Werkman, Marleen, Tildesley, Michael J., Brooks-Pollock, Ellen and Keeling, Matthew James (2016) Preserving privacy whilst maintaining robust epidemiological predictions. Epidemics, 17 . pp. 35-41. doi:10.1016/j.epidem.2016.10.004 ISSN 1755-4365.

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Abstract

Mathematical models are invaluable tools for quantifying potential epidemics and devising optimal control strategies in case of an outbreak. State-of-the-art models increasingly require detailed individual farm-based and sensitive data, which may not be available due to either lack of capacity for data collection or privacy concerns. However, in many situations, aggregated data are available for use. In this study, we systematically investigate the accuracy of predictions made by mathematical models initialised with varying data aggregations, using the UK 2001 Foot-and-Mouth Disease Epidemic as a case study. We consider the scenario when the only data available are aggregated into spatial grid cells, and develop a metapopulation model where individual farms in a single subpopulation are assumed to behave uniformly and transmit randomly. We also adapt this standard metapopulation model to capture heterogeneity in farm size and composition, using farm census data. Our results show that homogeneous models based on aggregated data overestimate final epidemic size but can perform well for predicting spatial spread. Recognising heterogeneity in farm sizes improves predictions of the final epidemic size, identifying risk areas, determining the likelihood of epidemic take-off and identifying the optimal control strategy. In conclusion, in cases where individual farm-based data are not available, models can still generate meaningful predictions, although care must be taken in their interpretation and use.

Item Type:	Journal Article
Subjects:	S Agriculture > SF Animal culture
Divisions:	Faculty of Science, Engineering and Medicine > Science > Life Sciences (2010- ) Faculty of Science, Engineering and Medicine > Science > Mathematics
Library of Congress Subject Headings (LCSH):	Foot-and-mouth disease -- Epidemiology -- Mathematical models
Journal or Publication Title:	Epidemics
Publisher:	Elsevier BV
ISSN:	1755-4365
Official Date:	December 2016
Dates:	Date Event December 2016 Published 13 October 2016 Available 12 October 2016 Accepted 8 March 2016 Submitted
Volume:	17
Page Range:	pp. 35-41
DOI:	10.1016/j.epidem.2016.10.004
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Open Access (Creative Commons)
Date of first compliant deposit:	18 October 2016
Date of first compliant Open Access:	18 October 2016
Funder:	United States. Department of Homeland Security. Science and Technology Directorate, National Institutes of Health (U.S.) (NIH), Biotechnology and Biological Sciences Research Council (Great Britain) (BBSRC), Great Britain. Department for Environment, Food and Rural Affairs (DEFRA)
Grant number:	ST-108-000017 (Homeland), BB/K010972/3 (BBSRC)

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