Kinetics of SARS-CoV-2 infection in the human upper and lower respiratory tracts and their relationship with infectiousness

Abstract

SARS-CoV-2 is a human pathogen that causes infection in both the upper respiratory tract (URT) and the lower respiratory tract (LRT). The viral kinetics of SARS-CoV-2 infection and how they relate to infectiousness and disease progression are not well understood. Here, we develop data-driven viral dynamic models of SARS-CoV-2 infection in both the URT and LRT. We fit the models to viral load data from patients with likely infection dates known, we estimated that infected individuals with a longer incubation period had lower rates of viral growth, took longer to reach peak viremia in the URT, and had higher chances of presymptomatic transmission. We then developed a model linking viral load to infectiousness. We found that to explain the substantial fraction of transmissions occurring presymptomatically, the infectiousness of a person should depend on a saturating function of the viral load, making the logarithm of the URT viral load a better surrogate of infectiousness than the viral load itself. Comparing the roles of target-cell limitation, the innate immune response, proliferation of target cells and spatial infection in the LRT, we found that spatial dissemination in the lungs is likely to be an important process in sustaining the prolonged high viral loads. Overall, our models provide a quantitative framework for predicting how SARS-CoV-2 within-host dynamics determine infectiousness and represent a step towards quantifying how viral load dynamics and the immune responses determine disease severity.

Mechanistic Modeling of SARS‐CoV‐2 and Other Infectious Diseases and the Effects of Therapeutics

“…the antiviral remdesivir 58 . With the availability of clinical and experimental data, many mathematical models have been developed and calibrated with data 43,44,45,46,47,48,49,50,51,52,53 . Overall, these studies greatly advanced our quantitative understanding of SARS-CoV-2 infection in both humans and NHPs, the immune responses and the impact of therapeutics.…”

“…One key challenge in modeling a novel viral infection, such as SARS-CoV-2 infection, is that key parameter values such as the number of target cells in the model are unknown. Efforts have been made to estimate these parameter values from data 44,48,49,50 . For example, single cell gene expression data suggests that approximately 1% of cells in the respiratory tracts express the receptor and the coreceptor for SARS-CoV-2 entry, i.e., angiotensin-converting enzyme 2 (ACE2) and the type II transmembrane serine protease TMPRSS2 59,60 .…”

“…To model the protection of target cells by IFN signaling in influenza infection, Pawelek et al added a refractory cell compartment 40 . Similar formulations were used for SARS-CoV-2 infection 44,46,49 . The ODEs for the target cell population (T), the refractory cell population (R) and interferon (F) from Eqs.…”

“…This approach involves adding only one or two extra parameters to an existing model, and thus is particularly well-suited for model calibration with data. Several studies have adopted this approach to understand SARS-CoV-2 infection dynamics beyond 2 weeks after infection 45,49,51 . In general, the increased killing explains the rapid viral load decreases that accompany viral clearance (see the solid blue line in Fig.…”

“…In some individuals, multiple viral load peaks were observed late in the infection. Motivated by these observations, we developed a two-compartment model to keep track of these distinct dynamics 49 .…”