Tildesley, Michael J., House, Thomas A., Bruhn, Mark C., Curry, Ross J., O'Neil, Maggie, Allpress, Justine L. E., Smith, Gary and Keeling, Matthew James (2010) Impact of spatial clustering on disease transmission and optimal control. Proceedings of the National Academy of Sciences of the United States of America, Vol.107 (No.3). pp. 1041-1046. doi:10.1073/pnas.0909047107

Preview	PDF WRAP_Tildesley_1070268-lb-150611-maintext_tildesley.pdf - Accepted Version - Requires a PDF viewer. Download (155Kb)
Preview	PDF WRAP_House_maintextfigures.pdf - Accepted Version - Requires a PDF viewer. Download (1647Kb)
Preview	PDF WRAP_House_supplementary_figures_tildesley.pdf - Accepted Version - Requires a PDF viewer. Download (6Mb)
Preview	PDF WRAP_House_supplementary_text_tildesley.pdf - Requires a PDF viewer. Download (38Kb)
Preview	PDF WRAP_House_supplementary_text_tildesley.pdf - Accepted Version - Requires a PDF viewer. Download (38Kb)

Request Changes to record.

Abstract

Spatial heterogeneities and spatial separation of hosts are often seen as key factors when developing accurate predictive models of the spread of pathogens. The question we address in this paper is how coarse the resolution of the spatial data can be for a model to be a useful tool for informing control policies. We examine this problem using the specific case of foot-and-mouth disease spreading between farms using the formulation developed during the 2001 epidemic in the United Kingdom. We show that, if our model is carefully parameterized to match epidemic behavior, then using aggregate county-scale data from the United States is sufficient to closely determine optimal control measures (specifically ring culling). This result also holds when the approach is extended to theoretical distributions of farms where the spatial clustering can be manipulated to extremes. We have therefore shown that, although spatial structure can be critically important in allowing us to predict the emergent population-scale behavior from a knowledge of the individual-level dynamics, for this specific applied question, such structure is mostly subsumed in the parameterization allowing us to make policy predictions in the absence of high-quality spatial information. We believe that this approach will be of considerable benefit across a range of disciplines where data are only available at intermediate spatial scales.

Item Type:	Journal Article
Subjects:	Q Science > QA Mathematics S Agriculture > SF Animal culture
Divisions:	Faculty of Science > Life Sciences (2010- ) > Biological Sciences ( -2010) Faculty of Science > Mathematics
Library of Congress Subject Headings (LCSH):	Veterinary epidemiology -- Mathematical models, Pathogenic microorganisms -- Mathematical models, Foot-and-mouth disease virus -- Mathematical models, Animal health -- Law and legislation
Journal or Publication Title:	Proceedings of the National Academy of Sciences of the United States of America
Publisher:	National Academy of Sciences
ISSN:	0027-8424
Official Date:	19 January 2010
Dates:	Date Event 19 January 2010 Published
Volume:	Vol.107
Number:	No.3
Number of Pages:	6
Page Range:	pp. 1041-1046
DOI:	10.1073/pnas.0909047107
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Restricted or Subscription Access
Funder:	Great Britain. Dept. for Environment, Food & Rural Affairs (DEFRA), National Institute of General Medical Sciences (U.S.) (NIGMS), National Institutes of Health (U.S.) (NIH), Wellcome Trust (London, England), Scottish Funding Council (SFC), United States. Dept. of Homeland Security. Science and Technology Directorate

Data sourced from Thomson Reuters' Web of Knowledge

Request changes or add full text files to a record

Repository staff actions (login required)

View Item

Downloads