Bayesian inference for an emerging arboreal epidemic in the presence of control
Identifieur interne : 000B80 ( Main/Curation ); précédent : 000B79; suivant : 000B81Bayesian 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.
Abstract
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:
DOI: 10.1073/pnas.1310997111
PubMed: 24711393
PubMed Central: 4035939
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PMC:4035939Le document en format XML
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<author><name sortKey="Parry, Matthew" sort="Parry, Matthew" uniqKey="Parry M" first="Matthew" last="Parry">Matthew Parry</name>
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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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