Optimal control strategies to tailor antivirals for acute infectious diseases in the host

TL;DR

This paper uses mathematical modeling and optimal control theory to design antiviral treatment schedules for SARS-CoV-2, aiming to minimize cell death and prevent infection rebounds.

Contribution

It provides a full dynamical analysis of the target-cell model under control actions and computes optimal fixed-dose antiviral schedules based on this analysis.

Findings

Optimal schedules reduce dead cell count

Model explains infection rebounds due to treatment interruption

Simulations with real data demonstrate strategy effectiveness

Abstract

Several mathematical models in SARS-CoV-2 have shown how target-cell model can help to understand the spread of the virus in the host and how potential candidates of antiviral treatments can help to control the virus. Concepts as equilibrium and stability show to be crucial to qualitatively determine the best alternatives to schedule drugs, according to effectivity in inhibiting the virus 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 made 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. Several simulation results - performed by considering real patient data - show the potential benefits of both, the model characterization and the control strategy.