Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Bayesian Analysis for Emerging Infectious Diseases
AU - Jewell, Chris
AU - Kypraios, Theodore
AU - Neal, Peter
AU - Roberts, Gareth
PY - 2009
Y1 - 2009
N2 - 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 param-eters governing infectious mechanisms will typically be unknown. On the otherhand, epidemic control strategies need to be decided rapidly as data accumulate.In this paper we present a fully Bayesian methodology for performing inferenceand online prediction for epidemics in structured populations. Key features ofour approach are the development of an MCMC- (and adaptive MCMC-) basedmethodology 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.
AB - 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 param-eters governing infectious mechanisms will typically be unknown. On the otherhand, epidemic control strategies need to be decided rapidly as data accumulate.In this paper we present a fully Bayesian methodology for performing inferenceand online prediction for epidemics in structured populations. Key features ofour approach are the development of an MCMC- (and adaptive MCMC-) basedmethodology 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.
U2 - 10.1214/09-BA417
DO - 10.1214/09-BA417
M3 - Journal article
VL - 4
SP - 465
EP - 496
JO - Bayesian Analysis
JF - Bayesian Analysis
SN - 1931-6690
IS - 3
ER -