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:	Penelope A Hancock, Yasmin Rehman, Ian M Hall, Obaghe Edeghere, Leon Danon, Thomas A House, Matthew J Keeling
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/77e028c1a305448d83adb4c5687bddfd
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spelling	oai:doaj.org-article:77e028c1a305448d83adb4c5687bddfd2021-11-25T05:40:46ZStrategies for controlling non-transmissible infection outbreaks using a large human movement data set.1553-734X1553-735810.1371/journal.pcbi.1003809https://doaj.org/article/77e028c1a305448d83adb4c5687bddfd2014-09-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1003809https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Prediction 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.Penelope A HancockYasmin RehmanIan M HallObaghe EdeghereLeon DanonThomas A HouseMatthew J KeelingPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 10, Iss 9, p e1003809 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Penelope A Hancock Yasmin Rehman Ian M Hall Obaghe Edeghere Leon Danon Thomas A House Matthew J Keeling Strategies for controlling non-transmissible infection outbreaks using a large human movement data set.
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.
format	article
author	Penelope A Hancock Yasmin Rehman Ian M Hall Obaghe Edeghere Leon Danon Thomas A House Matthew J Keeling
author_facet	Penelope A Hancock Yasmin Rehman Ian M Hall Obaghe Edeghere Leon Danon Thomas A House Matthew J Keeling
author_sort	Penelope A Hancock
title	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_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_sort	strategies for controlling non-transmissible infection outbreaks using a large human movement data set.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/77e028c1a305448d83adb4c5687bddfd
work_keys_str_mv	AT penelopeahancock strategiesforcontrollingnontransmissibleinfectionoutbreaksusingalargehumanmovementdataset AT yasminrehman strategiesforcontrollingnontransmissibleinfectionoutbreaksusingalargehumanmovementdataset AT ianmhall strategiesforcontrollingnontransmissibleinfectionoutbreaksusingalargehumanmovementdataset AT obagheedeghere strategiesforcontrollingnontransmissibleinfectionoutbreaksusingalargehumanmovementdataset AT leondanon strategiesforcontrollingnontransmissibleinfectionoutbreaksusingalargehumanmovementdataset AT thomasahouse strategiesforcontrollingnontransmissibleinfectionoutbreaksusingalargehumanmovementdataset AT matthewjkeeling strategiesforcontrollingnontransmissibleinfectionoutbreaksusingalargehumanmovementdataset
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Strategies for controlling non-transmissible infection outbreaks using a large human movement data set.

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