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Statistica Sinica 20 (2010), 239-261



Rob Deardon$^1$, Stephen P. Brooks$^2$, Bryan T. Grenfell$^3$,
Matthew J. Keeling$^4$, Michael J. Tildesley$^4$, Nicholas J. Savill$^5$,
Darren J. Shaw$^5$ and Mark E. J. Woolhouse$^5$

$^1$University of Guelph, $^2$University of Cambridge, $^3$Princeton University,
$^4$University of Warwick and $^5$University of Edinburgh

Abstract: Individual Level Models (ILMs), a new class of models, are being applied to infectious epidemic data to aid in the understanding of the spatio-temporal dynamics of infectious diseases. These models are highly flexible and intuitive, and can be parameterised under a Bayesian framework via Markov chain Monte Carlo (MCMC) methods. Unfortunately, this parameterisation can be difficult to implement due to intense computational requirements when calculating the full posterior for large, or even moderately large, susceptible populations, or when missing data are present. Here we detail a methodology that can be used to estimate parameters for such large, and/or incomplete, data sets. This is done in the context of a study of the UK 2001 foot-and-mouth disease (FMD) epidemic.

Key words and phrases: Bayesian inference, computational methodology, foot-and-mouth disease, Markov chain Monte Carlo, missing data, Spatio-temporal epidemic modelling.

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