Bayesian and maximum likelihood estimation of SIR stochastic models / Estimação bayesiana e por maxima verossimilhança de modelos SIR estocasticos

AUTOR(ES)

Rodrigo Bonato Manfredini

DATA DE PUBLICAÇÃO

2009

RESUMO

Compartmental models have been widely used in order to model epidemics. Several methods have been proposed in the literature to estimate the models, specially, the least squares method, maximum likelihood estimation and Bayes estimators based on Monte Carlo simulation. In the most of real cases, the data are only partially observable. The work considers the case that all the data are observed and the case that only the removal times are available. The sampling properties of the maximum likelihood and Bayes estimators for complete and incomplete data are investigated through simulation.

ASSUNTO(S)

modelagem de dados epidemiologia epidemiology data modeling monte carlo method metodo de monte carlo

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