Use este identificador para citar ou linkar para este item: http://repositorio.ufla.br/jspui/handle/1/13945
Título: Individual-Based Model (IBM): An alternative framework for epidemiological compartment models
Autores: Nepomuceno, Erivelton Geraldo
Takahashi, Ricardo Hiroshi Caldeira
Aguirre, Luis Antonio
Palavras-chave: Individual-Based model
Mathematical epidemiology
Stochastic fluctuations
Epidemiological compartment models
Modelo baseado em indivíduos
Epidemiologia matemática
Flutuações estocásticas
Modelo epidemiológico compartimental
Data do documento: 1-Ago-2017
Editor: Universidade Federal de Lavras
Citação: NEPOMUCENO, E. G.; TAKAHASHI, R. H. C.; AGUIRRE, L. A. Individual-Based Model (IBM): An alternative framework for epidemiological compartment models. Revista Brasileira de Biometria, Lavras, v. 34, n. 1, p. 133-162, mar. 2016.
Resumo: A traditional approach to model infectious diseases is to use compartment models based on dierential equations, such as the SIR (Susceptible-Infected-Recovered) model. These models explain average behavior, but are inadequate to account for stochastic fluctuations of epidemiological variables. An alternative approach is to use Individual-Based Model (IBM), that represent each individual as a set of features that change dynamically over time. This allows modeling population phenomena as aggregates of individual interactions. This paper presents a general framework to model epidemiological systems using IBM as an alternative to replace or complement epidemiological compartment models. The proposed modeling approach is shown to allow the study of some phenomena which are related to nite-population demographic stochastic fluctuation. In particular, a procedure for the computation of the probability of disease eradication within a time horizon in the case of systems which have mean-field endemic equilibrium is presented as a direct application of the proposed approach. It is shown, how this general framework may be described as an algorithm suitable to model dierent types of compartment  models. Numerical simulations illustrate how this approach may provide greater insight about a great variety of epidemiological systems.
URI: http://repositorio.ufla.br/jspui/handle/1/13945
Aparece nas coleções:Revista Brasileira de Biometria

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