Speaker
Description
Understanding the within-host SARS-CoV-2 dynamics is of considerable importance for predicting clinical and public health outcomes. So far, mathematical models have been used to describe the within-host SARS-CoV-2 infection dynamics and to quantify viral load data of single individuals. Their focus has been on describing the within-host SARS-CoV-2 dynamics during the acute infection phase only, disregarding potentially important long-term post-acute infection effects. We present a novel mathematical model, which describes the SARS-CoV-2 infection dynamics during the acute short-term infection phase and fits clinical data of infected individuals as well as existing models. Moreover, our model captures additional fundamental characteristics of the post-acute long-term infection process. Features such as the permanent clearance of the infection within the individual, waning immunity and long-term post-infection immunity levels, are reproduced through the intrinsic dynamics of the model. Finally, we used our model to explore reinfection scenarios differentiating between distinct variant-specific properties of the reinfecting virus. The model's ability to describe not only the acute short-term but also the post-acute long-term infection dynamics provides a more realistic description of key outcomes. Moreover, it allows for its application in clinical research, such as the uncovering of the underlying dynamics of the immune response-virus interaction or the optimization of treatment regimens.
