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Computational Modeling of Interventions and Protective Thresholds to Prevent Disease Transmission in Deploying Populations

Military personnel are deployed abroad for missions ranging from humanitarian relief efforts to combat actions; delay or interruption in these activities due to disease transmission can cause operational disruptions, significant economic loss, and stressed or exceeded military medical resources. Dep...

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Detalles Bibliográficos
Autores principales: Burgess, Colleen, Peace, Angela, Everett, Rebecca, Allegri, Buena, Garman, Patrick
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4070471/
https://www.ncbi.nlm.nih.gov/pubmed/25009579
http://dx.doi.org/10.1155/2014/785752
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author Burgess, Colleen
Peace, Angela
Everett, Rebecca
Allegri, Buena
Garman, Patrick
author_facet Burgess, Colleen
Peace, Angela
Everett, Rebecca
Allegri, Buena
Garman, Patrick
author_sort Burgess, Colleen
collection PubMed
description Military personnel are deployed abroad for missions ranging from humanitarian relief efforts to combat actions; delay or interruption in these activities due to disease transmission can cause operational disruptions, significant economic loss, and stressed or exceeded military medical resources. Deployed troops function in environments favorable to the rapid and efficient transmission of many viruses particularly when levels of protection are suboptimal. When immunity among deployed military populations is low, the risk of vaccine-preventable disease outbreaks increases, impacting troop readiness and achievement of mission objectives. However, targeted vaccination and the optimization of preexisting immunity among deployed populations can decrease the threat of outbreaks among deployed troops. Here we describe methods for the computational modeling of disease transmission to explore how preexisting immunity compares with vaccination at the time of deployment as a means of preventing outbreaks and protecting troops and mission objectives during extended military deployment actions. These methods are illustrated with five modeling case studies for separate diseases common in many parts of the world, to show different approaches required in varying epidemiological settings.
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spelling pubmed-40704712014-07-09 Computational Modeling of Interventions and Protective Thresholds to Prevent Disease Transmission in Deploying Populations Burgess, Colleen Peace, Angela Everett, Rebecca Allegri, Buena Garman, Patrick Comput Math Methods Med Research Article Military personnel are deployed abroad for missions ranging from humanitarian relief efforts to combat actions; delay or interruption in these activities due to disease transmission can cause operational disruptions, significant economic loss, and stressed or exceeded military medical resources. Deployed troops function in environments favorable to the rapid and efficient transmission of many viruses particularly when levels of protection are suboptimal. When immunity among deployed military populations is low, the risk of vaccine-preventable disease outbreaks increases, impacting troop readiness and achievement of mission objectives. However, targeted vaccination and the optimization of preexisting immunity among deployed populations can decrease the threat of outbreaks among deployed troops. Here we describe methods for the computational modeling of disease transmission to explore how preexisting immunity compares with vaccination at the time of deployment as a means of preventing outbreaks and protecting troops and mission objectives during extended military deployment actions. These methods are illustrated with five modeling case studies for separate diseases common in many parts of the world, to show different approaches required in varying epidemiological settings. Hindawi Publishing Corporation 2014 2014-06-09 /pmc/articles/PMC4070471/ /pubmed/25009579 http://dx.doi.org/10.1155/2014/785752 Text en Copyright © 2014 Colleen Burgess et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Burgess, Colleen
Peace, Angela
Everett, Rebecca
Allegri, Buena
Garman, Patrick
Computational Modeling of Interventions and Protective Thresholds to Prevent Disease Transmission in Deploying Populations
title Computational Modeling of Interventions and Protective Thresholds to Prevent Disease Transmission in Deploying Populations
title_full Computational Modeling of Interventions and Protective Thresholds to Prevent Disease Transmission in Deploying Populations
title_fullStr Computational Modeling of Interventions and Protective Thresholds to Prevent Disease Transmission in Deploying Populations
title_full_unstemmed Computational Modeling of Interventions and Protective Thresholds to Prevent Disease Transmission in Deploying Populations
title_short Computational Modeling of Interventions and Protective Thresholds to Prevent Disease Transmission in Deploying Populations
title_sort computational modeling of interventions and protective thresholds to prevent disease transmission in deploying populations
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4070471/
https://www.ncbi.nlm.nih.gov/pubmed/25009579
http://dx.doi.org/10.1155/2014/785752
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