 0.05) were seen in the proximity of the largest lesion to the pleura, presence of mucoid impaction, presence of pleural effusion, and axial distribution of lesions, there was substantial overlap in these categories. Other characteristics such as the properties of the largest lesion, presence of ground-glass opacities, presence of consolidation, and a number of other features showed no significant differences between the two groups. The authors concluded,

However, CT manifestations of COVID-19 pneumonia and influenza virus pneumonia have a large amount of overlap, and that even with the characteristics evaluated using AI software, no significant differences were detected. Distinguishing between these two types of viral pneumonia with imaging alone is difficult. Therefore CT examination needs to be combined with clinical indicators for comprehensive evaluation, the more important role of CT in the pandemic is in finding lesions and evaluating the results of treatment.

None of these recommendations negate the important role that chest CT may have in management of patients with COVID-19 as determined by molecular testing, particularly with better attention paid to consistency of imaging parameters and the application of more sophisticated methods of image analysis including deep learning and radiomic analysis. They do argue strongly that the primary methods for population screening and clinical diagnostic analysis should remain molecular analysis for viral antigen and/or virus-specific antigen (Chou et al. 2020; Raptis et al.2020; Rubin et al. 2020).

Serological Assays

Recent generation of a serological enzyme-linked immunosorbent assay (ELISA), using recombinant antigens from the spike protein of SARS-CoV-2 (Amanat et al., 2020), offers hope for a more sensitive testing approach than the PCR analyses. Unlike PCR methods, which test for the presence of the virus (which eventually clears after infection), serological assays test for the presence of SARS-CoV-2 antibodies that persist long after infection has passed. Even though serological assays have limitations with regard for the detection of acute cases, they can be used for determining seroprevalence in particular populations where previous exposures and identifying highly reactive human donors offer utility for vaccine and other immunotherapy development. As the authors point out, “Sensitive and specific identification of Coronavirus SARS-CoV-2 antibody titers will also support screening of healthcare workers to identify those who are already immune and can be deployed to care for infected patients minimizing the risk of viral spread to colleagues and other patients…” Clearly, breakthroughs in the development of ELISA test kits and other advances in precision omics, including and beyond proteomics, will advance our understanding of the science of COVID-19, as well as support in silico screening as well as new model development for the better design and implementation of both precision and personalized therapies and preventatives.

Gordon et al. (2020) have suggested the mining of the SARS-CoV-2 protein interactome to better predict disease state and to discover druggable proteins or already approved FDA drugs that can be used in precision treatments for COVID-19 and other coronavirus infections. This, in combination with ELISAs for spike proteins from SARS-CoV-2 (Amanat et al., 2020) and the use of machine learning for large scale screening of datasets from e.g. the White House and National Institutes of Health COVID-19 Open Research Dataset (CORD-19) initiative, offer hope for more personalized and precision treatments for a heterogeneous population of patients who possess unique omics and distinctly challenging, coexisting comorbidities.

A June 2020 study (Premkumar et al.) has also helped the cause of more reliable serologic testing by way of focusing on the receptor binding domain (RBD). The recombinant SARS-CoV-2 RBD antigen was found to be highly sensitive for antibodies induced by this and possibly other SARS coronaviruses.

Pooled Sampling

The aforementioned studies have largely assumed that diagnostic testing will be performed on the sample taken from a single individual, in which case the implications of a positive, negative, or indeterminate test are obvious. Individual testing is, however, quite time and resource consuming and while appropriate for diagnosing modest numbers of individuals, it is impractical when testing larger numbers of people (e.g. the workforce of a factory, students in a college dormitory, etc.). This is especially true when testing must be repeated on a frequent basis. To circumvent these problems, testing of pooled samples has been proposed as a resource efficient alternative. In some cases, pooled testing has been implemented albeit without firm data as to its efficacy.

Both the FDA and CDC have issued statements providing interim guidance on pooled testing (Centers for Disease Control. 1 Aug 2020; US Food and Drug Administration. 24 August, 2020). They have considered its use in three settings:

  1. diagnostic testing of individuals thought likely to have been exposed to SARS-CoV-2 on the basis of symptoms of contact with other individuals known to be infected
  2. screening testing of individuals with no particular reason to expect exposure in order to detect infection in asymptomatic individuals and/or assist in contact tracing
  3. surveillance testing of populations to obtain aggregate data on prevalence, population effects of measures such as social distancing, masking, hand-washing, etc

Pooling is defined as “combining respiratory samples from several people and conducting one laboratory test on the combined pool of samples to detect SARS-CoV-2.” If the pooled test is negative, it is assumed that each of the specimens that contributed to the pool is also negative. If the pooled test is positive, the individuals who contributed to the pooled sample must then be tested individually to determine which one or ones are positive. Pooling should be performed only when the estimated probability of test positivity is low, and since most specimens will be negative, the sensitivity of pooled testing will be less than that of individual testing.

A number of universities including the University of Arizona, University of North Carolina, and University of Virginia have taken this one step further and are testing sewage from university dormitories, with planned testing of individual residents should a positive pooled test arise. The CDC has established the National Wastewater Surveillance System (NWSS) to provide a central mechanism for aggregation of such data.

Anticipating a second wave of SARS-CoV-2 infections, Fogarty et al. (2020) have presented a proposal for testing of groups of individuals who work together (e.g. hospital staff) which combines three key elements:

  1. use of saliva rather than nasopharyngeal swabs, which will facilitate frequent testing and minimize the need for PPE
  2. Pooled testing
  3. Obtaining two specimens of saliva at each encounter (Specimen A and Specimen B). The A specimens would be pooled and tested, with the B specimens being available for immediate individual testing should the pooled A test positive, rather than having to track down individuals to obtain another specimen. This latter approach is derived from sports drug testing where two specimens are routinely obtained but the second tested only if the first appears positive.

The researchers do note that this approach runs the risk of detecting late non-viable viral shedding and that the optimum number of specimens to pool and cycle for PCR will depend on the pool size and COVID-19 prevalence.

Current Clinical Management

The National Institutes of Health of the United States (NIH) has characterized five degrees of increasing severity of individuals infected with SARS-CoV-2:

  1. Asymptomatic or Presymptomatic Infection: Individuals who test positive for SARS-CoV-2 by virologic testing using a molecular diagnostic (e.g., polymerase chain reaction) or antigen test, but have no symptoms.
  2. Mild Illness: Individuals who have any of the various signs and symptoms of COVID 19 (e.g., fever, cough, sore throat, malaise, headache, muscle pain) without shortness of breath, dyspnea, or abnormal chest imaging.
  3. Moderate Illness: Individuals who have evidence of lower respiratory disease by clinical assessment or imaging and a saturation of oxygen (SpO2) ≥ 94% on room air at sea level.
  4. Severe Illness: Individuals who have respiratory frequency >3 0 breaths per minute, SpO2 < 94% on room air at sea level, ratio of arterial partial pressure of oxygen to fraction of inspired oxygen (PaO2/FiO2) < 300 mmHg, or lung infiltrates > 50%.
  5. Critical Illness: Individuals who have respiratory failure, septic shock, and/or multiple organ dysfunction.
Table 3.1: Recommended Initial Management of COVID-19 Patients Depending on Severity (September 1, 2020, Adapted from NIH COVID-19 Treatment Guidelines, Management of Patients with COVID-19)
Status
General Management
Laboratory and Imaging Assessment
Antiviral or Immune-based Therapy
Other
Asymptomatic or Presymptomatic
Self-isolation at home for 10 days from date of initial positive test and 3 days after becoming afebrile None recommended None recommended Contact tracing if short turn-around of testing
Mild
Generally may be managed in an ambulatory setting using telemedicine or remote visits. None recommended Insufficient data to recommend for or against Close monitoring indicated as some patients may worsen rapidly
Moderate
Hospital infection prevention and control measures. If patient will be undergoing aerosol-generating procedures, placement in airborne infection isolation rooms (AIIR) is desirable. Hospital staff should use N95 respirators during such procedures. CBC, Metabolic panel including liver and renal function. Pulmonary imaging by CXR, US, or CT. Refer to sections and tables on Antiviral and Immune-Based Therapies for discussion of investigational agents. Inflammatory markers including CRP, D-dimer, and ferritin may have prognostic value but are not part of standard care.
Severe
Hospital infection prevention and control measures. If patient will be undergoing aerosol-generating procedures, placement in airborne infection isolation rooms (AIIR) is desirable. Hospital staff should use N95 respirators during such procedures. CBC, Metabolic panel including liver and renal function. Pulmonary imaging by CXR, US, or CT. If secondary bacterial pneumonia or sepsis is suspected, administer empiric antibiotics. Refer to sections and tables on Antiviral and Immune-Based Therapies for discussion of investigational agents. Inflammatory markers including CRP, D-dimer, and ferritin may have prognostic value but are not part of standard care.
Critical
These patients will be undergoing aerosol-generating procedures, placement in airborne infection isolation rooms (AIIR) is desirable. Hospital staff should use N95 respirators during such procedures. As above plus additional tests for evaluation and management of cardiac, hepatic, renal, and CNS disease. See recommendations from Surviving Sepsis Campaign1. Refer to sections and tables on Antiviral and Immune-Based Therapies for discussion of investigational agents. Successful management requires attention not only to COVID-19 but also to comorbidities and nosocomial complications. Address goals of therapy with patient(s) (if possible) or family.

1.Alhazzani, W., Moller, M. H., Arabi, Y. M., et al. Surviving Sepsis Campaign: guidelines on the management of critically ill adults with coronavirus disease (COVID-19). Intensive Care Med. 2020. https://www.ncbi.nlm.nih.gov/pubmed/32222812

The NIH COVID-19 Treatment Guidelines also include reviews of standard and investigational antiviral and immunotherapeutic agents and are updated regularly.

Managing patients during a pandemic can be complicated further by comorbidity, that is, with one or more pre-existing or underlying health conditions including, in particular, cancer. Treatment of cancer patients raises a number of issues with the appropriate management of patients with and without a known COVID-19 infection as well as the appropriate allocation and use of hospital resources and staff who are likely to be in limited supply or absent entirely and distressed at times of peak outbreak and onslaught of ER admissions. These issues may involve surgery, radiation therapy, and systemic therapy (chemotherapy, targeted therapy, immunotherapy), and may also apply to patients being treated with curative and palliative intent.

Management of Patients with Cancer

Liang et al. (2020) reviewed data on a prospective cohort of 2007 patients with COVID-19 from 31 regional hospitals in China. All patients were diagnosed with laboratory confirmation of infection and were admitted to the hospital. Because of inadequate documentation of previous medical history, 417 were excluded leaving 1590 for further analysis. Of these, 18 (1%) had a history of cancer, which the authors felt was higher than the incidence in the overall Chinese population (0.29%). Patients with cancer were older (mean age 63.1 years [SD 12.1] vs. 48.7 [SD 16.2]) and more likely to have a history of smoking (4 of 18 (22%) vs. 107 of 1590 (7%)). Differences in sex, other baseline symptoms or comorbidities, or baseline appearance of CXR were not seen. Patients with a history of cancer were more likely than others to have a severe event, defined as admission to the ICU for ventilatory support or death (7/18 vs 124/1572). Patients who had undergone chemotherapy or surgery within a month of the diagnosis of COVID-19 were at particularly high risk. The authors concluded that patients with a history of cancer might have a greater frequency of COVID-19 than those lacking this factor and, if they did develop COVID-19 and were admitted to the hospital, were more likely to suffer severe consequences. The authors proposed three tentative strategies for cancer patients in the current pandemic and future attacks of infectious disease including 1.) intentional postponing of adjuvant chemotherapy or elective surgery, 2.) stronger personal protection for patients with cancer or cancer survivors, and 3.) more intensive surveillance and treatment for cancer patients/survivors who have COVID-19, particularly if they also have other comorbid factors including advanced age.

Zie et al. (2020) commented on this, noting that the conclusion that cancer patients/survivors were at a higher risk for COVID-19 infection was flawed, as the cancer patients might have been under closer follow up and thereby more likely to be diagnosed, and that a proper determination would require comparing the frequency of COVID-19 in a sample of cancer patients rather than the incidence of cancer in a population of COVID-19 patients. They also noted the marked heterogeneity among the 18 cancer patients/survivors in type of malignancy, length of time since treatment, and disease status. They concluded that the current data were “insufficient to explain a conclusive association between cancer and COVID-19”.

Sidaway (2020) reviewed in Nature Reviews Clinical Oncology the experience of Liang and two other reports of the initial Chinese experience with cancer patients and COVID-19. Yu et al. (2020) described the experience from the Department of Radiation and Medical Oncology at Zhongnan Hospital of Wuhan University from December 30, 2019 through February 17, 2020. Of 1524 patients with COVID-19, 12 had cancer (0.79%), which was higher than the cumulative incidence of COVID-19 cases reported in Wuhan, China in the same time frame (0.37%). Ten of the patients were male. Non-small cell lung cancer was the most common diagnosis (7/12). Two patients were without known disease (one two-years NED and another receiving adjuvant breast irradiation following surgery); the remaining 10 had known active disease. Five of 12 were or had recently received chemoimmunotherapy or radiotherapy. As of time of submission of the manuscript, 3 patients had died, 3 remained hospitalized, and 6 had been discharged alive.

Zhang et al. (2020) reviewed laboratory confirmed COVID-19 patients with cancer in three hospitals associated with Tongji College of Medicine in Wuhan from 13 Jan 2020 to 26 Feb 2020. Of 1276 patients with COVID-19 admitted to hospital, 28, or 2.2%, also had cancer. The most common types of malignancy were lung, esophageal, and breast. Mean age of patients was 65. Survival was significantly worse for patients whose chest CT scans showed patchy consolidation or who had received anti-cancer therapy (e.g., chemotherapy, immunotherapy, radiotherapy) less than 14 days before the diagnosis of a COVID-19 infection.

In summary, Sidaway concluded that despite small patient numbers, retrospective data collection, and limited follow-up, there appeared to be a strong suggestion that patients with cancer, especially those receiving recent treatment, were both at higher risk than the general population and more likely to suffer poor outcomes. He urged caution in the conduct of routine follow-up and treatment visits that might increase the risk of cancer patients to those infected with SARS-CoV-2.

Several Oncology Societies representing surgical, medical, and radiation oncologists have provided guidelines on the management of cancer patients guidelines from national organizations. The focus of these guidelines has been to maintain adequate treatment and follow-up of patients with known cancer, particularly but not exclusively for patients with curable malignancies or acutely life-threatening complications (e.g. vena cava compression, spinal cord compression), while minimizing the risk that cancer patients will become infected during the course of their treatment, limiting risk to hospital personnel, and minimizing use of potentially scarce resources such as PPE. These include:

  1. American Society of Clinical Oncology (ASCO, www.asco.org) includes separate sections for Patient Care Information, Provider and Practice Information, Meeting and Program Updates, Government, Reimbursement, and Regulatory Updates, and Questions.
  2. American Society of Hematology (ASH, www.ash.org) has established a website with extensive information on the management of patients with a variety of benign and malignant (e.g. leukemias, lymphomas) and COVID-19. They have also established an international registry to collect data on patients with hematological malignancies and COVID-19. Data will be de-identified and review of the registry by the Western Institutional Review Board has deemed it exempt. Further data are available on the ASH website.
  3. American Society of Therapeutic Radiology and Oncology (ASTRO, www.astro.org) provides general information on practice management, specific guidelines on hypofractionated regimens, which will limit exposure of patients to the clinic setting, and provides specific guidelines that have been shared by a number of Cancer Centers. They have also provided links to other organizations that provide relevant and appropriate information on management of patients with cancer during the COVID-19 Pandemic (See Hyperlinks).
  4. Society of Nuclear Medicine and Molecular Imaging (SNMMI, http://www.snmmi.org) has developed a page with information pertinent to this community and with a large listing of relevant resources:

http://www.snmmi.org/COVID-19?utm_source=Email&utm_medium=Informz&utm_campaign=Email%20Outreach&_zs=Kvon91&_zl=7Cof5

  1. American Association for Cancer Research (AACR, www.aacr.org) recently (27-28 April, 2020) conducted the first half of its usual annual meeting, which had been scheduled to meet in San Diego, as a virtual annual meeting. Sessions were and remain freely available. The morning plenary session on 28 April was devoted to studies of COVID-19 in cancer patients, addressing epidemiology, prognosis, possible interactions between specific cancer treatments, particularly immunotherapeutic ones, and the interaction of socioeconomic factors on treatment of both COVID-19 and cancer. A second session on COVID-19 and cancer later that day addressed key issues in funding of these trials and their impact particularly on early career investigators.
  2. The Society for Surgical Oncology (SSO, www.surgonc.org) has general updated resources and recommendations on the management of cancer patients and COVID-19 and specific recommendations for patients with breast, colorectal, endocrine, gastrointestinal and hepatobiliary, melanoma, peritoneal surface, and sarcomatous tumors.
  3. The American College of Surgeons (ACS, www.facs.org) issued guidelines on triage of patients being considered for thoracic surgery, including patients with known or suspected lung cancer. These were issued in March 2020, and with the ongoing duration on COVID-19 particularly in the United States, may warrant re-assessment to minimize undue delay of appropriate diagnosis and treatment of patients.

In addition to these policy statements, several institutions which have seen a relatively large number of cancer patients with COVID-19 have reported their own guidelines and early experience. Filippi et al. (2020) reported their experience and recommendations from several radiation oncology departments in Northern Italy during their first wave of patients with the COVID-19 pandemic. They defined five areas to prioritize in the management of cancer patients with known or suspected COVID-19: 1.) ensuring radiation therapy delivery to cancer patients, 2.) ensuring safety of staff, 3.) management of cancer patients known or suspected to be COVID-19 positive, 4.) staff reorganization to reduce time in clinic, reducing close contact, working from home, conducting conferences by video or phone, etc., and 5.) reducing patient contact with radiation therapy facilities by postponing or using telemedicine for follow-up visits, using hypo-fractionation when possible, delaying start of non-urgent treatment, and exploring non-radiotherapy methods of palliative treatment.

Ueda et al. (2020) reported experience and policies developed at the Seattle Cancer Care Alliance, Fred Hutchinson Cancer Research Center, and the University of Washington which were the first sites in the U.S. to see a large number of patients with cancer and COVID-19. They addressed issues of general infection control, reduction of hospital-based staffing to the minimum required for quality care with the majority of staff members (e.g. physics, dosimetry, many physicians and nurses, working from home, use of telemedicine for many follow-up and some initial consultations, deferral of consultations for second opinions, discussion of treatment options for patients with low risk prostate and breast cancer whose radiotherapy might be deferred during initial neoadjuvant hormonal management, the use of hypo-fractionated treatment for both definitive and palliative cases, and proactive discussion with patients about appropriate palliative and end-of-life goals). The impacts of these rapid changes on employee and leadership well-being are likely to be significant, and pre-emptive attention to issues such as burnout, dealing with illness and death of colleagues, development of policies for furloughs, mandatory isolation, compensation and provision for child care, and rotation of leadership positions are addressed.

Wu et al. (2020) reported experience from Wuhan, China in January and February 2020. In early January, when neither the extent nor mode of transmission of COVID-19 was clear, treatments were given as usual with no particular attention to mask wearing, hand hygiene, or linear accelerator disinfection. Around January 20, 2020, person to person transmission was reported. Departments were closed between January 23-27, 2020 for Chinese New Year, and after re-opening, many departments closed again for several days because of infections of patients and staff. The Hubei Cancer Hospital, the only hospital specializing in cancer treatment in Wuhan, China did not reopen then but spent three days disinfecting treatment machines and vaults and implementing strict infection control policies for staff and patients before reopening on January 30, 2020. These guidelines included: 1.) patient screening for COVID-19, 2.) health education for patients and re-consenting of patients regarding the risk of infection, 3.) screening of staff for COVID-19, 4.) staff education including proper use of PPE (staff are shown wearing gowns, N-95 masks, gloves, and protective eyewear), 5.) zoning the department into clean/semi-soiled/and soiled zones with specific protocols for allowed activities and disinfection schedules, 6.) special modification of immobilization equipment (e.g., clear wrap, thermoplastic masks, etc.), and 7.) modification of workflow to limit patient-patient and patient-staff contact. The authors report that between January 30, 2020 and the time of the writing of this report (submitted March 17, 2020) there was no documented transmission of COVID-19 between patients and staff.

In departments treating both cancer patients thought to be free of COVID-19 and those with known or suspected infection, it has been suggested that the daily treatment be scheduled to treat first those patients at highest risk for poor outcome with infection (the elderly, those with asthma, COPD, cardiac disease, or diabetes) than treat lower risk but COVID-19 negative patients, and treat COVID-19 positive patients only at the end of the day with reduced staffing.

In some cases these recommendations for alteration in what had been the conventional patterns of treatment in many radiation departments, with relatively lengthy fractionated treatment of many palliative regimens (e.g. bone metastases treated in 10 fractions over two weeks) have been criticized as “brutal” (Johnson 2020). It will be important to indicate to patients in these circumstances that there are a number of well-established, prospective, randomized trials which have shown that shorter treatment regimens, both for palliation of bone metastases, as well as definitive treatment of malignancies such as cancers of the breast and prostate, have shown that shorter regimens are as effective and no more toxic than longer ones (Wright, 2020; Yeramilli, 2020). Some of the reluctance to adopt these, at least in the U.S., has been based on reimbursement patterns which have perversely rewarded radiation oncologists for keeping to the older more protracted regimens. Shorter fractionation patterns reduce the time and economic disruption for the patient, allow more patients to be treated on a limited number of machines, and cost less. Sometimes it takes a pandemic to convince us to do the right thing.

Many of the above recommendations have come from relatively well-resourced cancer centers. As the extent of the COVID-19 pandemic increases to include more patients treated with poorer baseline healthcare, often with low and middle-income, and in rural hospitals, which may have significant limitations in equipment and staffing compared with larger cancer centers, these recommendations may require modification. Some suggestions for these settings have been proposed by Pino et al. (2020).

The initial concern about cancer patients and COVID-19 was to see if these patients had higher incidence and morbidity rates than the general population or a proper age and comorbidity adjusted group and to develop ways to minimize exposure of cancer patients receiving treatment to situations with a high risk of contracting COVID-19. A secondary concern was to develop ways to treat COVID-19 infected cancer patients receiving both definitive or palliative treatment. Initial studies did suggest that individuals with cancer were at somewhat higher risk for infection and for more severe outcomes if infected, although data presented at the most recent AACR meeting suggested that these effects were seen more prominently in data reported from Wuhan, China than in data from France and Italy.

Early strategies tried to minimize contact of known cancer patients with the hospital and clinic environments. Elective surgical procedures, which may have included diagnostic biopsies, were deferred, infusion schedules for chemotherapeutic or immunomodulatory agents were often prolonged, and the use of hypofractionated radiotherapy schedules, particularly those previously shown to be equivalent in tumor control or palliation to more protracted ones, were all strongly recommended. Patient contact was minimized, and most follow-up visits and many initial consultations were performed by telemedicine.

These strategies initially seemed reasonable, particularly when many thought that the COVID-19 pandemic might run its course in a few months. However, now that we understand better, barring rapid introduction of effective antiviral or vaccination regimens, we will remain in this situation for at least a year, and there is increasing concern about the impact of these strategies on survival of cancer patients whether or not they are also infected with COVID-19. The competing needs of patients with COVID-19 and cancer may worsen the prognosis of cancer patients in at least three ways: 1.) Risk of severe or fatal COVID-19 infections in cancer patients, particularly those who are older or have other comorbidities. 2.) Delay of cancer diagnosis and effective treatment due to reallocation of hospital resources, leading to cancer upstaging and likely poorer outcomes, and 3.) Loss of insurance by those whose coverage came from work may lead them to postpone or omit screening procedures or diagnostic evaluation for ambiguous symptoms, causing later diagnosis and poorer long-term results.

Cancer, Immune Checkpoint Inhibitors (ICI), and COVID-19

One issue of recent concern has been the possible interaction of patients with cancer who are being treated with agents which abrogate the PD-1/PD-L1 axis who are also infected with SARS-CoV-2. The use of these agents, as well as those interfering with CTLA-4, has become widespread in a number of cancers including lung, genitourinary, and melanoma, and they have become the mainstay of therapy for patients with metastatic disease in many cases. In theory, the use of these agents might be beneficial by enhancing the immune response to the virus, or deleterious by worsening the immune over-reactivity seen in many cases of COVID-19 associated with a cytokine storm. Early reports of clinical experience gave a mixed picture, with some studies reporting worsened survival for cancer patients who had received ICI (Robilotti et al. 2020) and others failing to show any such association (Mehta et al. 2020). Patients in these studies as well as other case reports had a mixture of underlying cancer diagnoses, co-morbidities, ages, and other factors known to impact prognosis.

Luo et al. (2020) have carefully examined a cohort of 69 patients from Memorial Sloan-Kettering Cancer Center with lung cancer and COVID-19 (documented by RT-PCR) treated between March 12, 2020 and April 13, 2020 and followed for a median of 14 days. Of these, 41 (69%) had received PD-1 blockade prior to the diagnosis of COVID-19 with a median time interval of 45 days (range 4-820 days). Overall, they did observe a significant association between prior PD-1 blockade and disease severity or outcome (hospitalization, intubation, death). Unadjusted differences in these were no longer seen when adjusted for prior smoking status. Peak IL-6 levels did not differ between the two groups. They concluded that these findings were encouraging for the continued use of PD-1 blockade for lung cancer patients during the COVID-19 pandemic, but that further studies in this and other malignancies (e.g. GU, melanoma) were indicated to establish generalizability and durability of their findings.

Potential Impact of Delays in Cancer Diagnosis and Treatment on Prognosis

There are at present few good quantitative data on the extent to which these will occur, or the impact they will have on survival outcomes. Lai et al. (2020) have developed a model for estimating excess mortality in patients with cancer, multimorbidity, and COVID-19. This is based on data on referrals for cancer diagnostic procedures and chemotherapy treatments in England and Northern Ireland during March and April 2020. It requires making assumptions, for which there are as yet few data and the authors admit that their estimates are “plausible’, on both the Relative Impact of the Emergency (RIE) and the Proportion of the population Affected by the Emergency (PAE). Under what they consider to be conservative assumptions that only incident cases will be affected, a PAE of 40% and an RIE of 1.5, they calculate an excess 6,270 deaths in England and 33,890 deaths in the United States due to the impact of COVID-19 on cancer treatment. While the exact figures are open to question and will require more data to refine the estimates of RIE and PAE, it seems clear that the COVID-19 Pandemic will impair survival of cancer patients including those not previously infected by the virus.

Mehta et al. (2020) have reported case fatality rates in cancer patients with COVID-19 treated at Montefiore Health System in New York between March 18 and April 8, 2020. They identified 218 patients with cancer and COVID-19, 164 with solid tumors and 54 with hematologic malignancies. Sixty-one (28%) of these patients died, with the rates highest for those with lung cancer (55%), hematologic malignancies (37%), and gastrointestinal cancers (colorectal (38%), pancreas (67%), upper GI (38%), and gynecologic malignancies (38%). An age and gender matched control group of COVID-19 patients without cancer from the same hospital system has a mortality of 14% during the same period. Active chemotherapy and radiotherapy were not associated with mortality, and few patients were receiving immunotherapy. These patterns of increased mortality with a predominance of patients with lung cancer or hematologic malignancies are similar to that reported by Dai et al. (2020).

It will be necessary in the near future to develop policies which can minimize the risk that cancer patients will be exposed to COVID-19 infection, particularly while under active therapy with chemotherapy, while minimizing delays in their diagnostic and staging procedures, facilitating appropriate surgery, and allowing essential adjuvant radiation, chemotherapy, or immunotherapy with minimal disruption. Waterhouse et al. (2020) have in press in JCO Oncology Practice a survey of ASCO members addressing specifically the changes and challenges impacting clinical trial programs but which also addresses other key elements of oncology practice in the context of COVID-19. They note that the development of robust mechanisms for telehealth, use of electronic signatures, allowing remote lab and imaging facilities to collect data, direct shipment of oral drugs to patients, and standardization of policies and procedures, all of which have been discussed by Cooperative Groups for decades, have now become standard of care in a few months. While borne of tragedy, some of the changes to health care and its documentation may be of great general value. It will be our responsibility to ensure that these improvements are distributed equitably throughout our healthcare system which may be a major challenge in the coming years until we develop a more unified healthcare system.

Treatment of Cancer Patients on Cancer-related Clinical Trials.

While entry of new patients on cancer-related clinical trials is likely to be infrequent or nonexistent during the COVID-19 Pandemic, a number of patients currently enrolled on trials will require further treatment, assessment of response, and follow-up. For these patients, the changes in their usual treatment, follow-up, and imaging schedules mandated by the pandemic may understandably result in what would ordinarily be considered protocol variations or violations. Guidelines for appropriate management of these situations, including provision for expedited review of protocol amendments and guidelines to Institutional Review Boards and Protocol Review Committees have been proposed by the FDA, CTEP, and several Cooperative Groups (O’Dwyer, 2020; You, 2020). In some cases there may be potential conflicts for use of scarce agents such as tocilizumab, an IL-6 antagonist, which is used to manage cytokine release occurring following CAR-T therapy and is in investigational use in patients with COVID-19.

The April 17, 2020 issue of The Cancer Letter was devoted largely to interviews with the NCI, ACS, and other major US agencies exploring the interaction between the need for cancer treatment during the COVID-19 Pandemic. The authors provided data indicating that accrual to clinical trials from the National Clinical Trial Network had declined significantly between February 3-9, 2020 and March 23-29, 2020. Comparing the last week of this period to the average of the preceding 7 weeks, accrual for the Intervention step trials decreased by 44% and for the Screening step declined 42%. While the initial impact of the COVID-19 pandemic on accrual to clinical trials has been negative, a number of investigators have viewed this as an opportunity to make some long-needed changes in trial efficiency, approval and accrual processes, availability to individuals not living in major urban centers, and limiting data collection to what is needed to evaluate the endpoints of the trial (Bailey 2020; Borno 2020; Shuman 2020). In the future, these changes may result in more efficient and equitable clinical trials not only in oncology but in medicine in general.

Several registries have been established to collect data from national and international sources on COVID-19 patients with cancer. These include:

  1. ASCO Survey on COVID-19 in Oncology (ASCO) Registry (centra@asco.org).
  2. The COVID-19 & Cancer Consortium (ccc19.org). US Cancer Centers and other hospitals
  3. The Global COVID-19 Observatory and Resource Center for Childhood Cancer, St. Jude Global (covid19childhoodcancer@stjude.org) in collaboration with the Societe Internationale Oncologie Pediatrique (SIOP).
  4. TERAVOLT (Thoracic Cancers International COVID-19 Collaboration) (AACR Abstracts Online 2020; Garassino M.C., TERAVOLT: First results of a global collaboration to address the impact of COVID-19 in patients with thoracic malignancies). Coordinated through the International Association for the Study of Lung Cancer and Vanderbilt University (leora.horn@vumc.org).

Other disease and population specific registries are rapidly being formed and the parent oncologic organizations should be consulted for additional options.

Management of Patients with Diabetes

One of the most severe complications associated with diabetes is diabetic ketoacidosis (DKA), a life-threatening condition induced by low levels of insulin. In the absence of insulin, relatively higher levels of glucagon will trigger the liver to produce glucose from its glycogen stores, thereby elevating circulating blood glucose, which may contribute to increased urination and dehydration. At the same time, the body will also switch over to fatty acid catabolism, producing acidic ketones. The increased production of this byproduct contributes to lowered blood pH (acidosis).

DKA is more common in individuals with Type 1 diabetes, but it may also occur in Type 2 patients, particularly in those who have poor blood sugar management and concurrent infections. Viral infections are especially associated with elevated risk of DKA, and so glucose levels, blood pH, as well as urine or blood ketone levels should be closely monitored in diabetics with COVID-19. Common DKA symptoms include dehydration, increased urination, vomiting, abdominal pain, and in severe cases, a fruity breath odor, confusion, loss of consciousness, and deep gasping breathing (known as Kussmaul breathing), a form of hyperventilation that may help increase blood pH. It is particularly important that diabetes patients are kept well-hydrated with carbohydrate-free fluids to help prevent this possible complication. DKA can make fluid and electrolyte management particularly challenging, which may contribute to elevated risk of sepsis, a serious complication associated with COVID-19.

Management of Pregnant Patients

From the first reports of COVID-19 infection, there has been understandable concern regarding the impact of infection on maternal and fetal health. Initial reports from Wuhan, China suggested that pregnant women did not seem to be at risk for more severe disease, and further experience in Europe and the U.S. appears to have borne this out (Breslin 2020; American College of Obstetricians and Gynecologists 2020). While data are limited, particularly among the medically underserved population who make up a large percentage of the U.S. cases, guidelines regarding management of COVID-19 during pregnancy have been issued by a number of relevant professional societies including the CDC (2020), the American College of Obstetrics and Gynecology (2020), and the Society for Maternal Fetal Medicine (2020).

In general, overall management of the pregnant woman with COVID-19 should be directed primarily by the severity of the COVID-19 infection. Management of the pregnancy should be guided more by obstetric considerations unless COVID-19 is severe. A diagnosis of COVID-19 is not generally considered an indication for early delivery. Women diagnosed with COVID-19 late in the third trimester may attempt to postpone delivery (if feasible) until a negative test is obtained to minimize neonatal transmission. SARS-CoV-2 has not been reported in vaginal fluids nor in breast milk but is present in feces, suggesting an increased risk with vaginal delivery rather than Cesarean section. There is limited data on transplacental vertical transmission of SARS-CoV-2. This appears to be uncommon but has been reported in rare cases, as have neonates who had IgM against SARS-CoV-2 at birth.

Current and Proposed Clinical Trials

The scientific method allows one to formulate hypotheses that are intended to explain phenomena by understanding the cause-and-effect relationship between two measurable variables. These hypotheses can be investigated by designing studies with an appropriate methodological approach. A good study design addresses concerns like biases that represent a threat for the validity and reliability of the results. Studies can be classified into two types: 1.) observational, if there is no intervention being introduced to the subjects by the researcher, or 2.) interventional, if in fact there is. Interventional studies are superior to observational studies in many aspects; in particular, they allow the investigator to have better control over the conditions of study, decreasing the possibility of biases such as confounding.

Among interventional studies in medical research, clinical trials are by far the most reliable source of scientific evidence when investigating cause-and-effect relationships. Essentially, in clinical trials an intervention is introduced into subjects sampled from a population of interest. The effect of the intervention can be evidenced upon completion of the study, when researchers after collecting a sufficient amount of data can perform a thorough analysis according to an appropriate statistical method. This intervention might involve, but is not limited to, for example, a drug, a diagnostic test, or even a survey by questionnaire.

Every clinical trial investigates the effect of an intervention on an outcome (usually multiple outcomes) of interest. Aims of clinical trials may vary depending on the stage of development, which in turn is defined by phases (only for drugs, biological products, or radiation) that differ from each other according to study design characteristics. For example, first-in-human studies are mainly focused on safety to humans. Five different phases are described based on definitions developed by the U.S. Food and Drug Administration (FDA).

Information about clinical trials is publicly available through www.clinicaltrials.gov. Filtering by phase allows to search for studies according to the following categories:

  1. Preclinical Trial involves testing of a compound in non-human candidates for the purpose of studying efficacy, toxicity, and pharmacokinetic action.
  2. Early Phase I Trial (formerly listed as Phase 0) involves exploratory trials aiming to confirm what preclinical trials predicted about how the compound will affect the body.
  3. Phase I Trial (also known as First-in-Human Studies) is mainly focused on safety, as well as side effects (on-target and off-target), pharmacokinetics, posology, etc. and is usually conducted with a small number of subjects, often just including healthy volunteers. One goal is to determine the recommended Phase II dose for use in subsequent studies.
  4. Phase II Trial (also known as Proof-of-Concept Studies) is focused on effectiveness, but safety is still being evaluated (i.e. short-term adverse events) and may be conducted as a single-arm trial with comparison to historical controls, or as a randomized Phase II design comparing the investigational arm with a standard arm (which may be a placebo if there is no established effective therapy).
  5. Phase III Trial involves larger studies (often multi-center) with more participants, and is focused on improving the knowledge on safety and effectiveness by studying different populations and different dosages as well as comparing with different compounds. These typically compare an intervention with the current standard of treatment, which in some cases may be a placebo
  6. Phase IV Trial is the final phase after the FDA has given its approval for marketing. These studies are focused on long-term surveillance for safety, efficacy, and optimal use. Some refer to them as post-approval advertising whose main goal is to get the agent into the hands of prescribing clinicians to encourage its use.
  7. Not Applicable (Trial) applies to trials without FDA-defined phases, including trials of devices or behavioral interventions.

In planning any clinical trials, careful attention should be paid in its design to the outcome(s) to be assessed, the clinical endpoints which will be measured to assess these outcomes, patient inclusion and exclusion criteria and possible stratification in randomized trials if factors such as age, comorbidities, and severity of disease are felt to be relevant to the outcome(s), and plan for statistical analysis. These should all be defined before patients are entered in the study.

Given the spread and mortality rates of SARS-CoV-2 and COVID-19, significant concerns for humanity arise. Prevention and treatment options are eagerly needed. As for the moment we have no available options, the standard of treatment remains as supportive care. To tackle this threat, several clinical trials are currently being conducted in countries all over the world, and the number of trials is growing each day. As of March 30, 2020, almost 3 weeks after COVID-19 was declared a pandemic by the WHO, 220 clinical studies were listed for public access (see http://www.clinicaltrials.gov), none of which have provided preliminary results. Out of this total, 143 (65%) trials corresponded to interventional studies (actual clinical trials). From the 143 clinical trials, 44 (30.8%) trials were classified as Phase III, and 14 (9.79%) were classified as Phase IV. As of August 23, 2020, out of the ~3,000 clinical trials being conducted concerning COVID-19, ~2,000 were studying treatment options, with ~120 Phase I-IV clinical trials studying potential vaccine candidates (52 in Phase I, 59 in Phase II, 44 in Phase III, and 4 in Phase IV, all four of which were studying the BCG vaccine, an approved vaccine for use in the prevention of tuberculosis).

The development of a novel medication in the U.S. takes on average 12 years, from discovery of target to approval for marketing. For vaccines, from target identification to development of a high quality compound the average time usually lies between 10 to 15 years. These time lapses can be reduced if enough resources and efforts are put together. Some experts expect that good evidence should be available in about 6 months. As of April 1, 2020, the NIH and other U.S. Federal Agencies funded 3 clinical studies; 2 of these for treatment purposes and 1 for the development of a vaccine. The following sub section will summarize the 2 studies being conducted for treatment purposes, as the vaccine is already mentioned later in this section (see Current Vaccine Candidates).

The rapid spread and significant lethality of COVID-19 have understandably given us a sense of great urgency in the development and deployment of effective treatment and preventive strategies. While this is understandable, it may give rise to incautious haste in the design and reporting of clinical trials of these agents. A number of recent agents have had results reported in submitted but not reviewed form, or as press releases, without undergoing peer review. Critical details appear not to be provided. London and Kimmelman (2020) have outlined the core concepts of what they deem “pandemic research exceptionalism” in a recent article in Science. Here, they identify the assumptions driving this practice as 1.) some evidence now is preferable to better evidence later, 2.) the belief that randomization or placebo controls (when there is no standard of care) conflict with the perceived responsibility of the clinician caring for the patient, and 3.) the expectation that researchers and sponsors are free to exercise broad discretion over research design, without the recognition that their work frequently has undergone significant funding and its results a public good. To counter these assumptions, the authors propose five “conditions of informativeness and social value”, namely, 1.) Trials should be important, aiming to detect results that are realistic but also clinically meaningful, 2.) Trials should be rigorously designed, and 3.) Trials should undergo analytical integrity. This includes such elements as specifying trial outcomes and endpoints up-front, rather than choosing them after the fact. 4.) Trials should be reported completely, promptly, and consistently with prespecified analyses. 5.) Trials should be feasible, in terms of timeliness and accrual. While these criteria may seem to run counter to our desire to get rapid answers, they have been well-borne out by decades of clinical research and seem far preferable to the current practice of publication by press release (with details to follow at some unspecified date). There is also the responsibility of the press to report the results of one trial in the context of other trials addressing the same general issue rather than citing its results in isolation.

The global production of literature on coronaviruses, SARS-CoV-2, and COVID-19 has exploded and become a ‘pandemic paper tsunami’ (Brainard 2020). As of April 24, 2020, it is estimated that this literature has grown to ~88,000 published articles and ~24,000 preprints. While a number of journals have made all COVID-19-related publications freely available, not all have and about 20% of these may be behind various paywalls, which limit both human and AI access.

Several institutions have attempted to coordinate these various publications and make them amenable to both human and AI searches. The White House office of Science and Technology Policy launched the COVID-19 Open Research Dataset (CORD-19), which now includes more than 200,000 articles and preprints. However, not all of these are available in full text format, and not all include such terms as ‘coronavirus’ in their titles, abstracts, or keywords.

Another approach, being used by Kate Grabowski and colleagues at Johns Hopkins, is to use careful human curation by 50 or more reviewers of the literature and to select high-value articles in their database, rejecting those which were reviews, commentaries, protocols, or contain poor-quality data. Their results, available as the 2019 Novel Coronavirus Research Compendium, have reviewed and summarized more than 120 high quality papers on a variety of topics related to COVID-19 as of its launch in April and should continue to grow with time. Balsari et al. (2020) have also published guidelines for selecting trustworthy papers from the current unwieldy flood.

Adaptive COVID-19 Treatment Trial

The Adaptive COVID-19 Treatment Trial (ACTT) is an adaptive, multicenter, randomized, double-blind, placebo-controlled Phase III clinical trial. It is funded by the National Institute of Allergy and Infectious Diseases (NIAID). The aim is to evaluate the safety and efficacy of novel therapeutic agents in hospitalized patients with confirmed diagnosis of COVID-19 infection. The population includes people with moderate to severe disease aged between 18 and 99 years, excluding pregnant women, as well as people with hepatic and renal impairment. The primary outcome is defined as the percentage of subjects reporting each severity rating on an 8-point ordinal scale (www.clinicaltrials.gov Identifier: NCT04280705).

Adaptive clinical trials allow investigators to modify parameters of the protocol according to observations related to the outcomes along the course of the study. This is part of interim analyses, which are performed to monitor safety and efficacy of the intervention. The idea is to make decisions during the course of the trial, such as early termination, if needed, either because the data shows enough evidence to justify the next Phase, or to stop the trial due to concerns about risk/benefit ratio.

Leask (2020) has summarized the progression from anecdotal observations to randomized trials with a variety of agents and listed more than 50 current and planned trials as well as provided several links to organizations providing links to trials of both vaccines and therapeutic agents. The NIH provides an up to date listing of COVID-19-related clinical trials (902 trials as of April 23, 20202) which allows filtering by study type, activity, eligibility criteria, phase of trial, funding source, publication status, and other factors (https://clinicaltrials.gov/ct2/results?cond=COVID-19).

RECOVERY Trial

The Randomised Evaluation of COVID-19 Therapy (RECOVERY) Trial is a UK randomized clinical trial that aims to test the therapeutic benefit of a variety of possible treatments for hospitalized COVID-19 patients. The primary sponsor for the trial is Oxford University, with funding provided by a grant from the UK Research & Innovation/National Institute for Health Research. As of July 28, 2020, the trial has already published results on the clinical effects of the use of such therapies as lopinavir-ritonavir (see Lopinavir and Ritonavir), dexamethasone (see Dexamethasone), hydroxychloroquine sulfate (see Chloroquines), and clinical trials concerning the use of low-dose dexamethasone, azithromycin, Tocilizumab, and convalescent plasma are ongoing. It has also enrolled over 11,800 patients across 176 NHS hospitals in the UK.

Solidarity Trial

The Solidarity Trial for COVID-19 treatments is a Phase III-IV multinational clinical trial organized by the World Health Organization. It began on March 18, 2020 and has over 100 countries participating. The randomized, clinical trial aimed to evaluate the efficacy and safety of the use of remdesivir, lopinavir/ritonavir, lopinavir/ritonavir with interferon beta, and hydroxychloroquine or chloroquine. On July 4, 2020, the WHO announced that it would continue the hydroxychloroquine and lopinavir/ritonavir treatment arms of the study, citing interim trial evidence that both treatments showed little or no reduction in mortality of hospitalized COVID-19 patients when compared to patients receiving standard care. A separate Solidarity Trial for vaccines was announced in May, 2020.

Preliminary results of this trial have been released as a medRxiv preprint (not peer-reviewed) as of 15 October, 2020 (Pan et al. 2020). A total of 11,266 adults who were hospitalized with COVID-19 were randomized in 405 hospitals in 30 countries Allocation was 27850 remdesivir, 964 hydroxychloroquine, 1411 lopinivir, 651 interferon plus lobinivir, 1412 only interfreron, and 4088 no study drug. Compliance was reported as 94-96% midway through treatment with 2-6% crossover. None of the study drugs definitely reduced in-hospital mortality in unventilated patients or any other subgroup of entry characteristics, nor did they impact initiation of ventilation or duration of hospitalization. The authors concluded that these drugs as used in this patient population had littleor no effect on the predefined study outcomes.

Proposed Repurposed Drug Therapies

As of September 2020, only one FDA-approved drug therapy (Remdesivir) is available for use in the treatment of severe COVID-19 patients (convalescent blood plasma, which was also given emergency use authorization, is discussed in Potential Experimental Treatments), so the identification of existing pharmaceutical drugs that may be repurposed for use in such patients is especially crucial. Moreover, clinical trials for repurposed drugs are much faster than they are for new drug therapies. Repurposed drugs have also gone through previous safety trials, and their potential for adverse reactions has already been well-described. Using AI and large scale compound repurposing (e.g. Riva L et al., Nature, 2020), several new candidate compounds have emerged that could contribute to applications of precision molecular medicine for COVID-19. One finding that came out of this study, looking at isolates of SARS-CoV-2, is the potential for antiviral activity of a highly selective PIKfyve kinase inhibitor. An example of such AI-informed large scale screens of potentially repurposable drugs is the discovery of a compound, LAM-002A, that could have utility for COVID-19 (Bouhaddou M et al., Cell, 2020).

The drugs that follow in this section are compounds that have been approved for use in the treatment of other conditions but may have potential in ameliorating the condition of COVID-19 patients. The mechanisms of their purported actions differ widely, however. Since an understanding into the effects these drugs may have on COVID-19 is only emerging, it is essential to assess the efficacy, specificity, and safety of each individually. For this reason, randomized, large-scale clinical trials on such patients are not only recommended but necessary.

Angiotensin Receptor Blockers

Angiotensin II Receptor Blockers (ARBs) are antagonists of angiotensin II receptors, which are commonly used to treat hypertension. ARBs bind to angiotensin II thereby inhibiting angiotensin II from binding to its receptor. In doing so, ARBs effectively block a physiological pathway responsible for vasoconstriction, which can lead to increased blood pressure and hypertension (see ACE2 Receptor). In addition to being a risk factor for severe COVID-19, hypertension has also been linked to many other risk factors of severe COVID-19, such as endothelial dysfunction, inflammation, and fibrosis. The loss of ACE2, an enzyme which can convert angiotensin II into angiotensin 1-7, during SARS-CoV-2 infection can lead to enhanced levels of angiotensin II, causing enhanced fibrosis and in some cases acute lung injury. Thus, the use of ARBs to lower the action of angiotensin II may be therapeutic in COVID-19 patients, particularly in those with a history of hypertension.

However, the use of ARBs in COVID-19 patients has been the subject of some controversy. In March 2020, Fang et al. suggested that the use of ARBs may upregulate ACE2 expression, which could enhance SARS-CoV-2 entry into host cells, potentially leading to a more severe clinical course from higher successful rates of early viral replication. A response from the European Society of Cardiology on March 13, 2020 claims that there is no evidence to substantiate this claim concerning the use of ARBs, and in fact, prior animal studies have shown that these very medications may be protective against serious lung complications. Below we outline information on current clinical trials that are testing the safety and efficacy of using ARBs in the treatment of COVID-19 patients and provide some study results on the drugs’ impact in COVID-19 patients.

Current Clinical Trials

There are a wide variety of ARBs that have been studied for their effect in the treatment of COVID-19 patients. Some common ARBs include Losartan, Valsartan, Olmesartan, and Telmisartan. A substantial portion of COVID-19 clinical trials concerning ARBs are focused on the use of Losartan, particularly those being carried out in the U.S.

As of July 28, 2020, there are 45 clinical trials studying the effect of ARBs in the treatment of COVID-19 registered internationally, 10 of which are being carried out in the U.S. For the U.S. trials, one is in Phase I, five are in Phase II, two are in Phase III, and one is in Phase IV. Only one registered trial in Ireland has been suspended, and one registered trial in China concerning the impact of hypertension and hypertension treatments in COVID-19 patients was completed on March 30, 2020.

Study Results

***Mehra et al. (2020) conducted an observational study of 8,910 hospitalized COVID-19 patients from 11 countries located on three continents. The authors report that neither ACE inhibitors or ARBs were associated with increased risk of death or increased severity in COVID-19 symptoms for all patients studied. The same lack of association was found when only patients with hypertension were included in the analysis. The study also found that use of ACE inhibitors or statins (used to treat high cholesterol) was associated with decreased death when compared to non-use of the drugs, but since the study was not randomized, limited conclusions can be drawn. [Concerns over the source of the data presented in this paragraph have been recently expressed by the editors of the New England Journal of Medicine, and the conclusions of this work have been called into question.]*** (Servick et al. (2020))

In a study of COVID-19 patients from the Lombardy region in Italy, Mancia et al. (2020) found similar conclusions. A comparison of 6,272 COVID-19 patients with 30,759 controls matched for age, sex, and municipality of residence revealed that neither ACE inhibitors or ARBs were associated with any differences in prevalence of SARS-CoV-2 infection. The drugs were also not associated with increased severity of COVID-19.

Reynolds et al. conducted an observational study that analyzed the health records of 12,594 patients admitted into the New York University Langone Health System who were tested for COVID-19. Only 5,894 patients tested positive, and of those, only 1,002 showed signs of severe illness. The study found no association between ACE inhibitor or ARB use with increased likelihood of SARS-CoV-2 infection or with increased severity of illness, even when the analysis was isolated to just patients with hypertension.

Chloroquines (with or without Azithromycin)

On March 28, 2020, the FDA issued an Emergency Use Authorization to allow for hydroxychloroquine sulfate and chloroquine phosphate in adult hospitalized COVID-19 patients who were not participating in another investigational clinical trial. However, on June 15, 2020, the FDA revoked the authorization, citing that both chloroquine phosphate and hydroxychloroquine sulfate showed little to no efficacy in treating COVID-19. Furthermore, the use of the drug had been previously linked to an increase in serious cardiac adverse events, as well as other serious adverse effects, showing definitively that the FDA no longer had confidence that the benefits of using the drug outweigh any potential risks.

Initial interest in the use of the two medications in treating COVID-19 stemmed from the widespread availability of the drugs and also their previously reported antiviral properties. Zinc ionophores like chloroquine phosphate and hydroxychloroquine sulfate show increased transport of Zn2+ cation into the intracellular space (Yao et al., 2020), which inhibits RNA-dependent RNA polymerase, thereby inhibiting the replication of the virus (te Velthuis et al., 2010). Both drugs are approved for use to prevent and treat malaria. While the two drugs are structurally similar, hydroxychloroquine sulfate is also used in the treatment of lupus and rheumatoid arthritis. Both drugs have shown efficacy in lowering inflammatory response, and the mechanism for their action is thought to stem from their chemical properties. Both drugs are weak bases that can easily diffuse through lipid membranes. When they enter acidic lysosomes, they become protonated, which in turn inhibits their ability to cross lipid membranes, causing them to accumulate in lysosomes. As a result, the pH of the lysosomes increase, which decreases the lysosomes’ proteolytic activity. It is believed that this process decreases innate immune cell activity, which is why hydroxychloroquine sulfate may be effective in treating some autoimmune conditions.

Most adverse responses to the medications are relatively mild, but retinopathy is a more serious side effect that may develop, particularly with chronic use. Liver damage and liver failure are also possible adverse effects from long-term use, and both medications are contraindicated for patients with glucose-6-phosphate dehydrogenase deficiency, psoriasis, porphyria, anemia, as well as other conditions. The use of these medications may also be associated with elevated risk of heart problems in patients with COVID-19, particularly arrhythmias associated with QT prolongation.

Both medications were tested early on in the pandemic as COVID-19 therapies in China and South Korea, where they showed some efficacy in reducing symptoms and in improving patient outcome (Todaro et al., 2020). The CDC also reported that in cell culture, when administered 24 hours prior to introduction of the virus, chloroquine phosphate significantly reduced SARS-CoV-1 infection (Vincent et al., 2005). Nevertheless, many large-scale meta-analyses have provided evidence that these drugs have limited efficacy in the treatment of patients with severe COVID-19. Some studies have also highlighted the potential for adverse effects in COVID-19 patients, possibly contributing to increased mortality.

Current Clinical Trials

As of September 26, 2020, there were 26 clinical trials studying the effect of chloroquine phosphate in the treatment of COVID-19 registered internationally, two of which are/were being carried out in the U.S. For the U.S. trials, one is in Phase II, and one is currently suspended, but was in Phase III. There are ~200 clinical trials studying the effect of hydroxychloroquine sulfate in the treatment of COVID-19 registered internationally, 53 of which are being carried out in the U.S. For the U.S. trials, 5 are in Phase I, 28 are in Phase II, 21 are in Phase III, 6 are in Phase IV. Nine U.S. trials have been terminated, withdrawn, or suspended, and six have been completed.

Results from Individual Trials

Results from a double-blind, randomized, Phase IIb clinical trial indicate that dosing of chloroquine phosphate is an especially important consideration when treating COVID-19 patients, as higher dosing was associated with higher mortality risk (Borba et al., 2020). The study sought to evaluate the clinical course of 81 hospitalized patients in Manaus, Brazil who were either administered a high dose of chloroquine phosphate (600 mg twice daily for 10 days) or a low dose (450 mg once daily daily for 5 days except on the first day when two doses were administered) in conjunction with ceftriaxone and azithromycin. For all 81 patients tested, the case fatality rate was 13.5%, and the 95% confidence interval (6.9-23.0%) overlapped with that of two other major studies that used patients not receiving chloroquine phosphate. Therefore, the authors were not able to conclude that the drug was associated with reduced COVID-19 mortality. The researchers found elevated mortality in the patients treated with the higher dose, and once observed, all patients receiving the high dose treatment were immediately switched to the low dose treatment for the remainder of the trial.

On March 16, 2020, Raoult discussed results from a successful trial of hydroxychloroquine sulfate and azithromycin tested on 26 patients with COVID-19 infection from southern France (16 other COVID-19 patients were not administered the treatment). A daily 600 mg dose of hydroxychloroquine sulfate not only improved patients’ clinical symptoms and disease outcome, but after 6 days, only 25% of the patients of the patients receiving hydroxychloroquine sulfate remained contagious, whereas the patients receiving standard treatment remained contagious after the same time period (Gautret et al., 2020). The French government plans to scale this testing on COVID-19 patients in other hospitals in the country. On March 27, 2020, this group subsequently reported on a larger trial of 80 patients (6 of whom were included in the earlier report) treated with hydroxychloroquine sulfate and azithromycin. They noted rapid falls in nasopharyngeal viral load and improvement in clinical course with all but two patients being discharged from the ICU (Gautret et al., 2020). This was, however, an uncontrolled study, and we await the results of prospective large-scale randomized controlled trials, both for treatment of active disease with this regimen or its possible use in preventing infection in high-risk individuals (e.g. professionals working with COVID-19 patients).

On March 28, 2020, the FDA issued an Emergency Use Authorization (EUA) for the use of hydroxychloroquine sulfate and chloroquine phosphate for hospitalized COVID-19 patients. Magagnoli et al. (2020) have reported a large but retrospective study of 368 veterans hospitalized in U.S. Veterans Affairs (V.A.) Hospitals with COVID-19. Studied patients (17 women were not analyzed because of small numbers) were all male of median age 69, and 64% were African American. Patients received supportive treatment (no HC, 158), hydroxychloroquine (HC, 97), or hydroxychloroquine plus azithromycin (HC+AZ, 113) at the discretion of their care team. Primary endpoints for the analysis were death and the need for mechanical ventilation. Rates of death in the no HC, HC, and HC+AZ groups were 11.4%, 27.8%, and 22.1%, respectively. Rates of mechanical ventilation were 14.1%, 13.3%, and 6.9%, respectively. The authors concluded that their results showed no evidence of benefit for these drugs and that further use should await results of controlled prospective trials. It is important to note that the study was not randomized, and so the different death rates among the treatment groups do not indicate increased elevated risk of complication from drug treatment.

On March 30, 2020, Molina et al. published results of a small, non-randomized study of 11 COVID-19 patients in France that aimed to test the efficacy of the same hydroxychloroquine sulfate and azithromycin treatment that was administered by the group led by Gautret. By Day 5 of treatment, one patient had died and two had been transferred to the ICU. By Day 6, eight of the remaining ten patients still tested positive for the SARS-CoV-2 virus using RT-PCR, a result which stands in stark contrast to the viral clearance reported by Day 6 in the study conducted by Gautret et al. One of the surviving patients in Molina’s study had to discontinue hydroxychloroquine sulfate use because of a prolongation of the QT interval after treatment, from 405 ms before treatment to 460-470 ms after treatment, indicative of cardiac arrhythmia. The authors conclude that the use of hydroxychloroquine sulfate with azithromycin did not result in significant improvement in clinical outcome or in viral clearance for COVID-19 patients.

Another small study conducted in China on just 30 patients with COVID-19 suggests that these treatments may be less promising: five daily doses of 400 mg of hydroxychloroquine sulfate administered alongside standard treatment was found to be no more effective in treating the disease than standard treatment on its own (Chen et al., 2020). This result was evaluated by comparing the median time of hospitalization, the median time for fever reduction, and the results of CT scans evaluating pulmonary symptoms between the treatment and control groups. The researchers also tested the subjects for the presence of SARS-CoV-2 by pharyngeal swab, and found that by Day 7 of the study, 13 of the 15 patients receiving hydroxychloroquine sulfate treatment had no detectable viral RNA and 14 of the 15 patients receiving standard care tested negative for the virus. While these results may seem discouraging, it is again important to note that the study size was very small, which limits any broader conclusions from being drawn. In fact, rather than concluding that hydroxychloroquine treatment is not effective in treatment, the authors conclude that these preliminary results seem more indicative of an overall promising prognosis for all patients with COVID-19.

A randomized study of 62 COVID-19 patients at Renmin Hospital at Wuhan University aimed to evaluate the efficacy of hydroxychloroquine sulfate in the treatment of hospitalized patients with mild COVID-19. A group of 31 patients received standard treatment along with 400 mg doses of hydroxychloroquine sulfate administered daily for five days, while the other 31 received only standard treatment. For those patients with fever at the onset of the study, it took one day less on average for body temperature to return to normal for those receiving hydroxychloroquine sulfate (Chen, Z. et al., 2020). Furthermore, chest CTs of the subjects revealed that patients receiving hydroxychloroquine sulfate showed higher rates of improvement in pneumonia (25 of 31 vs. 17 of 31 in the control group). The authors also report that cough remission time was significantly improved in the group receiving treatment and that only two patients receiving hydroxychloroquine reported mild adverse reactions (rash or headache), while all four patients that progressed to severe disease were in the control group. These results may show some limited promise in the use of the drug, but the authors note that larger-scale randomized studies need to be conducted to draw any broader conclusions.

On July 15, 2020, Horby et al. posted pre-print preliminary results of a multi-center, randomized, controlled clinical trial concerning the outcome of hospitalized COVID-19 patients treated with hydroxychloroquine sulfate. The trial was part of the Randomized Evaluation of COVID-19 Therapy Program (RECOVERY) in the U.K.. A total of 4,716 hospitalized patients were selected for the study; 1,561 were randomly selected for treatment with hydroxychloroquine, and 3,155 received standard care without hydroxychloroquine treatment. Results indicated that patients receiving hydroxychloroquine were slightly less likely to be discharged from the hospital alive and were slightly more likely to require mechanical ventilation or die within 28 days of randomization. There was no significant increase in cardiac arrhythmia associated with hydroxychloroquine use. Approximately 26.8% of patients receiving hydroxychloroquine treatment died within 28 days of study initiation compared to 25.0% of patients in the control group; this difference was not statistically significant however. Use of hydroxychloroquine was associated with a slightly longer median hospitalization time of 16 days compared to a median time of 13 days for those in the control group. Overall, the authors conclude that the use of hydroxychloroquine appears to have no discernible benefit for hospitalized COVID-19 patients. These results persisted when only specific subgroups were considered, such as those based on sex, age, baseline predicted risk, among other factors.

Meta-Analyses

***Mehra et al. (2020) performed a meta-analysis of 96,032 COVID-19 patients across 671 hospitals across the globe to determine how the use of chloroquine phosphate or hydroxychloroquine sulfate with or without a macrolide affected clinical course and outcome. Patients receiving remdesivir treatment were excluded from the study as were patients receiving mechanical ventilation before treatment with chloroquine phosphate or hydroxychloroquine sulfate. For those patients in the treatment group, defined as the group of patients receiving chloroquine or hydroxychloroquine, only patients who began treatment within 48 hours of COVID-19 diagnosis were included in the study. These groups were compared to a control group, i.e. the group of patients not receiving chloroquine phosphate or hydroxychloroquine sulfate. Patients were evaluated for their degree of severity of the disease by their qSOFA score (a score below 1 is indicative of lower severity) and by oxygen saturation at baseline (a level below 94% is indicative of increased severity). After controlling for multiple cofounding factors, including sex, age, and comorbidities, the authors report that patients in the treatment group were associated with greater case fatality rates and with increased ventricular arhythmia during hospitalization, a condition which has been previously linked to chloroquine and hydroxychloroquine use. Table 3.2 lists the number of patients with specific baseline characteristics, the number of patients that developed ventricular arrhythmias, and the number of survivors and non-survivors for each treatment and control groups. Various clinical outcomes for each of these groups are also included.

On September 18, 2020, Axfors et al. released results of an international collaborative meta-analysis of randomized clinical trials that aimed to assess the survival rates of COVID-19 patients treated with hydroxychloroquine sulfate (HCQ) or chloroquine phosphate (CQ). After identifying potential trials that qualified for study, the researchers reached out to principal investigators and identified 26 trials for inclusion in the meta-analysis. Of these 26 trials, 16 had unpublished results, five had published results, and five had results available in pre-print form. The use of HCQ was evaluated in 24 of the trials (n = 7,659), while the use of CQ was evaluated in four of the trials (n = 307). Two of the studies looked at mortality outcomes in patients treated with HCQ vs. placebo or in patients treated with CQ vs. placebo (n = 63). For the 24 HCQ trials, 499 of the 3,020 patients treated with HCQ died, corresponding to a death rate of 16.5%, while 874 of the 4,639 patients in the control groups died, corresponding to a slightly elevated death rate of 18.8%. However, the 95% confidence interval for the odds ratio (OR) of the death rate of patients treated with HCQ to that of patients in the control group showed that there was no conclusive survival benefit from the use of HCQ (95% confidence interval for the OR was 0.99-1.18). For the published results from randomized clinical trials studying HCQ, there was a statistically significant increased harmful effect found in the use of HCQ in patients (95% CI for this OR was 1.07-1.13). Quite curiously, among the unpublished trials, there was no such statistically significant increased harmful effect found (The 95% OR was 0.71-1.30). For the four trials where the use of CQ was investigated, 18 of 160 patients treated with CQ died, corresponding to a death rate of 11%, while 12 of 146 patients in the respective control groups died, corresponding to a death rate of 8%. The 95% odds ratio for the death rates was 0.15-21.3, again showing that CQ had no added benefit for survival rates in COVID-19 patients.

Axfors et al. astutely highlight that for the HCQ trials, the predominant source of data came from the RECOVERY trial (see Results from Clinical Trials), which concluded no benefit for the use of HCQ in COVID-19 patients and instead showed a longer hospitalization period required for patients administered this treatment, as well as a higher risk of mechanical ventilation and death. Other published studies showed similar results. Meanwhile, studies that found no conclusive evidence for increased mortality with the use of HCQ were more likely to go unpublished. Therefore, the meta-analysis highlights an important bias that is observable from only considering the published studies alone. Null studies, which are well known to have a higher likelihood of going unpublished, must be taken into account to accurately assess the mortality risks for the use of any particular drug in the treatment of any disease.

Table 3.2: Clinical Outcome and Survival in COVID-19 Patients Treated with Chloroquine Phosphate (C), Hydroxychloroquine Sulfate (HC) with and without Macrolide (M) (Adapted from Mehra et al, 2020)
Control
C
C + M
HC
HC + M
Total
Subjects
81,144
(84.5%)
1,868
(2.0%)
3,783
(3.9%)
3,016
(3.1%)
6,221
(6.5%)
96,032
Baseline
qSOFA < 1
67,316 (84.8%)
1,530
(1.9%)
3,051
(3.8%)
2,477
(3.1%)
4,994
(6.3%)
79,368
Baseline
SPO2 < 94%
7,721
(82.9%)
209
(2.2%)
413
(4.4%)
323
(3.5%)
651
(7.0%)
9,317
De-Novo Ventricular Arrhythmia
226 (18.4%)
81
(6.6%)
236
(19.2%)
184
(15.0%)
502
(40.8%)
1,229
Mechanical Ventilation
6,278 (67.1%)
403
(4.3%)
814
(8.7%)
616
(6.6%)
1,243
(13.3%)
9,354
Mean ICU Stay [d]
2.6
4.3
4.9
4.3
4.7
2.9
Survivors
73,614 (86.3%)
1,561
(1.8%)
2,944
(3.4%)
2,473
(2.9%)
4,742
(5.6%)
85,344
Non-Survivors
7,530
(70.4%)
307
(2.9%)
839
(7.8%)
543
(5.1%)
1,479
(13.8%)
10,698


[Concerns over the source of the data presented in this paragraph and table have been recently expressed by the editors of the Lancet, and the conclusions of this work have been called into question (Servick et al., 2020). NB: The aforementioned papers in both the Lancet and NEJM have been withdrawn by the authors as of 6/4/2020.]***

This retraction of two high profile COVID-19 related papers, including the paper from which the data of Table 3.2 were collected, led to careful consideration of what had gone wrong with the generation and authorship of these two papers, their review, and the urgent decision to publish them, followed within days by widespread concern about their veracity and eventual retraction by the journals. This situation has been reviewed by Catherine Offord in The Scientist (1 October, 2020), with a review of the past rather checkered history of Surgisphere as well as Dr. Desai, the lead author of the studies. It appears that an initial intent to deceive followed by inattention to detail, such as review of primary data, by coauthors led to this clinical disaster which led to significant delays of several trials as well as further public doubt about the accuracy of medical science.

Unfortunately the retraction of the Surgisphere studies is far from unique. Retraction Watch (2020) keeps a running tally of retracted papers in a variety of fields, and as of September 16, 2020, had noted 33 retracted papers between February and September 2020, 3 temporarily retracted, and 2 reviewed with expressions of concern. The urgency of the pandemic is not an excuse for careless or self-serving publications. Continuing episodes such as this will only worsen an already worrisome public suspicion of science, with devastating impact (Horton, 2020).

Dexamethasone and Other Corticosteroids

The use of corticosteroids such as prednisone, methylprednisolone, or dexamethasone has also been considered for patients with COVID-19, particularly in the late stages of infection where a pattern of sustained pulmonary inflammation predominates and is often the cause of death. Such drugs are widely used in management of other inflammatory conditions, are available generically at low cost, and have well-established and modest toxicity profiles when used briefly.

Current Clinical Trials

As of September 26, 2020, there were 25 clinical trials studying the effect of dexamethasone in the treatment of COVID-19 registered internationally, three of which are/were being carried out in the U.S. For the U.S. trials, one is in Phase II, one is in Phase III, and one was a retrospective study completed on June 24, 2020.

Study Results

On June 16, 2020, investigators at Oxford University reported in a press release the preliminary results of the Randomized Evaluation of COVID-19 Therapy (RECOVERY). On July 17, 2020 these results were published in the New England Journal of Medicine. In this trial more than 11,500 patients with COVID-19 from 175 NHS hospitals in the U.K. were enrolled and randomized to a number of therapies. Accrual to the Dexamethasone arm (6 mg daily x 10 days given orally or by intravenous injection) was halted on June 8, 2020 by the trial steering committee because of evidence of benefit compared with patients receiving usual care. At this time 2,104 patients had been randomized to dexamethasone and 4,321 to usual care. In patients receiving usual care, 28-day mortality was 41% for patients requiring ventilation, 25% for patients requiring supplemental oxygen but not ventilation, and 13% for those requiring no ventilatory assistance. These rates were reduced in the dexamethasone group by one third (HR 0.65, CI 0.48-0.88, p = 0.0003) for the group requiring mechanical ventilation and by about one fifth (HR 0.80, CI 0.67-0.95, p = 0.0021) for patients on supplemental oxygen but not mechanical ventilation. Benefit was not seen for patients not requiring respiratory support (p = 0.14). The authors note that this would result in preventing one death for treatment of 8 patients on ventilators or 25 patients on oxygen.

These reported results with a reduction not merely in time to recovery, as has been reported with other agents such as Remdesivir, but in 28-day mortality, are encouraging. We await full details of the trial including patient population and management of the standard care group, as their 28-day mortality rates seem rather high. The ready availability of Dexamethasone, its well-defined and modest toxicities, and its low cost (30 4 mg tablets currently priced in the $15-25 USD) make it an appealing candidate if these results are substantiated and confirmed. It should also be noted that there are no data that Dexamethasone would be useful as a preventive agent or one for treating infected but asymptomatic patients, and more chronic use can result in toxicities including hyperglycemia, hypertension, aseptic necrosis of bone. Routine use of this agent is not indicated.

On September 2, 2020, Sterne reported for the WHO Rapid Evidence Appraisal for COVID-19 Therapies (REACT) Working Group results of a prospective meta-analysis pooling data from 7 randomized trials of corticosteroids (e.g. dexamethasone, hydrocortisone, or methylprednisolone) versus usual care or placebo in critically ill patients with COVID-19 (Sterne, 2020). The primary endpoint of the meta-analysis was 28-day all-cause mortality. Of 1,703 patients analyzed, 57% had been participants in the RECOVERY trial, the others came from 6 other trials. Dexamethasone was the most commonly used corticosteroid (1,282 patients) followed by hydrocortisone (374 patients) and methylprednisolone (47 patients). The summary OR was 0.66 (95% CI 0.63-0.82; P<0.001) for all-cause mortality favoring the administration of corticosteroids versus usual care or placebo. There was no indication of an increase in adverse events associated with these corticosteroid regimens. The magnitude of the benefits were similar for dexamethasone and hydrocortisone. These data provide further support for the use of corticosteroids in critically ill patients with COVID-19.

Famotidine

Famotidine (the active compound in Pepcid®) is a generic medication that is used to treat peptic ulcer disease and gastroesophageal reflux. It acts as an inhibitor of H2 receptors in parietal cells, blocking histamine from binding to the receptor, which would otherwise activate proton pumps to produce stomach acid. A Phase III clinical trial to study the potential of famotidine to treat COVID-19 was initiated on April 7, 2019, and its enrollment is expected to reach 1,170. The randomized, double-blind comparative trial will test the efficacy of treatment with hydroxychloroquine sulfate and intravenous famotidine (10 mg/mL concentration) as compared to treatment with hydroxychloroquine sulfate and placebo. The total daily dosage for patients in the treatment group will be 360 mg of famotidine per day for a maximum of 14 days.

Callahan, an infectious disease expert and physician, first called attention to the drug’s potential use as a treatment after an extensive review of Chinese medical records of COVID-19 patients, which showed that patients on the drug when compared to patients treating their heartburn with proton pump inhibitors, such as Omeprazole, were dying at a substantially lower rate. Freedberg et al. (2020) later conducted a retrospective study of 1,620 COVID-19 patients, of which 84 (5.1%) had received famotidine within 24 hours of admission. Use of famotidine was associated with reduced risk of death or intubation and with reduced risk of death specifically. Proton pump inhibitors, such as Omeprazole, did not exhibit the same association, however. Modeling of the SARS-CoV-2 papain-like protease, which enables viral replication, shows that famotidine may effectively bind to the protease, thereby inhibiting its action in viral replication (Borrell, 2020).

Favipiravir

Favipiravir (also known as Avigan, T-705, or Favilavir) is an antiviral drug produced in Japan by Toyama Chemical. The drug was originally developed for the treatment of influenza. The drug is an analog of the nitrogenous base guanine, and it has shown broad spectrum antiviral activity against a wide range of RNA viruses (Furuta et al., 2009). In vitro studies have revealed that the compound increases both the rate of specific nucleotide transversion mutations action in the influenza A H1N1 genome and the overall mutation frequency in the virus’s genome (Baranovich et al., 2013). Favipiravir has shown efficacy as an inhibitor of viral replication (possibly by inhibiting the action of RNA-Dependent RNA Polymerase, an enzyme required for the replication of an RNA-virus) in Ebola virus infection in a mouse model of the disease, where it reduced viral load, improved disease condition, and increased recovery rates to 100% for infected mice (Oestereich et al., 2014).

Two Chinese clinical trials to test for the efficacy of Favipiravir in treating COVID-19 were registered in early February (De Clerq, 2020). At a press conference on March 17, 2020, Zhang Xinmin, director of the National Center for Biotechnology Development in China, announced the successful results of two Favipiravir drug trials, one on 240 COVID-19 patients in Wuhan, China and the other on an 80 COVID-19 patient cohort in Shenzhen. Xinmin reported that in the Shenzhen trial, patients treated with Favipiravir (treatment group) tested negative after a median of 4 days after testing positive, while those treated with Lopinavir/Ritonavir (control group) tested negative after a median of 11 days. Furthermore, 72% of patients in the treatment group had fevers gone within 2 days compared to 26% of patients with the same fever recovery time in the control group. Chest X-ray revealed that 91% of patients in the treatment group showed improved lung conditions, compared to 62% in the control group. Xinmin recommended the use of the drug in treatment of COVID-19 patients. The Chinese government has granted approval to a Chinese firm to mass produce the drug for use in treatment against COVID-19 disease. These preliminary results must still be followed up with further trials and peer-review to verify the treatment efficacy of the drug.

Interleukin-6 Receptor Antagonists

Interleukin-6 receptor antagonists (tocilizumab, sarilumab, siltuximab) have been proposed as ways of treating the cytokine release syndromes, CRS (or cytokine storm syndrome, CSS) often seen in patients with severe COVID -19 infection (Moore, 2020). These agents were initially introduced and approved for treatment of rheumatologic conditions. Tocilizumab has been used successfully in managing the CRS which may be seen following CAR-T therapy. Xu (2020) reported experience in treating 21 patients with severe or critical COVID-19 infection with standard therapy (lopinavir, methylprednisolone, other symptom relievers and oxygen plus tocilizumab 400 mg in a single dose. They reported marked clinical improvement in temperature elevation and SaO2 within the first day, decline of previously elevated C-reactive protein, and improvement of chest CT scans. Nineteen of the 21 patients were successfully discharged from the hospital. This was a small and uncontrolled trial but the results in this high risk population are intriguing. A number of prospective randomized trials of IL-6 blocking agents (both tocilizumab and sarilumab) have been designed and should begin accrual shortly (Goldberg 2020). In centers also conducting treatment of patients with cancers with CAR-T therapy, careful consideration should be given to the use of tocilizumab both for CAR-T related CRS as well as for COVID-19 to ensure adequate supplies.

Somers et al. (2020) have reported in a non-peer-reviewed preprint in medRix their experience at the University of Michigan using tocilizumab (8 mg/kg) for treatment of mechanically-ventilated patients with COVID-19. This was a large observational study of 154 patients not eligible for other concurrent trials underway at the institution. Of these, 78 patients received tocilizumab, and 76 did not at physician discretion. Baseline clinical characteristics were similar although the tocilizumab patients were younger, less likely to have COPD, and had lower D-dimer levels at the time of intubation. In propensity score weighted models the tocilizumab treated patients had a lower hazard of death (HR 0.55 (95% CI 0.33-0.90)) and improved status on an ordinal outcome scale , with odds ratio of a 1-level increase 0.59 (CI 0.36-0.95). Tocilizumab treatment was associated with an increased proportion of superinfections (54% vs. 26% p < 0.001) but this did not appear to impact 28-day mortality. While observational, these data await the results of currently on-going randomized trials for confirmation. They may also prompt trials of the use of tocilizumab earlier in the course of the disease.

Lopinavir and Ritonavir

Lopinavir and Ritonavir are antiretrovirals that act as HIV-protease inhibitors. They are often administered in conjunction (higher dose Lopinavir with a lower dose of Ritonavir) as a therapy in HIV infection. The drug combination has been used to treat patients in South Korea and China with COVID-19 infection, where doctors have preliminarily observed that the treatment may be associated with decreased viral load and improved prognosis in patients. Rigorous clinical trials are currently underway to test the efficacy of these medications in treating COVID-19.

A randomized, controlled trial involving 199 severe COVID-19 hospitalized patients in China found that treatment with lopinavir-ritonavir resulted in no significant change in time to improvement of clinical symptoms but some improvement in mortality (Cao et al., 2020). Overall, median time to improvement was one day less for patients treated with the antiretrovirals than for patients assigned to standard treatment. Mortality at 28 days was slightly improved in the lopinavir-ritonavir treatment group (19.8% vs. 25.0% for standard care), and while adverse gastrointestinal events were more common in the antiretroviral treatment group, serious adverse effects were more common in patients receiving standard care (Cao et al., 2020).

On June 29, 2020, pre-print, preliminary results from the COVID-19 lopinavir/ritonavir RECOVERY trial were released by its chief investigators. The lopinavir/ritonavir arm of the trial had enrolled 1,596 COVID-19 patients, who were compared to a group of 3,376 patients receiving standard care. The preliminary results indicate that there was no significant difference in 28-day mortality, which was 22.1% for the group receiving lopinavir/ritonavir and 21.3% for the group receiving standard care. We await published results which detail more of the data collected, but the authors concluded no clinical benefit for COVID-19 patients receiving the treatment.

Remdesivir

Remdesivir (RDV; GS-5734™) is a single diastereomer monophosphoramidate prodrug designed for the intracellular delivery of a modified adenine nucleoside analog. Intracellularly, this prodrug is converted to its triphosphate active form, an adenine nucleotide analog that inhibits the viral RdRp (RNA Dependent RNA Polymerase). That is, the medication acts by inhibiting the viral RNA replication from its template strand, preventing viral replication. Remdesivir was developed by Gilead Sciences, Inc. initially to treat the Ebola virus infection, but over the past few years it has been reported to have antiviral activity against other filoviruses such as Marburg virus, several coronaviruses as well as certain pneumoviruses and paramyxoviruses. The FDA granted Remdesivir emergency use authorization on May 1, 2020. This announcement shortly followed the release of results from a study that showed a decreased time to recovery associated with the use of Remdesivir but it did not show a significant mortality benefit. We refer the reader to the following published reports: [doi:10.1021/acs.jmedchem.6b01594, 10.1038/nature17180, 10.3390/v11040326, 10.1073/pnas.1922083117][doi:10.1038/s41467-019-13940-6, 10.1074/jbc.AC120.013056, 10.1128/mBio.00221-18, 10.1016/j.antiviral.2019.104541], [doi:10.1038/srep43395].

Remdesivir has shown to inhibit SARS-CoV-1 viral replication and MERS-CoV replication in human lung epithelial cell culture (Sheahan et al., 2017). In vivo studies in a mouse model of SARS-CoV-1 infection also revealed that treatment with Remdesivir was associated with a significant reduction in viral load, as well as improved respiratory conditions and symptoms associated with SARS disease progression (Sheahan et al., 2017). The drug is currently undergoing several clinical trials to test its efficacy in treating patients with COVID-19 infection (see Current and Proposed Clinical Trials).

The Expanded Access Remdesivir is a clinical study funded by the U.S. Army Medical Research and Development Command. The population includes people with similar characteristics as the ACTT. Expanded access or “compassionate use” studies are intended to provide access outside of clinical trials to people with life-threatening conditions to a new investigational medical product that has not been approved by the FDA (www.clinicaltrials.gov Identifier: NCT04302766).

Since January 25, 2020, Gilead Sciences, Inc. has been accepting requests for the compassionate use of Remdesivir, which is only administered to patients with SARS-CoV-2 infection (confirmed by RT-PCR) and either an oxygen saturation level below 94% while breathing ambient air or in need of oxygen support. Treatment for approved cases included a first day dose of 200 mg of Remdesivir, followed by 100 mg daily doses of Remdesivir for the following 9 days. Grein et al. (2020) compiled results from 53 patients who underwent at least one dose of Remdesivir between January 25 and March 7, 2020 who were approved for compassionate use and were subsequently evaluated for progression of symptoms. There were originally 61 in the study, but 8 were not considered because they had missing information in clinical follow-up. The total number of patients requesting compassionate use of Remdesivir during this period is not specified. Of the 53 patients, 40 received the full 10 day treatment, 10 received the treatment for 6 of 10 days, and 3 received fewer than 6 days of treatment. At the initiation of treatment, 30 were receiving mechanical ventilation, and 4 were receiving ECMO. Patients’ clinical symptoms were reevaluated anywhere between 13 and 23 days after the initial dose of Remdesivir. These follow-ups resulted in 36 of the 53 patients demonstrating improvement in the category of oxygen support needed, while 8 of the 53 patients showed worsening. All of the patients breathing ambient air or receiving low-flow supplemental oxygen showed improvement. Moreover, 17 of the 30 patients receiving mechanical ventilation support were extubated, and 3 of 4 of the patients receiving ECMO no longer needed it. Seven of the 53 patients died, including 6 who were receiving invasive ventilation (mechanical ventilation or ECMO). Mortality risk was higher in patients over the age of 70 and with higher serum creatinine at the beginning of the study. Four patients had to discontinue Remdesivir early because of adverse reactions, which may have resulted from the use of the drug. The small number of subjects, limited followup, lack of pre-specified endpoints, and lack of a control group all limit any extrapolation of these results to the general population of severe COVID-19 patients. It should also be noted that the study was funded by Gilead Sciences, Inc., who also authored the initial draft of the report. However, the authors contend that when this group is compared to other cohorts studied during the same time period, the results indicate that Remdesivir may improve the clinical course of COVID-19 patients.

Preliminary results from some of the first COVID-19 patients recruited by the University of Chicago in a Phase III clinical trial were released on April 16 in a video recording obtained by STAT Medicine (NB: Not in pre-print form). Of the 125 recruited patients (113 with severe disease), all of whom were receiving daily infusions of Remdesivir, most were discharged from the hospital within six days, and only two had succumbed to the disease. The Phase III trial is a multi institutional effort that is expected to recruit 6,000 patients to assess the safety and antiviral activity of the drug, and additional results on the first 400 patients recruited in the Phase III trial are expected to be released imminently. Mullane, the infectious disease specialist overseeing the trial at the University of Chicago Hospital, exercised caution concerning these optimistic results, stating that the trial on severe patients had no control (placebo) group for comparison. She also noted that fevers fell quickly for the subjects treated and that some patients were taken off ventilators within a single day of treatment.

Preliminary results of this trial were released by Gilead Sciences, Inc. and by the National Institute for Allergy and Infectious Disease (NIAID) on April 29, 2020. On independent review by the Data Safety Monitoring Board (DSMB), Remdesivir was found to be significantly superior to placebo in time to recovery (being well enough for hospital discharge or returning to normal activity) (median 11 vs. 15 days, p < 0.001) and trended better for mortality (8.0% vs. 11.6%, p = 0.059). Full details, including breakdown of these differences by age, comorbid status, and severity of illness, as well as toxicities of the two arms, await publication. This is an encouraging signal, and is likely to lead to accelerated FDA approval of Remdesivir, but a more complete understanding of the role of Remdesivir should be based on consideration of all relevant trials.

On April 29, 2020, Wang, Y. et al. published results of a randomized, double-blind, placebo-controlled multicenter trial conducted in China that tested the efficacy of Remdesivir use in COVID-19 patients. Only 237 patients were recruited into the study, which was conducted between February 6, 2020 and March 12, 2020, during a time when China was experiencing a decline in reported cases. These data showed that of 158 patients randomized to Remdesivir and 79 control, there was no significant difference in time to clinical improvement (21 days for patients receiving Remdesivir and 23 days for the placebo group), mortality at 28 days (14% for patients receiving Remdesivir and 13% for patients in the placebo group), or time to virologic clearance. These results do not indicate a benefit for Remdesivir at these doses in this population of patients.

The results of the Adaptive COVID-19 Treatment Trial (ATTC-1) were published in peer-reviewed form in the New England Journal of Medicine on May 22, 2020 (Beigel et al. 2020). This was a randomized, placebo-controlled, double-blind study comparing intravenous remdesivir with placebo. Planned dose was 200 mg of remdesivir on Day 1 followed by 100 mg on Days 2-10 or until hospital discharge or death. Other treatments for patients were as per hospital policy, except that experimental or off label use of medications to treat COVID-19 were not permitted during the period of remdesivir administration or for two additional weeks, although they were allowed prior to enrollment in the trial. The initial primary endpoint of the trial was to compare the change of the two groups on an eight-category ordinal score of disease severity on Day 15 of the trial. This was changed on April 2, 2020 at a time when only 72 patients had been enrolled on the trial, to a comparison of time to recovery up to Day 29, as it became more evident that patients with COVID-19 infections often ran a longer course. The recommendation to change the primary endpoint was made by the trial statisticians who were not aware of treatment assignment or outcome of patients.

Inclusion criteria for the trial required evidence of SARS-CoV-2 infection by a positive RT-PCR test and symptoms of lower respiratory tract infection is indicated by radiographic infiltrates, SpO2 < 94% on room air, or requiring supplemental oxygen, mechanical ventilation, or extracorporeal membrane oxygenation. Exclusion criteria included elevation of ALT or AST >5 times ULN, impaired renal function or need for hemodialysis or hemofiltration, allergy, pregnancy, or anticipated discharge from the hospital within 72 hours from enrollment.

Enrollment was begun on February 21, 2020. Patients were stratified by location site and disease severity (moderate or severe) and randomized 1:1 between remdesivir and placebo. On April 27, 2020, the data safety and monitoring board reviewed results (a planned interim analysis) and recommended that the preliminary primary analysis report and mortality data be communicated to the trial team members from the National Institute of Allergy and Infectious Disease (NIAID). These initial results on time to recovery and mortality were then made public by the NIAID as well as in a press release by Gilead, the manufacturer of remdesivir.

A total of 1,107 patients were assessed for eligibility, of whom 1,063 were enrolled and randomized between remdesivir (n = 541) or placebo (n = 522). Demographic and disease characteristics appear well-balanced between the two groups. Analysis of the new primary outcome showed that the time to recovery was shorter in the remdesivir than the placebo group, 11 days vs. 15 days (RR 1.32, 95% CI 1.12-1.55; p < 0.001). Analysis of the initial primary endpoint (improvement in the ordinal disease score at Day 15) were also improved in the remdesivir group (ratio for improvement 1.50; 95% CI 1.18-1.91; p = 0.001). Mortality was less in the remdesivir group than in the control group, but this difference was not statistically significant (hazard ratio for death 0.70; 95% CI 0.47 to 1.04). Kaplan-Meir estimates for 14 day mortality were 7.1% and 11.9%, respectively. Serious adverse events were reported in 21.1% of the patients in the remdesivir group and 27.0% of the placebo group. The benefit in terms of reduction of days to recovery was most pronounced in those patients initially on supplemental oxygen (the largest subgroup of patients) as compared with those not requiring O2 or those requiring high-flow O2, non-invasive mechanical ventilation, intubation, or ECMO. This may have been due to the larger sample size of this group, as a test for interaction between baseline status and treatment benefit was not significant.

This study provides reasonable data for the safety of remdesivir and its ability to provide a modest benefit in time to recovery of patients with at least moderately severe COVID-19. It suggests that earlier use of remdesivir, before patients require mechanical ventilation, is better than waiting. It did not address nor should it be used to support the use of remdesivir in patients with earlier stages of disease, as a preventive strategy, or in patients known to be infected but not yet symptomatic who have been shown to be capable of spreading virus to others. Finally, this was an early publication of important data but without full follow-up of all entered patients and the authors acknowledge that full understanding of this trial will await such analysis.

Data is still emerging on Remdesivir from clinical trials that still offer challenges in interpretation because of the small size of patient samples or small numbers of randomized, double blind studies. It is therefore noteworthy that another nucleoside analog inhibitor, the orally bioavailable EIDD-2801, shows promise in animal studies for reducing virus titer, and improving lung function and weight control (Sheahan et al., 2020).

Other Pharmaceutical Drugs

Camostat mesylate is a serine protease inhibitor that acts to inhibit TMPRSS2, the serine protease that must modify the S protein of SARS-CoV-2 so that the virus can bind to ACE2 receptors and enter the cell. In vitro, the protease inhibitor has reduced the amount of SARs-CoV-2 infection of Calu-3 lung cells (Hoffman et al., 2020). It is a medication approved for the treatment of pancreatitis in Japan and is currently being tested on mice infected with SARS-CoV-2.

Work from the Baric Laboratory at the University of North Carolina (Sheahan et al., 2020) describes a very promising prophylactic and therapeutic oral antiviral candidate drug (a bioavailable ribonucleoside analog, β-D-N4-hydroxycytidine, NHC, EIDD-1931) that shows early efficacy in infected mice with reduce viral titers and improved pulmonary function.

The Netherlands, Australia, Greece, and U.K. will initiate clinical trials to test the use of the BCG vaccine as a treatment against COVID-19 infection, specifically in high-risk groups and in healthcare workers (de Vrieze, 2020). The vaccine was originally developed to prevent tuberculosis, a bacterial infection. However, over time, various researchers have observed that the vaccine may be effective against a broader range of diseases, suggesting that the vaccine may enhance innate and adaptive immunity. In 2018, a research team led by Netea was able to show that administration of the BCG vaccine was protective against infection with yellow fever, a viral disease. Furthermore, they found that monocytes, immune cells that produce antibodies that are part of the adaptive immune system, were specifically targeting the virus. They also found that administration of the vaccine enhanced production of interleukin-1β, a cytokine the researchers theorized may have a wider epigenetic effect that may modulate the monocytes. The results suggest that the vaccine may be protective against a broad range of viruses through this proposed mechanism.

Ivermectin is a drug used to treat various parasitic diseases and infestations. It is a relatively safe and inexpensive drug that is approved for human use by the FDA, and it has been demonstrated to inhibit viral replication, including for HIV-1, as well as for several RNA viruses. Caly et al. (2020) show that it also has potential to be repurposed as an antiviral agent in the treatment of SARS-CoV-2 infection. After 24 hours of treating a cell culture of Vero/hSLAM cells infected with SARS-CoV-2 with a single dose of 5µM ivermectin, the researchers tested for the presence of SARS-CoV-2 RNA using RT-PCR. When compared to the infected cells treated with DMSO (control condition), there was a 99.8% decrease in viral RNA detected. After 48 hours of treatment, the amount of SARS-CoV-2 RNA had decreased 5000-fold when compared to the cells in the control condition. This study demonstrates that the drug is a powerful SARS-CoV-2 inhibitor in vitro. Caly et al. (2020) propose that this inhibition may be explained by the drug’s possible inhibition of IMPɑ/β1-mediated translocation of viral proteins into the cell nucleus through the nuclear pore complex.

Acalabrutinib (ACP-196) is a bruton tyrosine kinase (BTK) inhibitor which has been approved by the FDA for treatment of patients with mantle cell lymphoma which has progressed after prior therapy or for patients with CLL/SLL and is marketed as CalquenceTM by AstraZeneca. In vitro studies and early clinical data indicated that the decrease in inflammation produced through BTK inhibition may reduce the severity of inflammation and COVID-19 associated respiratory distress. AstraZeneca will be initiating a global randomized Phase II trial (CALAVI) comparing best supportive care with or without acalabrutinib. This is scheduled to begin accrual in late April 2020 and accrue 428 patients.

Roschewski et al. (2020) released results in Sci. Immunol. of an off-label trial of acalabrutinib in 19 patients with COVID-19 who were hospitalized with evidence of hypoxemia (SpO2 94% or less on room air) and inflammation (CRP >10 mg/dl and/or ferritin > 500 ng/ml and/or lymphopenia (ALC <1000 cells/microliter.)) Of these, 11 patients were receiving supplemental oxygen, and 8 were receiving mechanical ventilation. Acalabrutinib was given orally or by enteric feeding tube twice-a-day for 10 days to patients on supplemental oxygen and for 14 days for patients on mechanical ventilation. Improvement on oxygen requirements and clinical status was seen in both the supplemental oxygen and intubated groups of patients, as were normalization of measures of inflammation such as CRP, elevation of IL-6 levels, and lymphopenia. At the completion of acalabrutinib treatment 8 of 11 (72.7%) of the patients in the supplemental oxygen cohort had been discharged on room air, and 4 of 8 (50%) of the intubated patients had been successfully extubated. No toxicities attributable to acalabrutinib were reported, and cardiac arrhythmias, grade 3 or higher bleeding, diarrhea, or opportunistic infections were not observed. While this is a small uncontrolled trial, the results are encouraging and support enrollment of patients in the aforementioned randomized trial.

Baricitinib (Olumiant®) is a drug approved for use in the U.S. as a treatment against moderate to severe rheumatoid arthritis, specifically for those patients that have not been able to manage their illness with tumor necrosis factor (TNF) antagonists. It acts as a janus kinase (JAK) inhibitor, inhibiting JAK1 or JAK2 in particular. By blocking the action of janus kinases, baricitinib interferes with the JAK-STAT signalling pathway, which in turn reduces cytokine signalling. With the assistance of BenevolentAI, Richardson et al. (2020) searched for drugs that might inhibit SARS-CoV-2 infection, and baricitinib was identified as a possible therapy that could hinder SARS-CoV-2 from infecting AT2 alveolar cells via endocytosis, which is mediated by AP2-Associated Protein Kinase 1 (AAPK1). Baricitinib was identified as one of six AAK1 inhibitors that antagonistically binds to the kinase with high affinity. It also can bind to the cyclin G-associated kinase, which also regulates endocytosis. The group singled out baricitinib as a relatively safe drug that has both antiviral and anti-inflammatory potential for the treatment of COVID-19. On May 8, 2020, NIAID announced that a combination treatment of remdesivir and baricitinib would undergo a randomized, double-blind, controlled clinical trial to evaluate its safety and efficacy in the treatment of severe COVID-19. More than 1,000 participants are expected to be enrolled in the trial. It is considered an arm of the NIAID’s Adaptive COVID-19 Treatment Trial.

Additional drugs for potential treatments in various stages of clinical trials include, but are not limited to, the following:

  1. Interferon alpha-2B (cytokine treatment for viral infection and cancers)
  2. Mepolizumab (anti-CD147 humanized antibody undergoing clinical trial in China)
  3. Methylprednisolone (corticosteroid)
  4. Tissue Plasminogen Activator (serine protease that catalyzes the conversion of plasminogen to plasmin used to break up blood clots, such as those arising from strokes)
  5. Umifenovir (Arbidol)

Current Vaccine Candidates

There are several aims for a successful vaccine candidate. Firstly, and perhaps most importantly, the vaccine should allow for an individual exposed to SARS-CoV-2 to generate a robust adaptive immune response upon exposure. In particular, exposure to specific regions of the virion should trigger a potent neutralizing antibody response from the host. It should also trigger a response from immunoglobulin antibodies, which can activate a pathway that culminates in the destruction of the virion itself. Furthermore, the vaccine should generate an adaptive response that is lasting in the individual, so that the vaccinated individual remains immune to the virus for a prolonged duration.

Others have questioned the adequacy of initial antibody response as an adequate surrogate for vaccine efficacy and duration of action (Hellerstein M, 2020). SARS-CoV-2 infection can result in the development of CD4 and CD8 T-cell responses to multiple epitopes in addition to the spike protein, with significant responses to membrane and nucleocapsid proteins. It will be important to assess the intensity and durability of the protective immunity induced by these various epitopes in comparing the results of natural infection and vaccines, those limited to a single protein (typically spike) as the immunogen. A vaccine which induces a strong but transient immune response is unlikely to be a clinical success and may worsen the public acceptance of vaccines in general.

Of course, assessing the safety of vaccine use is a chief concern, which is why adverse reactions should be closely studied and monitored during clinical trials. Of particular concern in the case of potential vaccinations is antibody-dependent enhancement (ADE). ADE occurs when antibodies specific to the virus can enhance the ability of a virion to gain entrance into a host cell. While ADE primarily occurs in cell cultures in vitro, it has been shown to occur in vivo in infections with the Dengue virus, feline coronaviruses such as SARS (suggesting that past coronaviral infections may also be a risk factor in severity of COVID-19), as well as HIV, where patients who are reinfected have worsened severity of symptoms. Because of ADE, effective vaccination, which generally leads to the production of antibodies, may in some cases also lead to increased severity of a viral infection. Thus, human trials are paramount for ensuring the safety of using such vaccines.

In mid-May 2020, the U.S. federal government unveiled a public-private partnership entitled Operation Warp Speed, which aims to accelerate the manufacturing, delivery, and development of COVID-19 vaccines as well as other therapeutics and diagnostics, with a stated goal of delivering 300 million doses of safe, effective COVID-19 vaccines by January 2021. The partnership has a budget of 10 billion USD and is a collaborative effort between the Center for Disease Control, the Food and Drug Administration, the National Institute of Health, the Biomedical Advanced Research and Development Authority, the Department of Defense, among others. As of June 2020, seven biomedical firms had been selected and prioritized for funding the development of their vaccine candidates. These firms include Johnson and Johnson, AstraZeneca-University of Oxford, Pfizer-BioNTech, Moderna, Merck, Vaxart, and Inovio. Of these, there are at least two promising vaccine candidates for COVID-19 being produced in the U.S. that are in clinical trials as of July 4, 2020: INO 4800, a DNA-based vaccine that codes for the SARS-CoV-2 spike protein and mRNA-1273, an mRNA vaccine that codes for the full-length spike protein of SARS-CoV-2. Other U.S.-produced vaccine candidates that have received funding from the project, such as the oral vaccine candidate produced by Vaxart, are in preclinical stages and are expected to enter clinical trials by late-summer 2020.

As of August 2020, there were a variety of different methods tested for vaccine delivery. Genetic vaccines, which include the vaccine candidates mRNA-1273, INO-4800, and BNT162, deliver mRNA or DNA that encode a protein of the SARS-CoV-2 virion that is known to trigger an immune response. Since previous studies have identified regions of the spike protein (particularly its receptor binding domain and the N-terminal portion of the protein) as potent regions of the SARS-CoV-2 epitope that trigger a robust antibody response (see SARS-CoV-2 Epitope), the genetic material delivered through these vaccines often codes for the spike protein itself or a modified version of the protein. Viral vector vaccines, such as ChAdOx1, Ad26-COV2-S, and Ad5-nCoV, deliver a virus that contains specific SARS-CoV-2 genes. These genes encode a protein or protein fragment that will activate an adaptive immune response. Generally, the protein encoded is the spike protein itself or a modified version. Protein-based vaccines, such as PittCoVac and NVX-CoV2373, deliver a SARS-CoV-2 protein or fragment, generally a modified version of the spike protein, directly in an attempt to trigger an adaptive immune response. Finally, some vaccines, such as CoronaVac, aim to deliver a weakened or inactivated version of the SARS-CoV-2 virus itself to trigger an adaptive immune response.

On August 11, 2020, Russia president Vladimir Putin announced that the Russian Health Ministry had officially registered a SARS-CoV-2 vaccine (see Sputnik V), an action tantamount to having received regulatory approval for public use of the vaccine. This approval was met with widespread criticism from the international scientific community as it was granted despite failure to complete any large-scale clinical trials for the vaccine. By that time, only two non-randomized Phase I/II clinical trials had officially been registered, each with 38 healthy participants between the ages of 18 and 60, and no results or preliminary results had yet been released. Despite having limited evidence, Murashko, the Russian Minister of Health, claimed in a press release from the same day that the vaccine was both safe and efficacious, conferring 2 years of immunity to SARS-CoV-2. Cohen of Science Insider reports that the registration certificate allows for the vaccine to be used on “a small number of citizens from vulnerable groups,” including medical staff and the elderly. The certificate also specifies that the vaccine cannot be used widely until January 1, 2021.

RNA Vaccine Candidates

mRNA-1273

The mRNA-1273 vaccine encodes the SARS-CoV-2 spike glycoprotein along with a transmembrane anchor and an intact S1-S2 cleavage site, together known as the S-2P antigen. The name for the antigen comes from its unique conformation, which is stabilized by two consecutive proline residues substituted in the central helix of the S2 subunit of the spike protein. The vaccine was developed by Moderna Therapeutics, a biotechnology company focused on vaccine delivery through messenger RNA (mRNA). To effectively deliver the nucleic acid contents, the mRNA that encodes the antigen is enveloped in a lipid nanoparticle capsule. The vaccine candidate is stable for 6 months at a temperature of -4℉ (-20C) and can be stored for 30 days with standard refrigeration temperatures. As of December 5, 2020, there are four clinical trials underway to test the efficacy, safety, and immunogenicity of the vaccine. All four trials will take place in the U.S.: one is a Phase I trial, one is in Phase II, one is in Phase III, and one is both a Phase II and III trial. On December 18, 2020, the FDA authorized the emergency use of the vaccine candidate. The following day, the CDC’s Advisory Committee on Immunization Practices voted unanimously to recommend mRNA-1273. On Wednesday, December 23, Health Canada officially approved the use of mRNA-1273 for the prevention of COVID-19, making Canada the first country to completely approve the vaccine candidate for its residents.

Phase I U.S. Trial

The phase I clinical trial of mRNA-1273 (Moderna) began at the Kaiser Permanente Washington Health Research Institute on March 16, 2020. The study originally enrolled a total of 80 healthy human subjects between the ages of 18 and 55, who would receive two intramuscular injections of either the vaccine or placebo on days 1 and 29 of the trial. Five different doses were tested (10 μg, 25 μg, 50 μg, 100 μg, and 250 μg) and the subjects will be evaluated regularly over the course of 12 months after the administration of the second injection. These evaluations are meant to test both the safety of the vaccine and immunogenicity of the vaccine (i.e. whether exposure to the antigen will trigger an immune response), the latter of which will be evaluated using immunoassay methods, namely IgG ELISA, which tests for the presence of IgG antibodies. In April, 2020, the trial was expanded to allow for 40 older adults above the age of 55 to participate.

One July 14, 2020, Jackson et al. reported initial results of the phase I trial that were published in the New England Journal of Medicine. Anti S-2P IgG antibody response was positively correlated with dosage: the geometric mean of immunosorbent assay antibody titers on Day 29 (28 days after the first dose) were 40,227 for the subjects receiving 25 μg dose, 109,209 for those receiving the 100 μg dose, and 213,526 for those receiving the 250 μg dose. On Day 57 (28 days after the second vaccination), the geometric means of the antibody titers had increased further: 299,751 for the group receiving the 25 μg dose, 782,719 for those receiving the 100 μg dose, and 1,192,154 for those receiving the 250 μg dose. Furthermore, all subjects had detectable IgG antibodies by two weeks after the first injection. Neutralizing antibodies were also assessed and found to be robust and positively correlated with dosage. The authors note that both binding and neutralizing antibody responses produced after the first injection were comparable to those found in convalescent patients. After the second injection, however, the median geometric mean antibody titer was in the upper quartile of corresponding antibody titers found in convalescent patients. Given the wide variation in antibody response in convalescent patients, this result supports the use of a two-dose administration of the vaccine. Subjects will continue to be tested for antibody response over the next year to assess the longevity and strength of such responses over time.

The trial also sought to assess adverse effects from the two-dose vaccine administration as well as how these effects may vary between dosage groups. More than half of the subjects experienced fatigue, chills, headache, muscle pain, and pain at the injection site, and systemic adverse effects were more common after the second dose, more pronouncedly so for the group receiving the highest dosage. No subjects experienced fever after the first injection, while 40% receiving the 100 μg dose did after the second injection, and 57% of those receiving the 250 μg dose reported this symptom after the second injection. Twenty-one percent of the group receiving the highest dosage experienced one or more serious adverse events. No participant reported any severe adverse effect after the first injection.

On September 29, 2020, Anderson et al. (2020) published results concerning the safety and immunogenicity of the vaccine candidate when administered to the 40 older participants enrolled. These 40 participants were stratified by age: 20 were between the ages of 56 and 70, and the remaining 20 were all 71 or older. All 40 participants received two doses of either 25 μg or 100 μg; one dose was administered on Day 1 and the other on Day 29 of the study. Local and systemic adverse effects were reported up through Day 57 on the study, which these results summarize, but participants will continue to be monitored for adverse events up to a year after the second dose administered. In this report, no serious local adverse events were reported in either age group for either dose level. The most commonly reported adverse events were headache, fatigues, muscle ache, chills, and pain at the injection site, and these effects were more commonly reported after the administration of the second dose and tended to appear on the day of or the day after the administration of a dose of the vaccine candidate. Most resolved quickly, but three patients experienced mild erythema (skin redness) that lasted for 5-7 days. One other participant reported mild muscle aches that appeared on Day 3, and the aches lasted for 5 days. Overall, there were only two systemic adverse effects that were classified as severe: one instance of fever in a participant in the 56-70 year age group after receiving a second 25 μg dose, and one instance of fatigue in a participant in the 71+ year age group after receiving a second 100 μg dose. A total of 71 unsolicited adverse events were reported, of which only 17 were thought to be related to the administration of the vaccine candidate. All but one of these adverse events was mild, but there was a report of one moderate adverse event, which was decreased appetite reported in a participant in the 56-70 year age group receiving the 25 μg dose. The number of adverse effects reports did increase with increasing dose. Overall however, both doses of the vaccine candidate were well tolerated in both age groups.

Binding antibody responses were more robust for the two older age groups than for the younger cohort previously reported on in July, 2020, and again, responses were dose-dependent. On Day 57, the GMT for binding IgG antibodies to S-2P receiving the 25 μg dose was 323,945 in the 56-70 year age group and 1,128,391 in the 71+ age group. For the 100 μg dose, the GMT for IgG antibodies specific to S-2P was even higher: 1,183,066 for the 56-70 year age group and 3,638,522 for the 71+ age group. All GMTs were considerably higher than the 138,901 GMT found in a panel of convalescent blood sera. Neutralizing antibodies were undetectable before administration of the vaccine candidate and steadily rose in a dose-dependent manner, but this time titer was independent of age. Neutralizing antibody responses to the 614D SARS-CoV-2 strain (the initial strain found in Wuhan but no longer the predominant 614G strain observed around the world) stayed at a high level for at least four weeks after the second dose (through the end of the period studied in this report). The researchers also tested for T cell immune responses to spike protein peptide sequences following administration of the vaccine candidate. They reported that participants aged 56-70 receiving the 25 μg dose and both age groups receiving the 100 μg dose showed a strong CD4+ response. CD8+ responses were markedly lower in all groups tested and were only observable at low levels after the second dose of the 100 μg dose administered for both age groups.

Phase II U.S. Trial

A phase II randomized, observer-blind, placebo-controlled trial was initiated on May 28, 2020, which aims to assess the safety and immunogenicity of two different doses of the vaccine in 600 participants over the age of 18. There are four experimental groups: one for adults aged 18-54 receiving a 50 μg dose of mRNA-1273, one for adults over the age of 55 receiving a 50 μg dose of mRNA-1273, one for adults aged 18-54 receiving a 100 μg dose of mRNA-1273, and one for adults over the age of 55 receiving a 100 μg dose of mRNA-1273. Each treatment group will be compared to a controlled group receiving saline placebo in lieu of a vaccine dose. Subjects will be monitored over the course of a year for any adverse effects; they will also be tested for SARS-CoV-2 neutralizing antibody titer during that time period to assess immunogenicity.

Phase II/III U.S. Adolescent Trial

On December 2, 2020, a Phase II/III randomized, observer-blind, placebo-controlled clinical trial to assess the efficacy, reactogenicity, and safety of mRNA-1273 in healthy adolescents between the ages of 12 and 18 was officially registered. It has a projected enrollment of 3,000 participants, who will be divided into two arms of the trial. Volunteers in the experimental group will receive two 100 μg intramuscular injections of the vaccine: one on Day 1 and one on Day 29 of the trial. Those in the placebo group will receive intramuscular injections of 0.9% saline solution on the same days of the trial. The study will track the number of participants with local and systemic adverse reactions up to 7 days after the first dose and up to 7 days after the second dose. Unsolicited and serious adverse events will also be noted, and the number of participants who reach a pre-defined minimum serum antibody level by Day 57 (four weeks after the second dose) will also be tracked. The geometric mean titer (GMT) values of the serum neutralizing antibody at Day 57 will also be compared to the Day 57 GMT of serum neutralizing antibody in the Phase III trial underway on adults (see Phase III U.S. Trial). Secondary outcome measures include recording the GMT values of SARS-CoV-2 Spike protein-specific neutralizing antibody and the SARS-CoV-2 specific neutralizing antibody on Days 1, 57, 209, and 394. The number of participants that develop a SARS-CoV-2 infection from Day 57 - 394 will also be tracked as will the number of participants that develop COVID-19 between Day 29 and Day 394.

Phase III U.S. Trial

The phase III randomized, observer-blind, placebo-controlled clinical trial (known as COVE) assessing the efficacy, immunogenicity, and safety of the vaccine candidate in adults over the age of 18 was initiated on July 27, 2020. It has an estimated enrollment of 30,000 subjects and will be conducted over the course of two years. Subjects are assigned to the experimental or placebo group. All participants in the experimental group will receive two intramuscular injections of 100 μg, one on Day 1 and one on Day 29 of the study. Subjects in the placebo group will receive two intramuscular injections of 0.9% saline solution, one on Day 1 and one on Day 29. The study aims to measure the number of participants that develop COVID-19 starting 14 days after the second injection, the number of patients with adverse effects or reactions, the geometric mean titers and geometric mean fold rise of SARS-CoV-2 specific neutralizing antibodies in participants receiving the mRNA-1273 vaccine, and the geometric mean titers and geometric mean fold rise of S-protein specific binding antibodies.

On November 15, 2020, an NIH-appointed independent data and safety monitoring board overseeing the Phase III trial of mRNA1273 shared preliminary trial data and analysis. Among volunteers enrolled in the study, 95 had developed COVID-19, of which 90 were in the placebo group and 5 were participants that received the vaccine candidate, suggesting a 94.5% efficacy rate, a result which was statistically significant. Out of the 95 that developed COVID-19, only 11 developed a severe form, and all 11 were in the placebo group. On November 30, 2020, Moderna announced in a press release that the vaccine candidate demonstrated 94.1% efficacy. By this point, 196 cases of COVID-19 were confirmed in the participants: 185 were in the placebo group and 11 received two doses of mRNA-1273. No severe cases were reported in the treatment group whereas 30 were reported in the placebo group.

BNT162

BNT162 is a set of four candidate vaccines developed by BioNTech in collaboration with Pfizer; the four potential vaccines are BNT162a1, BNT162b1, BNT162b2, and BNT162c1, all of which are mRNA-based and encode some portion or all of the SARS-CoV-2 spike protein. In particular, BNT162b1 encodes a trimerized version of the SARS-CoV-2 RBD, while BNT162b2 encodes a prefusion membrane-anchored full-length SARS-CoV-2 spike protein. A limiting factor to its distribution is that the vaccine candidate must be stored at a temperature of -70℃, or -94℉, in order to maintain viability for up to 6 months.

As of November 15, 2020, there are five registered clinical trials testing the vaccine candidate. A Phase I/II clinical study conducted in Germany was approved on April 22, 2020 by the Paul Ehrlich Institute. The trial aims to test the safety and efficacy of four candidates, and a range of doses from 1-100 μg were to be administered to determine optimal dosing regimens. On April April 29, 2020, a Phase I portion of a U.S. trial was initiated, and on July 27, 2020, Pfizer and BioNTech announced an expansion of the study to a Phase II/III study to be conducted in the U.S. and various South American and European countries, aiming to recruit 30,000 volunteers. This number was later expanded to 43,000 volunteers on September 12, 2020, with an enrollment of 43,538 participants reported in a November 9, 2020 press release. In August 2020, China registered a Phase I study of the BNT162b1 vaccine candidate.

On Monday, November 30, 2020, just a few weeks after announcement of promising preliminary Phase III trial results, Pfizer formally applied to the FDA for emergency use authorization of the BNT162b vaccine candidate. On Wednesday, December 2, 2020, the United Kingdom approved the vaccine candidate for emergency use. On December 11, 2020, The U.S. FDA authorized the vaccine candidate for emergency use, the first ever granted to a COVID-19 vaccine candidate in the United States. Pfizer currently has an agreement with the U.S. to supply 100 million doses by March, 2021; all shots are to be free to the public. As of December 21, 2020, three countries have granted the vaccine full approval: Bahrain, Saudi Arabia, and Canada.

Phase I/II/III International Trial

On April 29, 2020, the Phase I portion of a randomized, placebo-controlled, observer-blind Phase I/II/III global trial was launched. The purpose of the first phase was to determine if there was any preferred candidate vaccine to use in further trials and, if so, to determine its optimal dosing regimen. Specifically, the safety and immunogenicity results from the use of the two candidates BNT162b1 and BNT162b2 were compared. Ultimately, BNT162b2 was selected for use in the latter phases of the clinical trial because of a more favorable set of adverse reactions reported.

On July 1, 2020, Mulligan et al. published initial results from the Phase I study on vaccine candidate BNT162b1, a lipid, nucleoside-modified mRNA vaccine that encodes the SARS-CoV-2 spike protein receptor binding domain (RBD). The nucleoside modification involves the usage of 1-methylpseudouridine, which has been shown previously to increase mRNA translation rates in vivo (Kariko et al., 2008). Forty-five participants were randomized and vaccinated: 12 subjects received a dose of 10 μg on Days 0 and 21, 12 received a dose of 30 μg on Days 0 and 21, 12 received a single dose of 100 μg on Day 0, and nine received placebo. Adverse effects, which included chills, fever, muscle pain, among others, increased with dose level and were reported more frequently after second doses. Severe adverse effects were reported in two participants and included elevated fever and sleep disturbances following vaccination. IgG antibody concentrations specific to the RBD, as well as neutralizing SARS-CoV-2 antibody concentrations increased with dose and also increased after a second dose. Furthermore, the geometric mean for neutralizing antibody titers was 1.8-2.8 times greater than that of those found in a group of tested COVID-19 convalescent human sera.

On August 28, 2020, Walsh et al. published further preliminary results of this initial phase, where data supporting the use of BNT162b2 for future phases of the study were highlighted. The study used 13 groups of 15 participants (n = 195) where 12 received the vaccine and 3 received placebo; each group had a unique combination of vaccine used (only BNT162b1 and BNT162b2 were tested), vaccine dose, and age range of participants tested. Doses of either vaccine were 10 μg, 20 μg, or 30 μg, all delivered through two doses, administered 3 weeks apart. Younger participants between the ages of 18 and 55 were randomized and grouped together; they were kept separate from older participants between the age of 65 and 85, who participated in their own groups. Younger participants reported mild to moderate local reactions, mostly pain at the injection site, and reactions were more common after the second dose. Both local reactions were milder in older patients. Local reactions were fairly similar between the groups receiving BNT162b1 and BNT162b2. However, the frequency and severity of systemic adverse reactions were much lower in participants receiving BNT162b2. For younger participants receiving BNT162b1, 75% reported a fever over 38℃ after the second dose of 30 μg. For older participants receiving the same vaccine, reports of fever were fewer, with only 33% reporting fever over 38℃ after the second dose. However, the frequency of patients reporting fever was significantly lower in groups receiving BNT172b2: only 17% of younger participants and 8% of older participants reported a fever over 38℃ after the second dose. Moreover, no severe systemic adverse events were reported in older participants receiving the vaccine. The frequency of local and systemic adverse events increased with increasing dose, were higher after the second dose, and were all temporary. The immunogenic response elicited from vaccination by both vaccine candidates was robust and similar. Generally, responses were lower in older adults. Geometric mean titer of neutralizing antibodies for both vaccine candidates measured in older participants a week after the second dose ranged from 1.1-1.6 the geometric mean titer found in convalescent blood plasma. For younger participants, the geometric mean titer of neutralizing antibodies was 2.8-3.8 times the level found in convalescent blood plasma. Based on these results, latter phases of the trial will proceed in testing the BNT162b2 vaccine at a 30 μg dose level, a recommendation spurred by the dramatic reduction in adverse events reported with the use of BNT162b2 over BNT162b1.

On November 9, 2020, Pfizer and BioNTech announced preliminary results testing the safety and efficacy of BNT162b2 from the Phase III portion of the study in a press release. They reported that for a total of 38,955 healthy participants with no prior SARS-CoV-2 infection who had received two doses of BNT162b2, the vaccine candidate was over 90% effective in preventing COVID-19 disease at Day 28 of the study, 7 days after the administration of a second dose. By November 8, 2020, a total of 94 trial participants had been diagnosed with COVID-19 and no serious adverse effects from the administration of the two doses had been reported in participants. By November 18, 2020, a total of 170 participants had been diagnosed with COVID-19: 162 were in the placebo group vs. 8 participants that received the vaccine, suggesting that the vaccine candidate was 95% effective. Furthermore, BNT162b was 94% effective in preventing COVID-19 in adults over the age of 65. As of November 18, 2020, a total of 43,538 participants have been enrolled in the study. No serious grade 3 events were reported in frequencies over 2% except for 3.8% of participants reporting fatigue and 2.0% percent of participants reporting headaches.

Phase I/II German Trial

On April 23, 2020, an interventional, non-randomized, dose-escalation Phase I/II trial to test the safety and immunogenicity of the BNT162a1, BNT162b1, BNT162b2, and BNT162c1 vaccines was initiated. The trial recruited 456 healthy participants and included two parts: Part A was an initial phase with the purpose of testing dose escalation and de-escalation in participants aged 18-55 and in older participants between the ages of 56 and 85, and Part B was intended to study larger cohorts of participants using dose levels that were determined from the analysis of data collected from Part A.

On Jul 20, 2020, Sahin et al. reported preliminary results from the phase I/II trial clinical trial testing antibody and T-cell responses in healthy adults aged 18-55. A total of 48 subjects were administered two doses of BNT162b1: 12 received doses of 1 µg, 12 received doses of 10 µg, 12 received doses of 30 µg, and 12 received doses of 50 µg. These doses were administered on Day 1 and Day 22 of the study. An additional 12 subjects received one single dose of 60 µg on Day 1 of the study. No serious adverse effects were reported among the subjects. Most adverse events were mild, which included fatigue, headache, and pain at the injection site, while some moderate events included fever, chills, and muscle pain. Concentrations of RBD-specific IgG antibodies and SARS-Cov-2 neutralizing antibodies were measured at the beginning of the study, and on Days 7 and 21, to measure the immunogenic effect of the first dose. For those subjects that received a second dose, antibody concentrations were also measured on Days 29 and 43 of the study. The geometric mean concentrations of RBD-specific IgG antibodies increased with dosage, and on Day 21 of the study were in the range of 265-1672 U/mL. For those patients receiving a second dose, the geometric mean concentrations had increased to 2,015-25,006 U/mL on Day 29, which changed to 3,920-18,289 U/mL by Day 43. For the group that received one single 60 µg dose, the geometric mean concentration of RBD-specific IgG was 1,058 U/mL, indicating a booster dose was more effective at eliciting a robust immunogenic response. These concentrations were all significantly higher than the concentration of the same antibody found in samples taken from 38 convalescent COVID-19 patients, which had a geometric mean concentration of 602 U/mL Geometric mean titers of SARS-CoV-2 neutralizing antibodies were observed to follow similar trends, generating robust immunogenic responses that exceeded those found in convalescent patients. Furthermore, RBD-specific CD4+ T cell responses increased with increasing RBD-specific IgG and SARS-CoV-2 antibody concentrations.

DNA Vaccine Candidates

INO-4800

INO-4800, a DNA-based vaccine candidate that codes for the SARS-CoV-2 spike protein, entered a non-randomized, open-label, phase I clinical trial on April 7, 2020. The trial aims to evaluate the safety and immunogenicity in a small sample of 120 healthy human participants. Participants were divided into three treatment groups with 40 participants each; each group was delivered a different dose of the vaccine: Each participant in Group 1 received one intradermal injection of 1.0 mg of the vaccine on Day 0 and Week 4 of the study, participants in Group 2 received two 1.0 mg intradermal injections on Day 0 and Week 4 (a total dose of 2.0 mg per dosing visit), and participants in Group 3 received one 0.5 mg intradermal injections of Day 0 and Week 4 of the study. Formal results of the trial are expected to be published in July 2020.

Smith et al. (2020) published results demonstrating the INO-4800’s potential efficacy in both mice and guinea pigs receiving the vaccine. In the sera of mice vaccinated, IgG antibodies showed binding to the SARS-CoV-2 spike protein, a response which was considerably diminished in the presence of SARS-CoV-1 spike protein. Mice vaccinated also showed markedly elevated concentrations of SARS-CoV-2 neutralizing antibodies when compared to controls. Similar results were demonstrated in guinea pigs injected with the vaccine candidate. Furthermore, sera from both mice and guinea pigs inoculated with INO-4800 inhibited binding of the SARS-CoV-2 spike protein to the ACE2 receptor.

Viral Vector-Based Vaccine Candidates

Ad5-nCoV

On March 16, 2020, CanSino Biologic’s Recombinant Novel Coronavirus Vaccine, Ad5-nCoV (a recombinant adenovirus Type-5 vectored vaccine), which expresses the full-length SARS-CoV-2 spike glycoprotein, became the first Chinese vaccine candidate to receive approval to begin a Phase I clinical trial, becoming one of the first vaccine candidates in the world to be tested on humans. The vaccine uses a replication-defective adenovirus that encodes the SARS-CoV-2 spike protein, which, when transcribed and translated, is intended to generate an immunogenic host response. The Academy of Military Medical Sciences was the sponsor of the Phase I trial, which took place in Wuhan, China. On April 10, 2020, a Phase II trial was registered in China, and after the release of promising preliminary results from these early trials, China’s Central Military Commission approved the use of the vaccine on June 25, 2020 for a period of up to one year. As of September 7, 2020, the vaccine candidate is restricted for use by the Chinese military only.

International trials outside of China have also been pursued during this time. On May 16, 2020, Justin Trudeau, the prime minister of Canada, announced that Health Canada had approved the vaccine for human trials and had initiated a Phase I/II clinical study to be carried out in Halifax, Nova Scotia, sponsored by the Canadian Center for Vaccinology at Dalhousie University. However, news reports dating back to mid-July, 2020 began to emerge stating that the vaccine candidate was not approved by Chinese customs to export to Canada, and on August 27, 2020, Canada officially terminated the trial. Nevertheless, a larger scale international trial was registered on August 26, 2020, this time a global, multicenter Phase III clinical trial. The study endeavors to recruit 40,000 volunteers from Saudi Arabia, Russia, Mexico, and Pakistan, among other countries.

Phase I Chinese Trial

On March 18, 2020, Ad5-nCoV was officially registered to be tested in a non-randomized Phase I trial that aimed to assess the safety and immunogenicity of various dosages of the vaccine. Study participants were sequentially enrolled into one of three treatment groups. A total of 108 healthy adults between the ages of 18 and 60 were to receive intramuscular injections of the vaccine into the deltoid muscle, 36 of them receiving a low dose (5 x 1010 viral particles), 36 receiving a medium dose (1011 viral particles), and 36 receiving a high dose (1.5 x 1011 viral particles). The study subjects were evaluated for adverse events and immunogenicity on specific days following vaccination during a 6-month period. Persistence of antibodies and cellular immunity would also be tested over the same 6-month period.

On May 22, 2020, initial results of the Ad5-NCoV Phase I trial were published in the Lancet (Zhu F., et al.). At least one adverse reaction was reported in the first seven days after vaccination for 30 subjects that received the low dose, 30 subjects that received the medium dose, and for 27 subjects that received the high dose of the vaccine. Pain at the injection site was the most common localized adverse effect, and systemic adverse reactions were also frequently reported, including 46% of participants reporting fever, 44% reporting fatigues, and 39% reporting headache. No serious adverse effects were reported within 28 days after the vaccination. Overall, the authors concluded that the vaccine was tolerated in all dosage groups but more severe reactions were observed in the highest dose group, so the authors recommend the low and middle dosages be further assessed in the Phase II clinical trial.

Fourteen days after vaccination, all participants’ blood sera showed rapid antibody response to the receptor binding domain of the spike protein. During the 28-day duration of the study, titers of both SARS-CoV-2 neutralizing antibodies and antibodies specific to the spike protein RBD peaked on Day 28, and antibody titers were positively correlated with dosage. More specifically, on Day 28, the geometric mean titer (GMT) of RBD specific IgG antibodies was 1,445.8 in the high-dose group, 806.0 in the middle-dose group, and 615.8 in the low-dose group. Neutralizing antibody titers were all negative by Day 0 for all groups, rose somewhat by Day 14, and peaked by Day 28. By Day 28, the GMT of neutralizing antibody titer for 34.0 for the high-dose group, 16.2 for the middle-dose group, and 14.5 for the low-dose group, again demonstrating that neutralizing antibody titer was positively correlated with dose. T cell responses were also significantly higher in the high-dose group than in the low-dose group. Furthermore, interferon-γ was detected from CD4+ and CD8+ cells on Days 14 and 28 after vaccination in all dose groups.

Phase II Chinese Trial

On April 10, 2020, a Phase II, randomized, double-blind, and placebo-controlled clinical trial of the use of the vaccine in 508 healthy adult subjects (who had never had SARS-CoV-2 infection) was officially registered in China. The phase II trial aimed to evaluate the immunogenicity and safety of the vaccine when used in participants receiving two different doses. The placebo-control group was comprised of 126 individuals who received a 1.0 mL intramuscular injection in the deltoid muscle on Day 0. The high-dose group was made up of 253 individuals who received 1.0 x 1011 viral particles administered in a 1.0 mL intramuscular injection on Day 0. The low-dose group was made up of 129 individuals who received 5 x 1010 viral particles in a 1.0 mL intramuscular injection on Day 0. Levels of Anti-SARS-CoV-2 spike IgG and neutralizing antibody response were assessed throughout the course of the 6-month study. The occurrence of adverse reactions were also monitored throughout the study.

On July 20, 2020, Zhu F. et al. released preliminary results of the Phase II clinical trial. Blood samples were collected from participants on Days 0, 14, and 28 to assess the vaccine’s immunogenicity. Starting on Day 14, RBD-specific IgG antibodies were detected in the participants receiving the vaccine. The high-dose group had a GMT of 94.5 and the low-dose group had a GMT of 85.1 by this point. By Day 28, the RBD-specific IgG GMTs had risen even further, at 656.5 in the high-dose group and at 571.0 in the low-dose group. Subjects in the placebo group showed no increase in antibodies from baseline. Significant neutralizing antibody response to live virus was induced in blood samples collected from subjects on Day 28. The GMT of neutralizing antibodies by this point was 19.5 for the high-dose group and 18.3 in the low-dose group. Baseline T cell responses were negative in 99% of participants, but by Day 28, 90% of the high-dose group and 88% of the low-dose group showed spike-specific IFN-γ responses, a response mediated by T cells.

Participants were also observed for adverse reactions within the first 14 days after vaccination and in the first 28 days after vaccination. During the first 14 days after vaccination, 72% of the high-dose participants, 74% of the low-dose participants, and 37% of the participants in the placebo group reported at least one adverse reaction. The most common local reaction was pain at the injection site (57% for the high-dose group and 56% for the lower-dose group), and the most common systemic reactions reported were fatigue (42% in the high-dose group vs. 34% in the low-dose group), fever (32% in the high-dose group and 16% in the low-dose group), and headache (29% in the high-dose group and 28% in the low-dose group). Most reactions were mild or moderate, but a total of 24 (9%) of the participants receiving the high-dose reported severe adverse reactions, a percentage that was significantly higher than for the low-dose group. The severe adverse reactions were most commonly fever, and all resolved within 72-96 hours without medication. The authors note that pre-existing Ad5 immunity was correlated with significantly lower occurrence of fever after vaccination.

Phase III Global Trial

On August 26, 2020, a double-blind, placebo-controlled, randomized, global Phase III trial of Ad5-NCoV was registered officially. The estimated enrollment of the study is currently 40,000, with half of participants assigned to the placebo group and half of participants receiving a single, intramuscular dose of the Ad5-NCoV vaccine. Study participants will all be adults over the age of 18. The study aims to assess the safety, immunogenicity, and efficacy of the vaccine.

AZD1222 (ChAdOx1 nCoV-19)

AZD1222, previously known as ChAdOx1 nCoV-19, is a SARS-CoV-2 vaccine candidate that uses a chimpanzee adenovirus, ChadOx1 (Chimpanzee adenovirus Oxford 1), which expresses the SARS-CoV-2 spike protein. ChadOx1 has previously been used as an adenovirus vectored vaccine candidate for other human coronaviruses, such as MERS-CoV and SARS-CoV-2. In particular, the MERS-CoV vaccine candidate ChadOx1-MERS, which encodes the MERS-CoV spike protein in the same simian adenovirus vector, was shown to both protect non-human primates from MERS-CoV disease and be safe and well tolerated in a previous Phase I clinical trial at three different doses. At the highest dose tested, the vaccine candidate produced a strong immunogenic effect against MERS-CoV a month after vaccination.

ChadOx1 nCoV-19 was originally developed at the University of Oxford Jenner Institute, and on April 30, 2020, AstraZeneca entered into partnership with Oxford to develop and distribute the vaccine. Three weeks later, the pharmaceutical company received over a billion dollars in funds from the US Biomedical Advanced Research and Development Authority (BARDA) for the development, production, and distribution of the vaccine. On August 14, AstraZeneca entered into an agreement with the European Commission to supply up to 400 million doses of the AZD1222 vaccine at no profit during the pandemic, allowing for EU member states to access the vaccine, provided it receives regulatory approval. As of November 27, 2020, there are seven clinical trials underway for the study of the vaccine candidate (of which one was suspended), with one Phase III trial initiated in the U.S. on August 31, 2020, supported in part by the public-private partnership Operation Warp Speed and another Phase III trial initiated in Brazil on June 20, 2020.

According to a STAT News report, on Tuesday, September 8, 2020, Soriot, CEO of AstraZeneca, shared with investors on a private conference call that the Phase III clinical trial in the U.S. had been halted because of a serious adverse reaction reported in a participant receiving the vaccine. The participant may have experienced this condition due to an unrelated condition however, but since the individual received the vaccine, the trial has been halted for safety reasons. On September 9, the University of Oxford announced that enrollment in the international Phase II/III trial had also been temporarily halted. On September 12, 2020, the Phase II/III trials were resumed after Medicines Health Regulatory Authority officially confirmed that it was safe to do so. The FDA granted authorization for the restart of the Phase III I.S. trial on October 23, 2020, claiming it was safe to do so.

Pre-Clinical Trial Results

Munster et al. (2020) demonstrate a strong immunogenic response to the ChAdOx1 nCoV-2019 potential vaccine in mice and in rhesus macaques. IgG serum titers for the S1 and S2 subunits of the SARS-CoV-2 spike protein were compared between controls and mice that received the potential vaccine. Control mice had below detectable levels of the antibodies whereas all mice that received the potential vaccine had detectable titers. The same result was found when comparing the titers of viral neutralizing antibodies between the two groups. Furthermore, the authors report a strong Th1 (T-helper or CD4+ cell) response post vaccination. The authors also report a strong immunogenic response in six rhesus macaques that received ChAdOx1 nCoV-2019. Spike specific antibodies were detected in these animals within 14 days of vaccination, and virus neutralizing antibodies were detected in all 6 animals receiving the vaccine versus the detection of no virus neutralizing antibodies in 3 control animals. All nine animals were challenged with SARS-CoV-2 20 days post-vaccination, and control animals fared worse clinically, while no animals that received ChAdOx1 nCoV-2019 showed signs of pneumonia. The authors also report that the animals receiving the vaccine demonstrated a significantly lower viral load detected in their bronchoalveolar lavage fluid and respiratory tract tissue compared to control animals. Munster at al. report no evidence of antibody-dependent enhancement post vaccination for the six animals studied.

Graham et al. (2020) tested the immunogenic effect of a second dose of ChadOx1 nCoV-2019 in mice and pigs and compared it to the antigen-specific antibody response derived from a single dose in these animals. A single dose of the vaccine was enough to produce an antigen-specific and T-cell response in both mice and pigs. However, a secondary booster immunisation increased the overall SARS-CoV-2 neutralizing titers found in both animals, but did so more pronouncedly in pigs.

Phase I/II U.K. Trial

In March 2020, The Jenner Institute of the University of Oxford, the King Abdullah International Medical Research Centre (KAIMRC), and Vaccitech announced a Phase I/II, single-blinded, randomized, placebo-controlled, multi-center human trial (NCT04324606) to study the efficacy, toxicity, and immunogenicity of ChAdOx1 nCoV-19 against COVID-19 in approximately 1,112 healthy U.K. adults aged 18-55. Volunteers are divided into four different groups, each of which will either receive the ChAdOx1 nCoV-19 or the MenACWY vaccine intramuscularly. MenACWY is a vaccine that protects against 4 strains of the meningococcal bacteria―A, C, W and Y and will act as an active comparator in this clinical trial study. Volunteers will be blinded and will not know if they have received the ChAdOx1 nCoV-19 or the MenACWY vaccines. The experimental groups 1, 2 and 4, will receive a single dose of 5 × 1010 viral particles (vp) of ChAdOx1 nCoV-19. Some subjects in Group 2 however, will receive two different doses, a single dose of 5 × 1010 vp of ChAdOx1 nCoV-19 followed by a boost dose of 2.5 × 1010 vp of ChAdOx1 nCoV-1. The efficacy of the candidate ChAdOx1 nCoV-19 against COVID-19 will be assessed within a 6 month time frame by counting the virologically confirmed PCR positive symptomatic cases, whereas the safety of the vaccine will be tested by measuring the occurrence of serious adverse events (SAEs).

On July 20, 2020, Folegatti et al. published preliminary results of the trial, which enrolled a total of 1,077 participants during the 28-day period from April 23, 2020 to May 21, 2020. Half (n = 533) of the subjects received ChadOx1 nCoV-19 vaccine candidate at a dosage of 5 × 1010 vp, and half (n = 534) were in the placebo group, receiving the MenACWY vaccine candidate instead. Additionally, ten of the subjects that received ChadOx1 nCoV-19 received a follow-up dose of 5 × 1010 vp ChadOx1 nCoV-2019 28 days after the initial vaccination. Of these, 67% of subjects receiving ChadOx1 nCoV-2019 reported pain after vaccination compared to 38% of subjects receiving MenACWY. For the group receiving ChadOx1 nCoV-2019, 70% reported fatigue (48% in the MenACWY group), 68% reported headaches (41% in the MenACWY group), 60% reported muscle ache, 56% reported chills, and 51% felt feverish; 18% of the subjects in the treatment group recorded a fever over 38℃, compared to less than 1% in the placebo group. The severity of the adverse reactions tended to peak on Day 1 after the vaccination. Only one serious adverse reaction was noted, and it occurred in a subject receiving MenACWY who subsequently developed hemolytic anemia. Overall, adverse reactions were more common in the experimental group, but these preliminary safety results indicate that larger scale trials could be recommended. For the group receiving ChadOx1 nCoV-2019, anti-spike IgG antibodies peaked at 28 days (median of 157 ELISA units) after the administration of the vaccine candidate and stayed elevated until Day 56 with a median of 119 ELISA units. For those subjects that received two doses, a median of 639 ELISA units was recorded at Day 56 of the study. Spike-specific T-cell responses peaked on Day 14. Furthermore, 91% of subjects receiving a single dose of the vaccine candidate were able to achieve 80% virus neutralization compared to 100% of subjects who received two doses. Using a Marburg VN assay, the researchers also demonstrated that 62% of subjects receiving a single dose of the vaccine were able to induce complete inhibition of the cytopathic effect of SARS-CoV-2 by Day 56, compared to 100% that received two doses. Overall, the vaccine showed a strong immunogenic effect that was boosted with a secondary dose.

Phase II/III U.K. Trial

On May 26, 2020, a randomized Phase II/III interventional clinical trial of ChadOx1 nCoV-19 using participant masking was initiated, which aims to assess the safety and efficacy of the vaccine candidate in eleven study groups that consist of approximately 12,230 healthy participants. Groups 1, 7, and 9 consist of adults between the ages of 56 and 60; groups 2, 8, and 10 consist of adults over the age of 70; group 3 consists of children between the ages of 5 and 12; groups 4, 5, 6, and 11 consist of adults between the ages of 18 and 55. Depending on which group a subject is assigned to, the subjects in the treatment group will receive a single dose, two doses spaced several weeks apart, or a single dose followed by a smaller secondary dose. Dosages are determined by the group to which a subject is assigned. These subjects will be compared to subjects receiving one or two doses of MenACWY, which comprise the control group. The subjects will undergo follow-up for 1 year after vaccination to assess for safety, adverse reactions, and immunogenicity of the vaccine.

Phase III U.S. Trial

On August 31, 2020, AstraZeneca announced that a Phase III clinical trial had been registered in the U.S. The randomized, double-blind, placebo-controlled, multi-center interventional trial aims to enroll approximately 30,000 participants. Two-thirds (n = 20,000) of participants will receive two intramuscular doses of 5 × 1010 vp of ChAdOx1 nCoV-19 spaced 4 weeks apart; these subjects will make up the treatment group, while the remaining third (n = 10,000) will receive two doses of saline placebo spaced 4 weeks apart. The primary outcome measures are to test the efficacy of the AZD1222 candidate vaccine in the prevention of COVID-19 in adults, to test the immunogenicity of the candidate vaccine, and to assess the safety and potential adverse effects associated with the candidate vaccine.

Phase III Brazil Trial

On June 20, 2020, Latin America’s first Phase III trial of a COVID-19 vaccine candidate began enrolling study subjects, originally aiming to test 5,000 healthy volunteers. As of November 27, 2020, the randomized, controlled Phase III trial has an estimated enrollment of 10,300 adult subjects divided across four study arms. Participants in group 1a received a single dose of AZD1222 (5 x 1010 virus particles) with paracetamol, and they were compared to participants in group 1b who received a single dose of MenACWY vaccine (a vaccine protecting against four strains of meningococcal bacteria) with paracetamol. Meanwhile participants in group 1c received two doses of AZD1222 spaced 4-12 weeks apart, starting with an initial dose of 5 x 1010 virus particles and a 0.5 mL booster dose of 3.5 - 6.5 1010 viral particles. This group was compared to a control group that received an initial dose of MenACWY vaccine with a 0.5 saline placebo boost. All groups received paracetamol in conjunction with the vaccine candidate, vaccine, or control. The study aims to measure the safety, tolerability, and reactogenicity of AZD1222, as well as test for overall efficacy in preventing COVID-19.

Ad26.COV2.S

Ad26.COV2.S is a candidate vaccine produced by Johnson & Johnson that delivers a recombinant adenovirus containing the SARS-CoV-2 spike gene. The gene expresses the stabilized pre-fusion SARS-CoV-2 spike protein. It uses a recombinant form of adenovirus serotype 26, which was originally developed by researchers at Beth Israel Deaconess Hospital, as a vector to deliver DNA encoding a particular antigen. A recombinant form of the virus has been produc