A tutorial introduction to Bayesian inference for stochastic epidemic models using Markov chain Monte Carlo methods.

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Philip D O'Neill

Author Information

Philip D O'Neill: School of Mathematical Sciences, University of Nottingham, University Park, Nottingham NG7 2RD, UK. pdo@maths.nott.ac.uk

PMID: 12387918 DOI: 10.1016/s0025-5564(02)00109-8

Recent Bayesian methods for the analysis of infectious disease outbreak data using stochastic epidemic models are reviewed. These methods rely on Markov chain Monte Carlo methods. Both temporal and non-temporal data are considered. The methods are illustrated with a number of examples featuring different models and datasets.

Algorithms

Bayes Theorem

Disease Outbreaks

Epidemiologic Methods

Humans

Markov Chains

Models, Biological

Monte Carlo Method

Stochastic Processes

Journal Article

Curing Effect on Durability of Cement Mortar with GGBS: Experimental and Numerical Study.Stochastic dynamic model of SARS spreading.A network epidemic model for online community commissioning data.Fitting mechanistic epidemic models to data: A comparison of simple Markov chain Monte Carlo approaches.Assessing the role of undetected colonization and isolation precautions in reducing methicillin-resistant Staphylococcus aureus transmission in intensive care units.Inference for discretely observed stochastic kinetic networks with applications to epidemic modeling.The outbreak pattern of the SARS cases in Asia.Inference for ecological dynamical systems: a case study of two endemic diseases.Nested sampling for Bayesian model comparison in the context of Salmonella disease dynamics.Efficient Data Augmentation for Fitting Stochastic Epidemic Models to Prevalence Data.

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