The effect of spatial scale on predicting time series: A study on epidemiological system identification
Tipo
Artigo
Data de publicação
2009
Periódico
Mathematical Problems in Engineering
Citações (Scopus)
4
Autores
Monteiro L.H.A.
Oliveira D.N.
Chaui-Berlinck J.G.
Oliveira D.N.
Chaui-Berlinck J.G.
Orientador
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ISSN da Revista
Título de Volume
Membros da banca
Programa
Resumo
A susceptible-infective-recovered (SIR) epidemiological model based on probabilistic cellular automaton (PCA) is employed for simulating the temporal evolution of the registered cases of chickenpox in Arizona, USA, between 1994 and 2004. At each time step, every individual is in one of the states S, I, or R. The parameters of this model are the probabilities of each individual (each cell forming the PCA lattice) passing from a state to another state. Here, the values of these probabilities are identified by using a genetic algorithm. If nonrealistic values are allowed to the parameters, the predictions present better agreement with the historical series than if they are forced to present realistic values. A discussion about how the size of the PCA lattice affects the quality of the model predictions is presented.
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Assuntos Scopus
Arizona , USA , Epidemiological models , Model prediction , Probabilistic cellular automatons , Spatial scale , System identifications , Temporal evolution , Time step