Multiobjective approach to optimal control for a tuberculosis model

TL;DR

This paper applies a multiobjective optimization approach to a tuberculosis model to identify optimal strategies that minimize infections and costs, providing insights into disease dynamics and control effectiveness.

Contribution

It introduces a novel multiobjective optimization method for TB control, considering reinfection and post-exposure interventions within a differential equations framework.

Findings

Optimal control strategies vary with model parameters.

Numerical results demonstrate the approach's effectiveness.

Strategies effectively reduce active and latent TB cases.

Abstract

Mathematical modelling can help to explain the nature and dynamics of infection transmissions, as well as support a policy for implementing those strategies that are most likely to bring public health and economic benefits. The paper addresses the application of optimal control strategies in a tuberculosis model. The model consists of a system of ordinary differential equations, which considers reinfection and post-exposure interventions. We propose a multiobjective optimization approach to find optimal control strategies for the minimization of active infectious and persistent latent individuals, as well as the cost associated to the implementation of the control strategies. Optimal control strategies are investigated for different values of the model parameters. The obtained numerical results cover a whole range of the optimal control strategies, providing valuable information about the tuberculosis dynamics and showing the usefulness of the proposed approach.