Optimal control applied to viral competition

TL;DR

This paper develops mathematical models and applies optimal control theory to manage viral mutations, aiming to reduce mutant growth and improve treatment strategies.

Contribution

It introduces a novel optimal control framework for viral mutation models, providing analytical insights and numerical validation.

Findings

Optimal control strategies can effectively reduce mutant viral growth.

Mathematical models predict the impact of interventions over time.

Numerical simulations validate the analytical results.

Abstract

The emergence of mutant lineages within a viral species has become a public health problem, as the existing treatments and drugs are usually more effective on the original lineages than in the mutant ones. The following manuscript presents mathematical models that describe the emergence of these lineages. In order to reduce the damage and possible casualties that can be attributed to these more contagious microorganisms, the theory of optimal control is introduced and a more sophisticated model is proposed to reduce the mutant growth compared to the original one. The analytical study of these models allows us to obtain an overview of the expected behavior over time, which is validated with numerical simulations.