SARS-CoV-2 production leads to Viral infection, proliferated

In pathogen evolution it is the nature of the virus to replicate in a host (upstream KE 1847) and take advantage of internal and external transport mechanisms to reach another suitable habitat (downstream KE/AO 1939) to replicate again and result in infection. For this AOP and specifically for this KER, it is helpful to be aware of historical development of disease theory, i.e., the germ theory of disease and Koch’s postulates from the 19^th century. Importantly, the first postulate that the microbe must be found in diseased individuals but not those without symptoms, had to be revised when it was realized that some bacteria like those causing cholera and typhoid could be carried by hosts who were asymptomatic (Fredricks and Relman, 1996). Viruses were discovered and were found to only replicate in cells and cannot be grown in pure culture, confounding the second postulate. Therefore, modifications of these disease principles have been applied to viruses (Rivers, 1937), and are basically an attempt at proving causation. Fredricks and Relman (1996) present a review citing several of these important revisions and their application with current technology like sequence-based identification of pathogens to prove the biological plausibility of the causal agent moving from host to host. Interestingly, Fouchier et al. (2003) carry out a proof that Koch’s postulates, as modified by Rivers (1937), are fulfilled for the (first) SARS virus. Numerous studies on SARS-CoV-2 demonstrate both the presence of the viral sequence by PCR (viral load), and the presence of neutralizing antibodies to the virus in upstream cases as considered by Evans’ (1976) proposed ‘‘Elements of Immunological Proof of Causation.’’ These principles go on to cover the downstream event, transmission to a healthy contact, where the antibody to the agent (SARS-CoV-2) is absent prior to the disease and exposure to the agent, the antibody appears during illness, and a downstream contact with no antibodies to the agent is susceptible to infection and disease produced by the agent (Evans, 1976; Fredricks and Relman, 1996). Literature providing empirical evidence of these principles specific to SARS-CoV-2 is provided below.

The causal agent of the COVID-19 disease, SARS-CoV-2, the stressor that is increased within a host’s cells in the upstream event, is also the stressor that infects multiple hosts (proliferates) in the downstream event. Evidence is found not only in many human studies, but also in other mammalian studies. Importantly, Worobey et al. (2022) gathered evidence within and around the Huanan Seafood Wholesale Market in Wuhan, China, with several findings on the origins of the human disease caused by the SARS-CoV-2 virus, including: 1) live animals shown to be susceptible to SARS-CoV-2 were sold at the market in late 2019; 2) the SARS-CoV-2 virus was found in environmental samples taken from the live animal vender locations, indicating viral shedding from the animals occurred; 3) earliest known human cases of the COVID-19 disease (December 2019) were geographically centered around the market, and 4) 66% of the 41 people hospitalized with the disease by January 2, 2020 had direct exposure to the market. Pekar et al. (2022) found only two distinct viral lineages of SARS-CoV-2 before February 2020 which epidemic simulations showed were the result of two or more separate zoonotic transmission events to humans, the first lineage introduced between late October and early December, and the second lineage weeks later. Those infected with the first lineage had direct contact with the Huanan market, and those with the second lineage did not, but lived or stayed near the market during that time period (Pekar et al., 2022). This evidence indicates zoonotic transfer to humans, with spread of the disease from human to human in Wuhan as the epicenter.

In humans, as the pandemic spread, household contact studies and tracking were used to determine viral loads and secondary infections in contacts:

Bhavnani et al. (2022 https://doi.org/10.1186/s12879-022-07663-1) found an association between SARS-CoV-2 viral load in an individual with a case of COVID-19 and the risk of transmission to contacts. Among the 212 primary (index) cases assessed, median viral load was 5.6 (1.8–10.4) log₁₀ RNA copies per mL of saliva, with 70 (19%) of their 365 contacts testing positive after exposure. Of those 70, 36 (51%) were exposed to an index case that was asymptomatic or pre-symptomatic on the day of exposure. Contacts infected increased monotonically with index case viral load, resulting in a significant association between viral load and risk of transmission (RR = 1.27, 95% CI 1.22–1.32).

• Marks et al. (2021 https://doi.org/10.1016/S1473-3099(20)30985-3) conducted a similar study in Catalonia, Spain. Of 282 patients with COVID-19 that had a total of 753 contacts, 17% (125 of 753 contacts) became infected. Infections varied from 12% when the index case viral load was less than 1 × 10⁶ copies per mL to 24% when the index case viral load was 1 × 10¹⁰ copies per mL or more. Transmission risk increased for household contacts and by increasing age of the contact. The study results indicated that viral load of index cases was the strongest factor in SARS-CoV-2 transmission.

While it has been documented that humans have passively transmitted SARS-CoV-2 back to other mammals (see KE1939), researchers have also conducted controlled exposure experiments in other mammals in which they inoculated test animals with SARS-CoV-2, measured viral shedding, and confirmed infection in contact animals:

• Freuling et al. (2020) tested raccoon dogs by administering intranasal inoculations to nine naive animals with SARS-CoV-2 2019_nCoV Muc-IMB-1, and introducing 3 naive animals 24 hours after inoculation. Six inoculated and two contact animals became infected based on viral RNA measured by qPCR in nasal, oropharyngeal, and rectal swab samples. SARS-CoV-2 viral RNA was first detected in a contact animal 7 days after contact.
• Palmer et al. (2021) conducted intranasal inoculations of white-tailed deer fawns with SARS-CoV-2, resulting in infection and shedding of infectious virus in nasal secretions. The infected animals were found to transmit the virus to contact deer.
• Martins, et al. (2022) found that white-tailed deer fawns shed infectious virus in nasal and oral secretions up to 5 days after intranasal inoculation with SARS-CoV-2 B.1 lineage, with deer-to-deer transmission occurring on day 3 post-inoculation.
• Cool, et al. 2022 investigated transmission in adult white-tailed deer co-infected with both the SARS-CoV-2 ancestral lineage A and the alpha variant of concern (VOC) B.1.1.7. Presence and transmission of each strain was determined using next-generation sequencing, with the finding that the alpha VOC B.1.1.7 isolate outcompeted ancestral lineage A. They found direct contact transmission and also vertical transmission from doe to fetus.
• Shuai et al. (2020) studied minks and showed that SARS-CoV-2 replicates efficiently in the upper and lower respiratory tracts. To investigate transmission, intranasal inoculations were administered to three animals that were then placed in a separate cages. After 24 hours, three naïve minks were placed in cages adjacent to the virus-inoculated mink without direct contact. Viral RNA was detected in the nasal washes of all three introduced animals after 3-9 days showing that SARS-CoV-2 was transmitted via respiratory droplets.
• Kim et al. (2021) found that SARS-CoV-2 replicated in 6 inoculated ferrets, and was transmitted to all 6 direct contact ferrets and two of 6 indirect contact ferrets, indicating direct contact was more efficient, but airborne transmission also occurred. Airborne transmission likely results in a lower dosage than direct contact.

The main area of uncertainty is in quantifying viral load either by measuring viral RNA by PCR or by isolating the virus in cell culture and determining numbers of plaque forming units (PFU).

A review by Puhach et al. (2022 https://doi.org/10.1038/s41579-022-00822-w) points out several issues affecting quantification:

• Quantitative real-time polymerase chain reaction (RT-PCR) cannot differentiate between replication-competent (infectious) virus and residual (non-infectious) viral RNA.
• RT-PCR cannot determine whether the RNA viral load is increasing or decreasing; peak viral load may have passed or has not yet been reached.
• Analytical sensitivity of measured RT-PCR cycles (Ct values) used to determine RNA copy numbers and limits of detection may vary between the tests and laboratories.
• Site of specimen collection (e.g., nasal or nasopharyngeal swabs or throat samples) can affect viral load measurement.

For cell culture, only live (infectious) viruses are counted, however:

• Quality of the sample affects success of viral culture.
• Loss of infectiousness can occur due to unsuitable storage conditions like high temperatures (requirement for −80 °C) or repeated freeze–thaw cycles.
• Cell lines used for isolation can show a high variability between laboratories.
• Culture medium or additives such as fetal bovine serum and antibiotics may affect success of viral culture.
• Infectious virus determined using Vero E6 cells might overestimate transmission risks in vivo.

Multilevel feedback mechanisms have been suggested regarding behaviors such as social distancing and how they influence SARS-CoV-2 mutational adaptations geospatially (Barrett et al., 2022 https://doi.org/10.1089/cmb.2020.0343). A transfer entropy (TE) framework was used to illustrate the feedback between macrolevel dynamics of socio-behavioral measures and microlevel mutational composition of the viral population. For example, A23404G leading to the D614G mutation in the viral spike protein significantly increases the viral load in patients, in turn increasing transmission rates. Differences in culture, policy, and the severity of infections resulted in distinct selective pressures on the virus in different geospatial blocks, producing different mutational signals for human populations in California versus New York versus Washington, suggesting a feedback loop connecting socio-behavioral patterns with mutational signatures. Additionally, the framework shows adaptability of the virus based on a noncoding mutation G29540A, highly localized in NY (> 95%), and extended incubation periods in China possibly due to the pressure imposed by drastic social distancing measures (Barrett et al., 2022).

Bradley et al. (2020 https://doi.org/10.1016/j.eclinm.2020.100325) use a systems approach with causal loop diagrams as tools to also illustrate the dynamics of a societal response to the threat of COVID-19. They propose CLDs that include numbers of infectious and susceptible people in feedback loops with policies that influence risk of transmission and transmission events.

Wanelik et al. (2023 https://doi.org/10.1016/j.isci.2023.106618) focus on “superspreaders”, i.e., individuals who are capable of infecting more than the average number of secondary contacts, who, evidence suggests, may be more likely to become superspreaders themselves. They used a generic model with hypothetical parameters to show that this positive feedback loop had effects on the herd immunity threshold, basic reproduction number (R₀), the peak prevalence of superspreaders, and the final epidemic size.