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Early Decision Indicators for Foot-and-Mouth Disease Outbreaks in Non-Endemic Countries

Disease managers face many challenges when deciding on the most effective control strategy to manage an outbreak of foot-and-mouth disease (FMD). Decisions have to be made under conditions of uncertainty and where the situation is continually evolving. In addition, resources for control are often li...

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Autores principales: Garner, Michael G., East, Iain J., Stevenson, Mark A., Sanson, Robert L., Rawdon, Thomas G., Bradhurst, Richard A., Roche, Sharon E., Van Ha, Pham, Kompas, Tom
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5127847/
https://www.ncbi.nlm.nih.gov/pubmed/27965969
http://dx.doi.org/10.3389/fvets.2016.00109
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author Garner, Michael G.
East, Iain J.
Stevenson, Mark A.
Sanson, Robert L.
Rawdon, Thomas G.
Bradhurst, Richard A.
Roche, Sharon E.
Van Ha, Pham
Kompas, Tom
author_facet Garner, Michael G.
East, Iain J.
Stevenson, Mark A.
Sanson, Robert L.
Rawdon, Thomas G.
Bradhurst, Richard A.
Roche, Sharon E.
Van Ha, Pham
Kompas, Tom
author_sort Garner, Michael G.
collection PubMed
description Disease managers face many challenges when deciding on the most effective control strategy to manage an outbreak of foot-and-mouth disease (FMD). Decisions have to be made under conditions of uncertainty and where the situation is continually evolving. In addition, resources for control are often limited. A modeling study was carried out to identify characteristics measurable during the early phase of a FMD outbreak that might be useful as predictors of the total number of infected places, outbreak duration, and the total area under control (AUC). The study involved two modeling platforms in two countries (Australia and New Zealand) and encompassed a large number of incursion scenarios. Linear regression, classification and regression tree, and boosted regression tree analyses were used to quantify the predictive value of a set of parameters on three outcome variables of interest: the total number of infected places, outbreak duration, and the total AUC. The number of infected premises (IPs), number of pending culls, AUC, estimated dissemination ratio, and cattle density around the index herd at days 7, 14, and 21 following first detection were associated with each of the outcome variables. Regression models for the size of the AUC had the highest predictive value (R(2) = 0.51–0.9) followed by the number of IPs (R(2) = 0.3–0.75) and outbreak duration (R(2) = 0.28–0.57). Predictability improved at later time points in the outbreak. Predictive regression models using various cut-points at day 14 to define small and large outbreaks had positive predictive values of 0.85–0.98 and negative predictive values of 0.52–0.91, with 79–97% of outbreaks correctly classified. On the strict assumption that each of the simulation models used in this study provide a realistic indication of the spread of FMD in animal populations. Our conclusion is that relatively simple metrics available early in a control program can be used to indicate the likely magnitude of an FMD outbreak under Australian and New Zealand conditions.
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spelling pubmed-51278472016-12-13 Early Decision Indicators for Foot-and-Mouth Disease Outbreaks in Non-Endemic Countries Garner, Michael G. East, Iain J. Stevenson, Mark A. Sanson, Robert L. Rawdon, Thomas G. Bradhurst, Richard A. Roche, Sharon E. Van Ha, Pham Kompas, Tom Front Vet Sci Veterinary Science Disease managers face many challenges when deciding on the most effective control strategy to manage an outbreak of foot-and-mouth disease (FMD). Decisions have to be made under conditions of uncertainty and where the situation is continually evolving. In addition, resources for control are often limited. A modeling study was carried out to identify characteristics measurable during the early phase of a FMD outbreak that might be useful as predictors of the total number of infected places, outbreak duration, and the total area under control (AUC). The study involved two modeling platforms in two countries (Australia and New Zealand) and encompassed a large number of incursion scenarios. Linear regression, classification and regression tree, and boosted regression tree analyses were used to quantify the predictive value of a set of parameters on three outcome variables of interest: the total number of infected places, outbreak duration, and the total AUC. The number of infected premises (IPs), number of pending culls, AUC, estimated dissemination ratio, and cattle density around the index herd at days 7, 14, and 21 following first detection were associated with each of the outcome variables. Regression models for the size of the AUC had the highest predictive value (R(2) = 0.51–0.9) followed by the number of IPs (R(2) = 0.3–0.75) and outbreak duration (R(2) = 0.28–0.57). Predictability improved at later time points in the outbreak. Predictive regression models using various cut-points at day 14 to define small and large outbreaks had positive predictive values of 0.85–0.98 and negative predictive values of 0.52–0.91, with 79–97% of outbreaks correctly classified. On the strict assumption that each of the simulation models used in this study provide a realistic indication of the spread of FMD in animal populations. Our conclusion is that relatively simple metrics available early in a control program can be used to indicate the likely magnitude of an FMD outbreak under Australian and New Zealand conditions. Frontiers Media S.A. 2016-11-30 /pmc/articles/PMC5127847/ /pubmed/27965969 http://dx.doi.org/10.3389/fvets.2016.00109 Text en Copyright © 2016 Garner, East, Stevenson, Sanson, Rawdon, Bradhurst, Roche, Van Ha and Kompas. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Veterinary Science
Garner, Michael G.
East, Iain J.
Stevenson, Mark A.
Sanson, Robert L.
Rawdon, Thomas G.
Bradhurst, Richard A.
Roche, Sharon E.
Van Ha, Pham
Kompas, Tom
Early Decision Indicators for Foot-and-Mouth Disease Outbreaks in Non-Endemic Countries
title Early Decision Indicators for Foot-and-Mouth Disease Outbreaks in Non-Endemic Countries
title_full Early Decision Indicators for Foot-and-Mouth Disease Outbreaks in Non-Endemic Countries
title_fullStr Early Decision Indicators for Foot-and-Mouth Disease Outbreaks in Non-Endemic Countries
title_full_unstemmed Early Decision Indicators for Foot-and-Mouth Disease Outbreaks in Non-Endemic Countries
title_short Early Decision Indicators for Foot-and-Mouth Disease Outbreaks in Non-Endemic Countries
title_sort early decision indicators for foot-and-mouth disease outbreaks in non-endemic countries
topic Veterinary Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5127847/
https://www.ncbi.nlm.nih.gov/pubmed/27965969
http://dx.doi.org/10.3389/fvets.2016.00109
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