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Strategies for controlling non-transmissible infection outbreaks using a large human movement data set
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Hancock, Penelope A., Rehman, Yasmin, Hall, Ian M., Edeghere, Obaghe, Danon, Leon, House, Thomas A. and Keeling, Matthew James (2014) Strategies for controlling non-transmissible infection outbreaks using a large human movement data set. PLoS Computational Biology, Volume 10 (Number 9). pp. 1-8. Article number e1003809. doi:10.1371/journal.pcbi.1003809 ISSN 1553-7358.
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Official URL: http://dx.doi.org/10.1371/journal.pcbi.1003809
Abstract
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.
Item Type: | Journal Article | ||||||||
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Subjects: | R Medicine > RA Public aspects of medicine | ||||||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Life Sciences (2010- ) Faculty of Science, Engineering and Medicine > Science > Mathematics |
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Library of Congress Subject Headings (LCSH): | Communicable diseases -- Epidemiology, Communicable diseases -- Computer simulation | ||||||||
Journal or Publication Title: | PLoS Computational Biology | ||||||||
Publisher: | Public Library of Science | ||||||||
ISSN: | 1553-7358 | ||||||||
Official Date: | 11 September 2014 | ||||||||
Dates: |
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Volume: | Volume 10 | ||||||||
Number: | Number 9 | ||||||||
Number of Pages: | 8 | ||||||||
Page Range: | pp. 1-8 | ||||||||
Article Number: | Article number e1003809 | ||||||||
DOI: | 10.1371/journal.pcbi.1003809 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||||
Date of first compliant deposit: | 28 December 2015 | ||||||||
Date of first compliant Open Access: | 28 December 2015 | ||||||||
Funder: | Wellcome Trust (London, England), Engineering and Physical Sciences Research Council (EPSRC), Leverhulme Trust (LT) | ||||||||
Grant number: | 089237/Z/09/Z (WT) |
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