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Inference for Individual-Level Models of Infectious Diseases in Large Populations

Author(s): Deardon, Rob; Brooks, Stephen P.; Grenfell, Bryan T.; Keeling, Matthew J.; Tildesley, Michael J.; et al

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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.
Publication Date: Jan-2010
Citation: Deardon, Rob, Brooks, Stephen P., Grenfell, Bryan T., Keeling, Matthew J., Tildesley, Michael J., Savill, Nicholas J., Shaw, Darren J., Woolhouse, Mark E.J. (2010). Inference for Individual-Level Models of Infectious Diseases in Large Populations. Stat Sin, 20 (1), 239 - 261
ISSN: 1017-0405
Pages: 239 - 261
Language: eng
Type of Material: Journal Article
Journal/Proceeding Title: Statistica Sinica
Version: Author's manuscript

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