Realistic assumptions about spatial locations and clustering of premises matter for models of foot-and-mouth disease spread in the United States.
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. He...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2020-02-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1007641 |
| _version_ | 1818584853295136768 |
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| author | Stefan Sellman Michael J Tildesley Christopher L Burdett Ryan S Miller Clayton Hallman Colleen T Webb Uno Wennergren Katie Portacci Tom Lindström |
| author_facet | Stefan Sellman Michael J Tildesley Christopher L Burdett Ryan S Miller Clayton Hallman Colleen T Webb Uno Wennergren Katie Portacci Tom Lindström |
| author_sort | Stefan Sellman |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-16T08:27:46Z |
| format | Article |
| id | doaj.art-5eb13ad14eb94c09a7eba7a08c43c3ba |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-12-16T08:27:46Z |
| publishDate | 2020-02-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-5eb13ad14eb94c09a7eba7a08c43c3ba2022-12-21T22:37:57ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582020-02-01162e100764110.1371/journal.pcbi.1007641Realistic assumptions about spatial locations and clustering of premises matter for models of foot-and-mouth disease spread in the United States.Stefan SellmanMichael J TildesleyChristopher L BurdettRyan S MillerClayton HallmanColleen T WebbUno WennergrenKatie PortacciTom LindströmSpatially 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.https://doi.org/10.1371/journal.pcbi.1007641 |
| spellingShingle | Stefan Sellman Michael J Tildesley Christopher L Burdett Ryan S Miller Clayton Hallman Colleen T Webb Uno Wennergren Katie Portacci Tom Lindström 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 |
| title | Realistic assumptions about spatial locations and clustering of premises matter for models of foot-and-mouth disease spread in the United States. |
| title_full | Realistic assumptions about spatial locations and clustering of premises matter for models of foot-and-mouth disease spread in the United States. |
| title_fullStr | Realistic assumptions about spatial locations and clustering of premises matter for models of foot-and-mouth disease spread in the United States. |
| title_full_unstemmed | Realistic assumptions about spatial locations and clustering of premises matter for models of foot-and-mouth disease spread in the United States. |
| title_short | Realistic assumptions about spatial locations and clustering of premises matter for models of foot-and-mouth disease spread in the United States. |
| title_sort | realistic assumptions about spatial locations and clustering of premises matter for models of foot and mouth disease spread in the united states |
| url | https://doi.org/10.1371/journal.pcbi.1007641 |
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