Bayesian inference for stochastic epidemic models with time-inhomogeneous removal rates.

Bayesian inference for stochastic epidemic models with time-inhomogeneous removal rates. Research Abstract Details 

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Bayesian inference for stochastic epidemic models with time-inhomogeneous removal rates. Abstract Text:

Richard J Boys,Philip R Giles,

Stochastic compartmental models of the SEIR type are often used to make inferences on epidemic processes from partially observed data in which only removal times are available. For many epidemics, the assumption of constant removal rates is not plausible. We develop methods for models in which these rates are a time-dependent step function. A reversible jump MCMC algorithm is described that permits Bayesian inferences to be made on model parameters, particularly those associated with the step function. The method is applied to two datasets on outbreaks of smallpox and a respiratory disease. The analyses highlight the importance of allowing for time dependence by contrasting the predictive distributions for the removal times and comparing them with the observed data.

Bayesian inference for stochastic epidemic models with time-inhomogeneous removal rates. Publishing Authors By Initials

RJ Boys,PR Giles,

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Bayesian inference for stochastic epidemic models with time-inhomogeneous removal rates. Journal Published:

PUBLICATION TYPE: Journal Article

Journal: Journal of mathematical biology

VOLUME: 55

Page Numbers: 223-47

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ISSN: 0303-6812

DAY: 15

MONTH: 03

YEAR: 2007

Bayesian inference for stochastic epidemic models with time-inhomogeneous removal rates. Information

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LANGUAGE: eng

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Grant and Affiliation Information for Bayesian inference for stochastic epidemic models with time-inhomogeneous removal rates.

AFFILIATION: School of Mathematics and Statistics, University of Newcastle upon Tyne, Newcastle upon Tyne, NE1 7RU, UK, richard.boys@ncl.ac.uk.

Country: Germany

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MEDLINETA: J Math Biol

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