Tildesley, Michael J. and Ryan, Sadie J. (2012) Disease prevention versus data privacy : using landcover maps to inform spatial epidemic models. PLoS Computational Biology, Vol.8 (No.11). e1002723. doi:10.1371/journal.pcbi.1002723

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Abstract

The availability of epidemiological data in the early stages of an outbreak of an infectious disease is vital for modelers to make accurate predictions regarding the likely spread of disease and preferred intervention strategies. However, in some countries, the necessary demographic data are only available at an aggregate scale. We investigated the ability of models of livestock infectious diseases to predict epidemic spread and obtain optimal control policies in the event of imperfect, aggregated data. Taking a geographic information approach, we used land cover data to predict UK farm locations and investigated the influence of using these synthetic location data sets upon epidemiological predictions in the event of an outbreak of foot-and-mouth disease. When broadly classified land cover data were used to create synthetic farm locations, model predictions deviated significantly from those simulated on true data. However, when more resolved subclass land use data were used, moderate to highly accurate predictions of epidemic size, duration and optimal vaccination and ring culling strategies were obtained. This suggests that a geographic information approach may be useful where individual farm-level data are not available, to allow predictive analyses to be carried out regarding the likely spread of disease. This method can also be used for contingency planning in collaboration with policy makers to determine preferred control strategies in the event of a future outbreak of infectious disease in livestock.

Item Type:	Journal Article
Subjects:	S Agriculture > SF Animal culture
Divisions:	Faculty of Science > Centre for Complexity Science Faculty of Science > Mathematics
Library of Congress Subject Headings (LCSH):	Communicable diseases -- Transmission -- Mathematical models, Land cover, Farms -- Data processing
Journal or Publication Title:	PLoS Computational Biology
Publisher:	Public Library of Science
ISSN:	1553-7358
Official Date:	2012
Dates:	Date Event 2012 Published
Date of first compliant deposit:	23 December 2015
Volume:	Vol.8
Number:	No.11
Page Range:	e1002723
DOI:	10.1371/journal.pcbi.1002723
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Restricted or Subscription Access
Funder:	National Science Foundation (U.S.) (NSF), United States. Dept. of Homeland Security, United States. Dept. of Agriculture, University of Tennessee, Knoxville, National Institutes of Health (U.S.) (NIH), University of California, Santa Barbara, California
Grant number:	EF-0832858 (NSF), ST-108-000017 (DHS), EF-0553768 (NSF)

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