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

Lookup NU author(s): Professor Richard Boys, Philip Giles

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Abstract

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. © Springer-Verlag 2007.

Publication metadata

Author(s): Boys RJ, Giles PR

Publication type: Article

Publication status: Published

Journal: Journal of Mathematical Biology

Year: 2007

Volume: 55

Issue: 2

Pages: 223-247

Print publication date: 01/08/2007

ISSN (print): 0303-6812

ISSN (electronic): 1432-1416

Publisher: Springer

URL: http://dx.doi.org/10.1007/s00285-007-0081-y

DOI: 10.1007/s00285-007-0081-y

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