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Jewell, Chris P., Kypraios, Theodore, Neal, Peter and Roberts, Gareth O. (2009) Bayesian analysis for emerging infectious diseases. Bayesian analysis, Vol.4 (No.3). pp. 465-496. doi:10.1214/09-BA417
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Official URL: http://dx.doi.org/10.1214/09-BA417
Abstract
Infectious diseases both within human and animal populations often pose serious health and socioeconomic risks. From a statistical perspective, their prediction is complicated by the fact that no two epidemics are identical due to changing contact habits, mutations of infectious agents, and changing human and animal behaviour in response to the presence of an epidemic. Thus model parameters governing infectious mechanisms will typically be unknown. On the other hand, epidemic control strategies need to be decided rapidly as data accumulate. In this paper we present a fully Bayesian methodology for performing inference and online prediction for epidemics in structured populations. Key features of our approach are the development of an MCMC- (and adaptive MCMC-) based methodology for parameter estimation, epidemic prediction, and online assessment of risk from currently unobserved infections. We illustrate our methods using two complementary studies: an analysis of the 2001 UK Foot and Mouth epidemic, and modelling the potential risk from a possible future Avian Influenza epidemic to the UK Poultry industry.
| Item Type: | Journal Article | ||||
|---|---|---|---|---|---|
| Subjects: | Q Science > QA Mathematics | ||||
| Divisions: | Faculty of Science > Statistics | ||||
| Library of Congress Subject Headings (LCSH): | Epidemics -- Mathematical models | ||||
| Journal or Publication Title: | Bayesian analysis | ||||
| Publisher: | Int Soc Bayesian Analysis | ||||
| ISSN: | 1931-6690 | ||||
| Official Date: | 2009 | ||||
| Dates: |
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| Volume: | Vol.4 | ||||
| Number: | No.3 | ||||
| Number of Pages: | 32 | ||||
| Page Range: | pp. 465-496 | ||||
| DOI: | 10.1214/09-BA417 | ||||
| Status: | Peer Reviewed | ||||
| Publication Status: | Published | ||||
| Access rights to Published version: | Open Access |
Data sourced from Thomson Reuters' Web of Knowledge
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