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Strategies for Controlling Non-Transmissible Infection Outbreaks Using a Large Human Movement Data Set

Prediction and control of the spread of infectious disease in human populations benefits greatly from our growing capacity to quantify human movement behavior. Here we develop a mathematical model for non-transmissible infections contracted from a localized environmental source, informed by a detail...

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Autores principales: Hancock, Penelope A., Rehman, Yasmin, Hall, Ian M., Edeghere, Obaghe, Danon, Leon, House, Thomas A., Keeling, Matthew J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4161289/
https://www.ncbi.nlm.nih.gov/pubmed/25211122
http://dx.doi.org/10.1371/journal.pcbi.1003809
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author Hancock, Penelope A.
Rehman, Yasmin
Hall, Ian M.
Edeghere, Obaghe
Danon, Leon
House, Thomas A.
Keeling, Matthew J.
author_facet Hancock, Penelope A.
Rehman, Yasmin
Hall, Ian M.
Edeghere, Obaghe
Danon, Leon
House, Thomas A.
Keeling, Matthew J.
author_sort Hancock, Penelope A.
collection PubMed
description Prediction and control of the spread of infectious disease in human populations benefits greatly from our growing capacity to quantify human movement behavior. Here we develop a mathematical model for non-transmissible infections contracted from a localized environmental source, informed by a detailed description of movement patterns of the population of Great Britain. The model is applied to outbreaks of Legionnaires' disease, a potentially life-threatening form of pneumonia caused by the bacteria Legionella pneumophilia. We use case-report data from three recent outbreaks that have occurred in Great Britain where the source has already been identified by public health agencies. We first demonstrate that the amount of individual-level heterogeneity incorporated in the movement data greatly influences our ability to predict the source location. The most accurate predictions were obtained using reported travel histories to describe movements of infected individuals, but using detailed simulation models to estimate movement patterns offers an effective fast alternative. Secondly, once the source is identified, we show that our model can be used to accurately determine the population likely to have been exposed to the pathogen, and hence predict the residential locations of infected individuals. The results give rise to an effective control strategy that can be implemented rapidly in response to an outbreak.
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spelling pubmed-41612892014-09-17 Strategies for Controlling Non-Transmissible Infection Outbreaks Using a Large Human Movement Data Set Hancock, Penelope A. Rehman, Yasmin Hall, Ian M. Edeghere, Obaghe Danon, Leon House, Thomas A. Keeling, Matthew J. PLoS Comput Biol Research Article Prediction and control of the spread of infectious disease in human populations benefits greatly from our growing capacity to quantify human movement behavior. Here we develop a mathematical model for non-transmissible infections contracted from a localized environmental source, informed by a detailed description of movement patterns of the population of Great Britain. The model is applied to outbreaks of Legionnaires' disease, a potentially life-threatening form of pneumonia caused by the bacteria Legionella pneumophilia. We use case-report data from three recent outbreaks that have occurred in Great Britain where the source has already been identified by public health agencies. We first demonstrate that the amount of individual-level heterogeneity incorporated in the movement data greatly influences our ability to predict the source location. The most accurate predictions were obtained using reported travel histories to describe movements of infected individuals, but using detailed simulation models to estimate movement patterns offers an effective fast alternative. Secondly, once the source is identified, we show that our model can be used to accurately determine the population likely to have been exposed to the pathogen, and hence predict the residential locations of infected individuals. The results give rise to an effective control strategy that can be implemented rapidly in response to an outbreak. Public Library of Science 2014-09-11 /pmc/articles/PMC4161289/ /pubmed/25211122 http://dx.doi.org/10.1371/journal.pcbi.1003809 Text en © 2014 Hancock et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Hancock, Penelope A.
Rehman, Yasmin
Hall, Ian M.
Edeghere, Obaghe
Danon, Leon
House, Thomas A.
Keeling, Matthew J.
Strategies for Controlling Non-Transmissible Infection Outbreaks Using a Large Human Movement Data Set
title Strategies for Controlling Non-Transmissible Infection Outbreaks Using a Large Human Movement Data Set
title_full Strategies for Controlling Non-Transmissible Infection Outbreaks Using a Large Human Movement Data Set
title_fullStr Strategies for Controlling Non-Transmissible Infection Outbreaks Using a Large Human Movement Data Set
title_full_unstemmed Strategies for Controlling Non-Transmissible Infection Outbreaks Using a Large Human Movement Data Set
title_short Strategies for Controlling Non-Transmissible Infection Outbreaks Using a Large Human Movement Data Set
title_sort strategies for controlling non-transmissible infection outbreaks using a large human movement data set
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4161289/
https://www.ncbi.nlm.nih.gov/pubmed/25211122
http://dx.doi.org/10.1371/journal.pcbi.1003809
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