Dynamical Characterization of Antiviral Effects in COVID-19

TL;DR

This paper develops mathematical models to evaluate antiviral treatment effectiveness for COVID-19, analyzing virus dynamics and immune responses to predict clinical outcomes and optimize treatment timing.

Contribution

It introduces a framework to determine antiviral effectiveness thresholds and characterizes virus dynamics based on treatment timing using real patient data.

Findings

Antiviral effectiveness thresholds can predict treatment success.

Timing of treatment initiation influences virus peak and clearance.

Simulation shows potential clinical benefits of tailored antiviral strategies.

Abstract

Mathematical models describing SARS-CoV-2 dynamics and the corresponding immune responses in patients with COVID-19 can be critical to evaluate possible clinical outcomes of antiviral treatments. In this work, based on the concept of virus spreadability in the host, antiviral effectiveness thresholds are determined to establish whether or not a treatment will be able to clear the infection. In addition, the virus dynamic in the host -- including the time-to-peak and the final monotonically decreasing behavior -- is chracterized as a function of the treatment initial time. Simulation results, based on nine real patient data, show the potential clinical benefits of a treatment classification according to patient critical parameters. This study is aimed at paving the way for the different antivirals being developed to tackle SARS-CoV-2.