An optimal control study for a two-strain SEIR epidemic model with saturated incidence rates and treatment

TL;DR

This paper develops and analyzes an optimal control model for a two-strain SEIR epidemic with saturated incidence and treatments, demonstrating the effectiveness of therapies in reducing disease severity and fitting COVID-19 data.

Contribution

It introduces a novel two-strain SEIR model with saturated rates and treatments, and applies optimal control to evaluate intervention strategies.

Findings

Optimal control reduces infection levels significantly.

Model fits well with COVID-19 clinical data.

Therapies effectively decrease disease severity.

Abstract

This work will study an optimal control problem describing the two-strain SEIR epidemic model. The studied model is in the form of six nonlinear differential equations illustrating the dynamics of the susceptibles and the exposed, the infected, and the recovered individuals. The exposed and the infected compartments are each divided into two sub-classes representing the first and the second strain. The model includes two saturated rates and two treatments for each strain. We begin our study by showing the well-posedness of our problem. The basic reproduction number is calculated and depends mainly on the reproduction numbers of the first and second strains. The global stability of the disease-free equilibrium is fulfilled. The optimal control study is achieved by using the Pontryagin minimum principle. Numerical simulations have shown the importance of therapy in minimizing the infection's effect. By administrating suitable therapies, the disease's severity decreases considerably. The estimation of parameters as well as a comparison study with COVID-19 clinical data are fulfilled. It was shown that the mathematical model results fits well the clinical data.