Heterogeneous SARS-CoV-2 kinetics due to variable timing and intensity of immune responses

Abstract

The viral kinetics of documented SARS-CoV-2 infections exhibit a high degree of inter-individual variability. We identified six distinct viral shedding patterns, which differed according to peak viral load, duration, expansion rate and clearance rate, by clustering data from 810 infections in the National Basketball Association cohort. Omicron variant infections in previously vaccinated individuals generally led to lower cumulative shedding levels of SARS-CoV-2 than other scenarios. We then developed a mechanistic mathematical model that recapitulated 1510 observed viral trajectories, including viral rebound and cases of reinfection. Lower peak viral loads were explained by a more rapid and sustained transition of susceptible cells to a refractory state during infection, as well as an earlier and more potent late, cytolytic immune response. Our results suggest that viral elimination occurs more rapidly during omicron infection, following vaccination, and following re-infection due to enhanced innate and acquired immune responses. Because viral load has been linked with COVID-19 severity and transmission risk, our model provides a framework for understanding the wide range of observed SARS-CoV-2 infection outcomes.

A unifying model to explain high nirmatrelvir therapeutic efficacy against SARS-CoV-2, despite low post-exposure prophylaxis efficacy and frequent viral rebound

“…Reference source not found. a ) (26) . The model is target-cell limited due to a finite number of susceptible cells.…”

“…We used our model of SARS-CoV-2 dynamics (26) to model the viral load dynamics of symptomatic individuals with SARS-CoV-2 infection. Our model assumes that susceptible cells ( S ) are infected at rate βvs by SARS-CoV-2 virions.…”

“…Finally, free virions are cleared at the rate γ . Of note, this model, previously proposed by Ke et al (52) , was selected against other models in (26) based on superior fit to data and parsimony. The model written as a set of differential equations has the form, To estimate parameter values, we fit the model to viral load data from the NBA cohort using a mixed-effect population approach implemented in Monolix.…”

Opening Pandora's box: caveats with using toolbox-based approaches in mathematical modeling in biology

“…Fitting non-linear, ODEbased mathematical models to longitudinal data from multiple individuals has been dramatically assisted with the release of freefor-academics (but yet proprietary) program Monolix with a simple and intuitive interface and powerful computational methods to ensure fit convergence (https://monolix.lixoft.com/). There is a surge in papers that build relatively complex mathematical models that often cannot be fit to data from a single individual but by using the power of mixed-effect modeling approach (and Monolix) sometimes adequate fits of the model to data can be generated [40,41]. However, (blind) usage of this tool can also result in conclusions that are not well-explained.…”

“…Even though the model fitted the data, the model included initial viral load and eclipse phase parameters that clearly could not be identified from the data because no initial viral loads were available (and that different eclipse phase durations are likely to be consistent with the data). Another study used better viral load measurements but could conclude how various elements of immunity contribute to the viral shedding pattern even though no immunological information was available in the patients [41]. Using a toolbox approach allows to get answers ("model fits the data") but whether these answers make sense and why the model is able to fit the data is typically not discussed.…”