Parsimonious models of in-host viral dynamics and immune response

Mathematical models of in-host viral dynamics and immune response are a vital tool for patient-specific estimation of the initial viral load, prediction of the course of an infection, etc. The COVID-19 pandemics has given impetus to the development of models with an ever-increasing degree of complexity. We show that one of the most popular models—the Target Cell Limited model—fails the identifiability test, i.e., its parameters cannot be uniquely inferred from readily available data such as viral load measurements. We present a model that is both identifiable and parsimonious according to information criteria. Our model's predictions match both reported observations of COVID-19 patients and predictions of its more complex counterparts.