Um modelo epidemiológico baseado em autômatos celulares e rede multicidades

Abstract

History contains several pandemics that have had major impacts on public health, the economy and society. Since the Antonine Plague in the 2nd century AD, several pandemics have been recorded, such as the Black Death, Spanish Flu, HIV/AIDS and SARS. The most recent pandemic is COVID-19, declared by the World Health Organization in March 2020, which has spread rapidly across the world and affected millions of people, leading to a global crisis in public health and the economy. This study aims to investigate the impact of moving individuals between cities on COVID-19 dynamics in the population during pandemics. For this purpose, an epidemiological mathematical model based on a multicity network was proposed. The city network was configured based on two classic models of complex random networks: Small-World and Barabási–Albert. The mathematical model used for a city uses cellular automata and a variation of the SIR (Susceptible-Infected-Recovered) model, proposed by Kermack and McKendrick in 1927. The research concludes that reducing the rate of travel between cities can decrease the global peak of infected in the population and delay it, "flattening the curve" of infections. In addition, the topology of connections between cities interferes with the speed of propagation and the dynamics of spreading the disease between cities. With this, the study contributes to the understanding of the effects of policies to control the movement of people between cities in the spread of pandemics.