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Bayesian Analysis for Emerging Infectious Diseases

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Bayesian Analysis for Emerging Infectious Diseases. / Jewell, Chris; Kypraios, Theodore; Neal, Peter; Roberts, Gareth .

In: Bayesian Analysis, Vol. 4, No. 3, 2009, p. 465-496.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Jewell, C, Kypraios, T, Neal, P & Roberts, G 2009, 'Bayesian Analysis for Emerging Infectious Diseases', Bayesian Analysis, vol. 4, no. 3, pp. 465-496. https://doi.org/10.1214/09-BA417

APA

Jewell, C., Kypraios, T., Neal, P., & Roberts, G. (2009). Bayesian Analysis for Emerging Infectious Diseases. Bayesian Analysis, 4(3), 465-496. https://doi.org/10.1214/09-BA417

Vancouver

Jewell C, Kypraios T, Neal P, Roberts G. Bayesian Analysis for Emerging Infectious Diseases. Bayesian Analysis. 2009;4(3):465-496. https://doi.org/10.1214/09-BA417

Author

Jewell, Chris ; Kypraios, Theodore ; Neal, Peter ; Roberts, Gareth . / Bayesian Analysis for Emerging Infectious Diseases. In: Bayesian Analysis. 2009 ; Vol. 4, No. 3. pp. 465-496.

Bibtex

@article{bbc656bc69684cd2a1165a0997ae768f,
title = "Bayesian Analysis for Emerging Infectious Diseases",
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 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.",
author = "Chris Jewell and Theodore Kypraios and Peter Neal and Gareth Roberts",
year = "2009",
doi = "10.1214/09-BA417",
language = "English",
volume = "4",
pages = "465--496",
journal = "Bayesian Analysis",
issn = "1936-0975",
publisher = "Carnegie Mellon University",
number = "3",

}

RIS

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 - 1936-0975

IS - 3

ER -