Uma investigação epidemiológica baseada em autômato celular probabilista assíncrono

Chaves, Luciano Lucindo
Monteiro, Luiz Henrique Alves
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Omar, Nizam
Oliveira, Pedro Paulo Balbi de
Caldas, Iberê Luiz
Berlinck, José Guilherme de Souza Chaui Mattos
Engenharia Elétrica
In epidemiology, analytical and computational models are used to study the dynamics of the spreading of contagious diseases. The goal is to predict the temporal evolution of the number of sick individuals and to evaluate the effectiveness of control measures. There are several epidemiological models based on cellular automata, differential equations and difference equations. When cellular automata are employed, a synchronous update is usually performed, so that the states of all the individuals composing the lattice are updated simultaneously at each step of time. In this thesis, the dynamics of SIS (susceptible-infected-susceptible) epidemiological models based on asynchronous probabilistic cellular automata are investigated. Equivalent models written in terms of difference equations are proposed and analyzed in order to understand the numerical results of cellular automata. In these models, the population is divided into groups of different habits. The first group, more socially active, has its states updated in the usual way, at each step of time. The other groups, with less social activity, undergo periodic updating only after a determined number of time steps. Such models may be suitable for studying sexually transmitted diseases, such as gonorrhea, chlamydia, and syphilis, and for studying the propagation of hepatitis C. t is shown that groups of individuals with distinct social habits, as well as the spatial distribution of these groups, can cause oscillation in the number of infected individuals. Until then, for SIS models, studies had shown that possible causes of oscillation are migration, seasonality and delay.
autômato celular propabilista , epidemiologia , modelo SIS , oscilação autossustentada , sistemas dinâmicos
CHAVES, Luciano Lucindo. Uma investigação epidemiológica baseada em autômato celular probabilista assíncrono. 2018. 101 f. Tese( Engenharia Elétrica) - Universidade Presbiteriana Mackenzie, São Paulo.