Identificando regras de transição de autômato celular probabilista via algoritmo genético em sistemas epidemiológicos / Identificando regras de transição de autômato celular probabilista via algoritmo genético em sistemas epidemiológicos

AUTOR(ES)
DATA DE PUBLICAÇÃO

2008

RESUMO

Probabilistic cellular automata can be used to model the spreading of contagious diseases in a population composed by susceptible, infected and recovered individuals. At each time step, a susceptible individual can either remain susceptible or contract the disease from infected individuals, where the probability related to the contagion depends on the number of infected individuals in contact with this susceptible individual. At each time step, an infected individual may (probabilistically) either remain infected or recuperate or die by the disease or die by other causes. A recovered individual, at each iteration, can either remain as he/she is or die. When an infected or recovered individual dies, a susceptible one appears in his/her place; thus, the population remains constant. Here, genetic algorithms are employed to identify the probability values concerning the processes of infection, cure and death, from epidemiological data from Arizona (USA) for measles. The goal is to obtain a model based on probabilistic rules of state transitions able of reproducing this time series and to verify the quality of the model prediction. This work reveals that the predictions are strongly influenced by the lattice dimension of the cellular automaton and by limitations imposed to the probability values.

ASSUNTO(S)

engenharia eletrica propagação de doenças genetic algorithm cellular automaton disease spreading algoritmo genético autômato celular

ACESSO AO ARTIGO

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