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Realistic assumptions about spatial locations and clustering of premises matter for models of foot-and-mouth disease spread in the United States
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Sellman, Stefan, Tildesley, Michael J., Burdett, Christopher L., Miller, Ryan S., Hallman, Clayton, Webb, Colleen T., Wennergren, Uno, Portacci, Katie and Lindström, Tom (2020) Realistic assumptions about spatial locations and clustering of premises matter for models of foot-and-mouth disease spread in the United States. PLoS Computational Biology, 16 (2). e1007641. doi:10.1371/journal.pcbi.1007641 ISSN 1553-7358.
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WRAP-realistic-assumptions-spatial-locations-clustering-foot-mouth-Tildesley-2020.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Public Domain Dedication. Download (2676Kb) | Preview |
Official URL: https://doi.org/10.1371/journal.pcbi.1007641
Abstract
Spatially explicit livestock disease models require demographic data for individual farms or premises. In the U.S., demographic data are only available aggregated at county or coarser scales, so disease models must rely on assumptions about how individual premises are distributed within counties. Here, we addressed the importance of realistic assumptions for this purpose. We compared modeling of foot and mouth disease (FMD) outbreaks using simple randomization of locations to premises configurations predicted by the Farm Location and Agricultural Production Simulator (FLAPS), which infers location based on features such as topography, land-cover, climate, and roads. We focused on three premises-level Susceptible-Exposed-Infectious-Removed models available from the literature, all using the same kernel approach but with different parameterizations and functional forms. By computing the basic reproductive number of the infection (R0) for both FLAPS and randomized configurations, we investigated how spatial locations and clustering of premises affects outbreak predictions. Further, we performed stochastic simulations to evaluate if identified differences were consistent for later stages of an outbreak. Using Ripley's K to quantify clustering, we found that FLAPS configurations were substantially more clustered at the scales relevant for the implemented models, leading to a higher frequency of nearby premises compared to randomized configurations. As a result, R0 was typically higher in FLAPS configurations, and the simulation study corroborated the pattern for later stages of outbreaks. Further, both R0 and simulations exhibited substantial spatial heterogeneity in terms of differences between configurations. Thus, using realistic assumptions when de-aggregating locations based on available data can have a pronounced effect on epidemiological predictions, affecting if, where, and to what extent FMD may invade the population. We conclude that methods such as FLAPS should be preferred over randomization approaches.
| Item Type: | Journal Article | |||||||||
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| Subjects: | S Agriculture > SF Animal culture | |||||||||
| Divisions: | Faculty of Science, Engineering and Medicine > Science > Life Sciences (2010- ) | |||||||||
| SWORD Depositor: | Library Publications Router | |||||||||
| Library of Congress Subject Headings (LCSH): | Foot-and-mouth disease , Foot-and-mouth disease -- United States, Foot-and-mouth disease -- Transmission -- Mathematical models -- United States | |||||||||
| Journal or Publication Title: | PLoS Computational Biology | |||||||||
| Publisher: | Public Library of Science | |||||||||
| ISSN: | 1553-7358 | |||||||||
| Official Date: | 20 February 2020 | |||||||||
| Dates: |
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| Volume: | 16 | |||||||||
| Number: | 2 | |||||||||
| Article Number: | e1007641 | |||||||||
| DOI: | 10.1371/journal.pcbi.1007641 | |||||||||
| Status: | Peer Reviewed | |||||||||
| Publication Status: | Published | |||||||||
| Access rights to Published version: | Open Access (Creative Commons) | |||||||||
| Date of first compliant deposit: | 3 April 2020 | |||||||||
| Date of first compliant Open Access: | 3 April 2020 | |||||||||
| RIOXX Funder/Project Grant: |
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