Optimal control strategies to tailor antivirals for acute infectious diseases in the host: A study case of COVID-19

Abstract

Several mathematical models in SARS-CoV-2 have shown how the target cell model can help to understand the spread of the virus in the host and how potential antiviral treatments can help to control the virus. Concepts as equilibrium and stability have shown to be crucial to qualitatively determine the best alternatives to schedule drugs, based on their effectiveness in reducing the viral infection and replication rates. Important biological events such as rebounds of the infections (when antivirals are incorrectly interrupted) can also be explained by means of a dynamic study of the target cell model. In this work a full characterization of the dynamical behavior of the target cell models under control actions is given and, based on this characterization, the optimal fixed-dose antiviral schedule that produces the smallest amount of dead cells (without viral load rebounds) is computed. The results of several simulations – performed by considering real patient data – show the potential benefits of both the model characterization and the control strategy.