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

The within-host viral kinetics of SARS-CoV-2 infection and how they relate to a person’s infectiousness are not well understood. This limits our ability to quantify the impact of interventions on viral transmission. Here, we develop viral dynamic models of SARS-CoV-2 infection and fit them to data to estimate key within-host parameters such as the infected cell half-life and the within-host reproductive number. We then develop a model linking viral load (VL) to infectiousness and show a person’s infectiousness increases sublinearly with VL and that the logarithm of the VL in the upper respiratory tract is a better surrogate of infectiousness than the VL itself. Using data on VL and the predicted infectiousness, we further incorporated data on antigen and RT-PCR tests and compared their usefulness in detecting infection and preventing transmission. We found that RT-PCR tests perform better than antigen tests assuming equal testing frequency; however, more frequent antigen testing may perform equally well with RT-PCR tests at a lower cost but with many more false-negative tests. Overall, our models provide a quantitative framework for inferring the impact of therapeutics and vaccines that lower VL on the infectiousness of individuals and for evaluating rapid testing strategies.

SARS-CoV-2 is a new human pathogen that causes COVID-19 (1). It is highly contagious, spread rapidly across the globe and has caused 5 million deaths worldwide as of the end of October 2021. At the molecular level, SARS-CoV-2 enters host cells via the angiotensin converting enzyme 2 (ACE-2) receptor. It infects cells in the upper respiratory tract (URT), can rapidly reach a high viral load (VL) and be effectively transmitted (2–4). However, it is not clear how VL, symptom onset, and infectiousness are quantitatively related.Previously, both VL and log₁₀ VL have been used as surrogates for infectiousness of influenza (5) and SARS-CoV-2 (6, 7). A quantitative understanding of the relationship is critical for both nonpharmaceutical and pharmaceutical interventions. First, it would allow for more precise prediction of the infectiousness of infected individuals, including children and pre- or asymptomatic individuals, based on their VL measurements (8, 9). This could in turn lead to quantification of their contribution to the overall transmission in a community and help to better inform public health policy decisions. Second, as administration of vaccines may lead to lowered VLs in breakthrough infections (10–12), a quantitative understanding will inform how these reductions in VL impact infectiousness and thus allow better predictions of how much transmission vaccinated individuals with breakthrough infection cause. Third, it would provide better insight into a person’s infectiousness throughout the course of infection and thus inform testing strategies for work/school reopening, travel, etc. The effectiveness of test, trace, and quarantine as control strategies heavily depends on the sensitivity and specificity of the tests and rate of testing being implemented (13). It was recently proposed that antigen tests with low sensitivity are preferred over highly sensitive RT-PCR tests because of their potential for wide coverage and short turnaround time (6). However, the effectiveness of this strategy has not been evaluated based on VL and infectiousness dynamics inferred from data.Here, we construct viral dynamic models of SARS-CoV-2 URT infection and a model linking VL to infectiousness. Mathematical modeling has been applied, by us and others, to understand SARS-CoV-2 infection and the potential impact of therapy (14–18). However, there were large uncertainties in model parameter estimates because in almost all studies, viral dynamic models were fit to data that were taken after symptom onset without knowledge of the patients’ infection dates and early VL dynamics. We resolve this issue by using two unique datasets and by using clinical and epidemiological data to inform the quantitative relationship between VL and infectiousness. Using this relationship, we further evaluate the effectiveness of testing strategies using either antigen or RT-PCR tests at different testing frequencies.