In vivo kinetics of SARS-CoV-2 infection and its relationship with a person's infectiousness

Within-host dynamics of SARS-CoV-2 infection: A systematic review and meta-analysis

Within-host model specified by viral dynamic parameters is a mainstream tool to understand SARS-CoV-2 replication cycle in infected patients. The parameter uncertainty further affects the output of the model, such as the efficacy of potential antiviral drugs. However, gathering empirical data on these parameters is challenging. Here, we aim to conduct a systematic review of viral dynamic parameters used in within-host models by calibrating the model to the viral load data measured from upper respiratory specimens. We searched the PubMed, Embase and Web of Science databases (between 1 December 2019 and 10 February 2022) for within-host modelling studies. We identified seven independent within-host models from the above nine studies, including Type I interferon, innate response, humoral immune response or cell-mediated immune response. From these models, we extracted and analyse seven widely used viral dynamic parameters including the viral load at the point of infection or symptom onset, the rate of viral particles infecting susceptible cells, the rate of infected cells releasing virus, the rate of virus particles cleared, the rate of infected cells cleared and the rate of cells in the eclipse phase can become productively infected. We identified seven independent within-host models from nine eligible studies. The viral load at symptom onset is 4.78 (95% CI:2.93, 6.62) log(copies/ml), and the viral load at the point of infection is -1.00 (95% CI:-1.94, -0.05) log(copies/ml). The rate of viral particles infecting susceptible cells and the rate of infected cells cleared have the pooled estimates as -6.96 (95% CI:-7.66, -6.25) log([copies/ml]^-1 day^-1 ) and 0.92 (95% CI:-0.09, 1.93) day^-1 , respectively. We found that the rate of infected cells cleared was associated with the reported model in the meta-analysis by including the model type as a categorical variable (p < .01). Joint viral dynamic parameters estimates when parameterizing within-host models have been published for SARS-CoV-2. The reviewed viral dynamic parameters can be used in the same within-host model to understand SARS-CoV-2 replication cycle in infected patients and assess the impact of pharmaceutical interventions.

Multi-scale modelling reveals that early super-spreader events are a likely contributor to novel variant predominance

The emergence of new SARS-CoV-2 variants of concern (VOC) has hampered international efforts to contain the COVID-19 pandemic. VOCs have been characterized to varying degrees by higher transmissibility, worse infection outcomes and evasion of vaccine and infection-induced immunologic memory. VOCs are hypothesized to have originated from animal reservoirs, communities in regions with low surveillance and/or single individuals with poor immunologic control of the virus. Yet, the factors dictating which variants ultimately predominate remain incompletely characterized. Here we present a multi-scale model of SARS-CoV-2 dynamics that describes population spread through individuals whose viral loads and numbers of contacts (drawn from an over-dispersed distribution) are both time-varying. This framework allows us to explore how super-spreader events (SSE) (defined as greater than five secondary infections per day) contribute to variant emergence. We find stochasticity remains a powerful determinant of predominance. Variants that predominate are more likely to be associated with higher infectiousness, an SSE early after variant emergence and ongoing decline of the current dominant variant. Additionally, our simulations reveal that most new highly infectious variants that infect one or a few individuals do not achieve permanence in the population. Consequently, interventions that reduce super-spreading may delay or mitigate emergence of VOCs.

Mathematical Modeling of Vaccines That Prevent SARS-CoV-2 Transmission

SARS-CoV-2 vaccine clinical trials assess efficacy against disease (VEDIS), the ability to block symptomatic COVID-19. They only partially discriminate whether VEDIS is mediated by preventing infection completely, which is defined as detection of virus in the airways (VESUSC), or by preventing symptoms despite infection (VESYMP). Vaccine efficacy against transmissibility given infection (VEINF), the decrease in secondary transmissions from infected vaccine recipients, is also not measured. Using mathematical modeling of data from King County Washington, we demonstrate that if the Moderna (mRNA-1273QS) and Pfizer-BioNTech (BNT162b2) vaccines, which demonstrated VEDIS > 90% in clinical trials, mediate VEDIS by VESUSC, then a limited fourth epidemic wave of infections with the highly infectious B.1.1.7 variant would have been predicted in spring 2021 assuming rapid vaccine roll out. If high VEDIS is explained by VESYMP, then high VEINF would have also been necessary to limit the extent of this fourth wave. Vaccines which completely protect against infection or secondary transmission also substantially lower the number of people who must be vaccinated before the herd immunity threshold is reached. The limited extent of the fourth wave suggests that the vaccines have either high VESUSC or both high VESYMP and high VEINF against B.1.1.7. Finally, using a separate intra-host mathematical model of viral kinetics, we demonstrate that a 0.6 log vaccine-mediated reduction in average peak viral load might be sufficient to achieve 50% VEINF, which suggests that human challenge studies with a relatively low number of infected participants could be employed to estimate all three vaccine efficacy metrics.

Daily sampling of early SARS-CoV-2 infection reveals substantial heterogeneity in infectiousness

The dynamics of SARS-CoV-2 replication and shedding in humans remain poorly understood. We captured the dynamics of infectious virus and viral RNA shedding during acute infection through daily longitudinal sampling of 60 individuals for up to 14 days. By fitting mechanistic models, we directly estimate viral reproduction and clearance rates, and overall infectiousness for each individual. Significant person-to-person variation in infectious virus shedding suggests that individual-level heterogeneity in viral dynamics contributes to superspreading. Viral genome load often peaked days earlier in saliva than in nasal swabs, indicating strong compartmentalization and suggesting that saliva may serve as a superior sampling site for early detection of infection. Viral loads and clearance kinetics of B.1.1.7 and non-B.1.1.7 viruses in nasal swabs were indistinguishable, however B.1.1.7 exhibited a significantly slower pre-peak growth rate in saliva. These results provide a high-resolution portrait of SARS-CoV-2 infection dynamics and implicate individual-level heterogeneity in infectiousness in superspreading.