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Real-time decision-making during emergency disease outbreaks

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Koelle, Katia, Probert, William J. M. , Jewell, Chris P., Werkman, Marleen, Fonnesbeck, Christopher J., Goto, Yoshitaka, Runge, Michael C., Sekiguchi, Satoshi, Shea, Katriona, Keeling, Matthew James, Ferrari, Matthew J. and Tildesley, Michael J. (2018) Real-time decision-making during emergency disease outbreaks. PLoS Computational Biology, 14 (7). e1006202. doi:10.1371/journal.pcbi.1006202

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Official URL: http://dx.doi.org/10.1371/journal.pcbi.1006202

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Abstract

In the event of a new infectious disease outbreak, mathematical and simulation models are commonly used to inform policy by evaluating which control strategies will minimize the impact of the epidemic. In the early stages of such outbreaks, substantial parameter uncertainty may limit the ability of models to provide accurate predictions, and policymakers do not have the luxury of waiting for data to alleviate this state of uncertainty. For policymakers, however, it is the selection of the optimal control intervention in the face of uncertainty, rather than accuracy of model predictions, that is the measure of success that counts. We simulate the process of real-time decision-making by fitting an epidemic model to observed, spatially-explicit, infection data at weekly intervals throughout two historical outbreaks of foot-and-mouth disease, UK in 2001 and Miyazaki, Japan in 2010, and compare forward simulations of the impact of switching to an alternative control intervention at the time point in question. These are compared to policy recommendations generated in hindsight using data from the entire outbreak, thereby comparing the best we could have done at the time with the best we could have done in retrospect.

Item Type: Journal Article
Subjects: R Medicine > RA Public aspects of medicine
Divisions: Faculty of Science > Life Sciences (2010- )
Library of Congress Subject Headings (LCSH): Epidemics -- Transmission -- Mathematical models, Communicable diseases -- Transmission -- Mathematical models, Decision making -- Mathematical models
Journal or Publication Title: PLoS Computational Biology
Publisher: Public Library of Science
ISSN: 1553-7358
Official Date: 24 July 2018
Dates:
DateEvent
24 July 2018Published
15 May 2018Accepted
Volume: 14
Number: 7
Article Number: e1006202
DOI: 10.1371/journal.pcbi.1006202
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Open Access
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
BB/K010972/4[BBSRC] Biotechnology and Biological Sciences Research Councilhttp://dx.doi.org/10.13039/501100000268
UNSPECIFIEDNational Science Foundationhttp://dx.doi.org/10.13039/100000001
1 R01 GM10524 7-01National Institutes of Healthhttp://dx.doi.org/10.13039/100000002
UNSPECIFIEDU.S. Department of Homeland Securityhttp://dx.doi.org/10.13039/100000180

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