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Bayesian inference for an emerging arboreal epidemic in the presence of control

Identifieur interne : 000B80 ( Main/Exploration ); précédent : 000B79; suivant : 000B81

Bayesian inference for an emerging arboreal epidemic in the presence of control

Auteurs : Matthew Parry [Nouvelle-Zélande, Royaume-Uni] ; Gavin J. Gibson [Royaume-Uni] ; Stephen Parnell [Royaume-Uni] ; Tim R. Gottwald ; Michael S. Irey ; Timothy C. Gast ; Christopher A. Gilligan [Royaume-Uni]

Source :

Proceedings of the National Academy of Sciences of the United States of America [ 0027-8424 ] ; 2014.

RBID : PMC:4035939

Abstract

Significance

Fast-moving and destructive emerging epidemics are seldom left to run their course because of the imperative to control further spread. Contemporaneous control measures, however, greatly complicate the characterization of the disease transmission process and the extraction of the epidemiological parameters of interest. The spread of Huanglongbing on orchard scales is used as a case study for modeling an emerging epidemic in the presence of control. We show that even with missing and censored data, and with seasonal and host age dependencies, it is possible to infer the parameters of a fully spatiotemporal stochastic model of disease spread. The value of the fitted model is to provide an engine for simulation studies of the costs and benefits of proposed disease control strategies.

Url:

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4035939

DOI: 10.1073/pnas.1310997111
PubMed: 24711393
PubMed Central: 4035939

Affiliations:

Nouvelle-Zélande, Royaume-Uni

Le document en format XML

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<affiliation wicri:level="1"><nlm:aff id="aff1">Department of Mathematics and Statistics,<institution>University of Otago</institution>
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<affiliation wicri:level="1"><nlm:aff id="aff2">Epidemiology and Modelling Group, Department of Plant Sciences,<institution>University of Cambridge</institution>
, Cambridge CB2 3EA,<country>United Kingdom</country>
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 33440</nlm:aff>
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<front><div type="abstract" xml:lang="en"><title>Significance</title>
<p>Fast-moving and destructive emerging epidemics are seldom left to run their course because of the imperative to control further spread. Contemporaneous control measures, however, greatly complicate the characterization of the disease transmission process and the extraction of the epidemiological parameters of interest. The spread of Huanglongbing on orchard scales is used as a case study for modeling an emerging epidemic in the presence of control. We show that even with missing and censored data, and with seasonal and host age dependencies, it is possible to infer the parameters of a fully spatiotemporal stochastic model of disease spread. The value of the fitted model is to provide an engine for simulation studies of the costs and benefits of proposed disease control strategies.</p>
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Bayesian inference for an emerging arboreal epidemic in the presence of control

Bayesian inference for an emerging arboreal epidemic in the presence of control

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Abstract

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