| 1. |
- Brommesson, Peter, et al.
(author)
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Assessing intrastate shipments from interstate data and expert opinion
- 2021
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In: Royal Society Open Science. - : Royal Society of Open Science. - 2054-5703. ; 8:3
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Journal article (peer-reviewed)abstract
- Live animal shipments are a potential route for transmitting animal diseases between holdings and are crucial when modelling spread of infectious diseases. Yet, complete contact networks are not available in all countries, including the USA. Here, we considered a 10% sample of Interstate Certificate of Veterinary Inspections from 1 year (2009). We focused on distance dependence in contacts and investigated how different functional forms affect estimates of unobserved intrastate shipments. To further enhance our predictions, we included responses from an expert elicitation survey about the proportion of shipments moving intrastate. We used hierarchical Bayesian modelling to estimate parameters describing the kernel and effects of expert data. We considered three functional forms of spatial kernels and the inclusion or exclusion of expert data. The resulting six models were ranked by widely applicable information criterion (WAIC) and deviance information criterion (DIC) and evaluated through within- and out-of-sample validation. We showed that predictions of intrastate shipments were mildly influenced by the functional form of the spatial kernel but kernel shapes that permitted a fat tail at large distances while maintaining a plateau-shaped behaviour at short distances better were preferred. Furthermore, our study showed that expert data may not guarantee enhanced predictions when expert estimates are disparate.
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| 2. |
- Gilbertson, Kendra, et al.
(author)
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The Importance of Livestock Demography and Infrastructure in Driving Foot and Mouth Disease Dynamics
- 2022
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In: Life. - : MDPI. - 2075-1729. ; 12:10
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Journal article (peer-reviewed)abstract
- Transboundary animal diseases, such as foot and mouth disease (FMD) pose a significant and ongoing threat to global food security. Such diseases can produce large, spatially complex outbreaks. Mathematical models are often used to understand the spatio-temporal dynamics and create response plans for possible disease introductions. Model assumptions regarding transmission behavior of premises and movement patterns of livestock directly impact our understanding of the ecological drivers of outbreaks and how to best control them. Here, we investigate the impact that these assumptions have on model predictions of FMD outbreaks in the U.S. using models of livestock shipment networks and disease spread. We explore the impact of changing assumptions about premises transmission behavior, both by including within-herd dynamics, and by accounting for premises type and increasing the accuracy of shipment predictions. We find that the impact these assumptions have on outbreak predictions is less than the impact of the underlying livestock demography, but that they are important for investigating some response objectives, such as the impact on trade. These results suggest that demography is a key ecological driver of outbreaks and is critical for making robust predictions but that understanding management objectives is also important when making choices about model assumptions.
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| 3. |
- Sellman, Stefan, et al.
(author)
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Modeling nation-wide US swine movement networks at the resolution of the individual premises
- 2022
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In: Epidemics. - : Elsevier. - 1755-4365 .- 1878-0067. ; 41
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Journal article (peer-reviewed)abstract
- The spread of infectious livestock diseases is a major cause for concern in modern agricultural systems. In the dynamics of the transmission of such diseases, movements of livestock between herds play an important role. When constructing mathematical models used for activities such as forecasting epidemic development, evaluating mitigation strategies, or determining important targets for disease surveillance, including between -premises shipments is often a necessity. In the United States (U.S.), livestock shipment data is not routinely collected, and when it is, it is not readily available and mostly concerned with between-state shipments. To bridge this gap in knowledge and provide insight into the complete livestock shipment network structure, we have developed the U.S. Animal Movement Model (USAMM). Previously, USAMM has only existed for cattle shipments, but here we present a version for domestic swine. This new version of USAMM consists of a Bayesian model fit to premises demography, county-level livestock industry variables, and two limited data sets of between-state swine movements. The model scales up the data to simulate nation-wide networks of both within-and between-state shipments at the level of individual premises. Here we describe this shipment model in detail and subsequently explore its usefulness with a rudimentary predictive model of the prevalence of porcine epidemic diarrhea virus (PEDv) across the U.S. Additionally, in order to promote further research on livestock disease and other topics involving the movements of swine in the U.S., we also make 250 synthetic premises-level swine shipment networks with complete coverage of the entire conterminous U.S. freely available to the research community as a useful surrogate for the absent shipment data.
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| 4. |
- Sellman, Stefan, et al.
(author)
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Modeling US cattle movements until the cows come home: Who ships to whom and how many?
- 2022
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In: Computers and Electronics in Agriculture. - : ELSEVIER SCI LTD. - 0168-1699 .- 1872-7107. ; 203
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Journal article (peer-reviewed)abstract
- Livestock movements between agricultural premises is an important pathway for the spread of infectious disease. Data providing details about the origin and destination of shipments, as well as information about the shipment size is an important component of computer models used to formulate mitigation strategies and design surveillance programs. The United States (U.S.) currently lacks a comprehensive database of farm animal shipments, which hinders such efforts. With the U.S. Animal Movement Model (USAMM), earlier work has successfully scaled up from limited data based on interstate certificates of veterinary inspection (CVI) to comprehensive county-level shipment networks at the national scale. In this work, we present three major improvements to earlier versions of USAMM: (1) increased resolution of the model and simulated networks to the level of individual premises; (2) predictions of shipment sizes; (3) taking into account the types and herd sizes of the premises. We fitted parameters in a Bayesian framework to two sets of CVI data consisting of sub-samples of one years between-state beef and dairy shipments. Through posterior predictive simulation, we then created 1,000 synthetic beef and dairy networks, which we make publicly available to support livestock disease modeling. The simulated networks were validated against summary statistics of the training data as well as out-of-sample CVI data from subsequent years. This new development opens up the possibility of using USAMM in a broader spectrum of applications where information about shipment size and premises identity is necessary and gives novel insights into the U.S. cattle shipment network.
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| 5. |
- Sellman, Stefan, et al.
(author)
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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
- 2020
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In: PloS Computational Biology. - : PUBLIC LIBRARY SCIENCE. - 1553-734X .- 1553-7358. ; 16:2
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Journal article (peer-reviewed)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 (R-0) 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 Ripleys 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, R-0 was typically higher in FLAPS configurations, and the simulation study corroborated the pattern for later stages of outbreaks. Further, both R-0 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.
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