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

Abstract

Modern viral kinetic modeling and its application to therapeutics is a field that attracted the attention of the medical, pharmaceutical, and modeling communities during the early days of the AIDS epidemic. Its successes led to applications of modeling methods not only to HIV but a plethora of other viruses, such as hepatitis C virus (HCV), hepatitis B virus and cytomegalovirus, which along with HIV cause chronic diseases, and viruses such as influenza, respiratory syncytial virus, West Nile virus, Zika virus, and severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2), which generally cause acute infections. Here we first review the historical development of mathematical models to understand HIV and HCV infections and the effects of treatment by fitting the models to clinical data. We then focus on recent efforts and contributions of applying these models towards understanding SARS‐CoV‐2 infection and highlight outstanding questions where modeling can provide crucial insights and help to optimize nonpharmaceutical and pharmaceutical interventions of the coronavirus disease 2019 (COVID‐19) pandemic. The review is written from our personal perspective emphasizing the power of simple target cell limited models that provided important insights and then their evolution into more complex models that captured more of the virology and immunology. To quote Albert Einstein, “Everything should be made as simple as possible, but not simpler,” and this idea underlies the modeling we describe below.

Comparing antiviral strategies against COVID-19 via multiscale within-host modelling

“…We note that the predicted two-peak behaviour is consistent with observations in Wölfel et al . [ 59 ], and indeed is expected in any model that includes the adaptive immune response [ 60 ].…”

Spatially distributed infection increases viral load in a computational model of SARS-CoV-2 lung infection

“…Most prior work uses Ordinary Differential Equation (ODE) models to represent within-host virus dynamics, e.g., [6][7][8][9][10][11][12]. Such models are useful for studying the onset and duration of the infective period [13] and the effect of various therapeutics given at different times [14][15][16]. However, they have limited ability to fully account for dynamics in the large and complex space of the lung [12,17].…”

The relative strength and timing of innate immune and CD8 T-cell responses underlie the heterogeneous outcomes of SARS-CoV-2 infection

“…In the last year, several mathematical models of within-host SAR-CoV-2 dynamics have been developed and have offered valuable insights (53). For instance, they have helped estimate the within-host basic reproductive ratio (33,34,52) and assess the effects of drugs and vaccines (26,27,44,(54)(55)(56)(57).…”