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Inference for individual-level models of infectious diseases in large populations
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Deardon, Rob, Brooks, Stephen. P., Grenfell, Bryan T., Keeling, Matthew James, Tildesley, Michael J., Savill, Nicholas J., Shaw, Darren J. and Woolhouse, Mark E. J. (2010) Inference for individual-level models of infectious diseases in large populations. Statistica Sinica, Vol.20 (No.1). pp. 239-261. ISSN 1017-0405.
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Official URL: http://www3.stat.sinica.edu.tw/statistica/
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 clue 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 v that can be used to estimate parameters for such large, and/or incomplete, data. sets This is clone in the context of a study of the UK 2001 foot-and-mouth disease (FMD) epidemic
| Item Type: | Journal Article | ||||
|---|---|---|---|---|---|
| Subjects: | Q Science > QA Mathematics | ||||
| Divisions: | Faculty of Science, Engineering and Medicine > Science > Mathematics | ||||
| Journal or Publication Title: | Statistica Sinica | ||||
| Publisher: | Academia Sinica * Institute of Statistical Science | ||||
| ISSN: | 1017-0405 | ||||
| Official Date: | January 2010 | ||||
| Dates: |
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| Volume: | Vol.20 | ||||
| Number: | No.1 | ||||
| Number of Pages: | 23 | ||||
| Page Range: | pp. 239-261 | ||||
| Status: | Peer Reviewed | ||||
| Publication Status: | Published | ||||
| Access rights to Published version: | Restricted or Subscription Access | ||||
| Funder: | Wellcome Trust, Canada Foundation for Innovation (CFI), Natural Sciences and Engineering Research Council of Canada (NSERC) | ||||
| Grant number: | GR 068678 MA |
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