Modelos de inteligência computacional aplicados ao sistema epidemiológico

Abstract

Due to the need for public authorities to establish strategies to combat epidemics, the analysis of mathematical models that describe the spread of viral infectious diseases has become a topic of significant relevance to society. Many researchers employ epidemiology approaches based on differential equations with static parameters, meaning parameters that remain constant over time. Generally, these models assume that each individual can transmit the disease and recover from it at a constant rate. Several epidemiological models describe the spread of viral infectious diseases, and some of them are based on differential equations. In this context, this study proposes the application of Computational Intelligence (CI) methods to solve a system modeled by differential equations. The focus of this research is to investigate epidemiological models that analyze the dynamics of COVID-19 transmission. Among these models, the SIR model stands out, widely cited for describing the behavior of the current pandemic, as evidenced by a systematic review. This is due to the SIR model’s ability to incorporate the most relevant characteristics of the disease caused by the novel coronavirus (COVID-19). CI methods are also employed for parameter estimation of these models. The computational results illustrate the behavior of the CI methods described in this study, from March 2020 to March 2023, in the state of Ceará and from February 2020 to February 2023, in Brazil.