Dengue disease: a multiobjective viewpoint

TL;DR

This paper models dengue transmission using differential equations and employs multiobjective evolutionary algorithms to optimize control strategies, providing valuable insights for intervention planning and demonstrating superior algorithm performance.

Contribution

It introduces a multiobjective optimization framework for dengue control, compares various EMO algorithms, and proposes a hybrid method with enhanced performance.

Findings

Multiobjective approach yields diverse effective control solutions.

Hybrid EMO algorithm outperforms five state-of-the-art algorithms.

Results inform better intervention strategies for dengue management.

Abstract

During the last decades, the global prevalence of dengue progressed dramatically. It is a disease that is now endemic in more than one hundred countries of Africa, America, Asia, and the Western Pacific. In this paper, we present a mathematical model for the dengue disease transmission described by a system of ordinary differential equations and propose a multiobjective approach to find the most effective ways of controlling the disease. We use evolutionary multiobjective optimization (EMO) algorithms to solve the resulting optimization problem, providing the performance comparison of different algorithms. The obtained results show that the multiobjective approach is an effective tool to solve the problem, giving higher quality and wider range of solutions compared to the traditional technique. The obtained trade-offs provide a valuable information about the dynamics of infection transmissions and can be used as an input in the process of planning the intervention measures by the health authorities. Additionally, a suggested hybrid EMO algorithm produces highly superior performance compared to five other state-of-the-art EMO algorithms, being indispensable to efficiently optimize the proposed model.