The Effect of Spatial Scale on Predicting Time Series: A Study on Epidemiological System Identification
Contribuinte(s) |
UNIVERSIDADE DE SÃO PAULO |
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Data(s) |
17/04/2012
17/04/2012
2009
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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. Copyright (C) 2009 L. H. A. Monteiro et al. CNPq |
Identificador |
MATHEMATICAL PROBLEMS IN ENGINEERING, 2009 1024-123X http://producao.usp.br/handle/BDPI/14706 10.1155/2009/137854 |
Idioma(s) |
eng |
Publicador |
HINDAWI PUBLISHING CORPORATION |
Relação |
Mathematical Problems in Engineering |
Direitos |
openAccess Copyright HINDAWI PUBLISHING CORPORATION |
Palavras-Chave | #CELLULAR-AUTOMATA #DYNAMICS #PATTERN #Engineering, Multidisciplinary #Mathematics, Interdisciplinary Applications |
Tipo |
article original article publishedVersion |