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Browsing Research Data by Subject "animal movement"

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    ItemOpen Access
    Data associated with "Modeling U.S. cattle movements until the cows come home: who ships to whom and how many?"
    (Colorado State University. Libraries, 2021) Sellman, Stefan; Beck-Johnson, Lindsay; Hallman, Clayton; Miller, Ryan S.; Owers Bonner, Katharine A.; Portacci, Katie; Webb, Colleen T.; Lindström, Tom
    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 year's 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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    ItemOpen Access
    Modeling Nation-Wide U.S. Swine Movement Networks at the Resolution of the Individual Premises
    (Colorado State University. Libraries, 2022) Sellman, Stefan; Beck-Johnson, Lindsay; Hallman, Clayton; Miller, Ryan S.; Owers Bonner, Katharine A.; Portacci, Katie; Webb, Colleen T.; Lindström, Tom
    The spread of transboundary animal diseases (TAD) is a major cause for concern to the worlds agricultural systems. In the dynamics of the spread of TADs between agricultural premises, the movement of livestock between herds plays an important role. Therefore, when constructing mathematical models used for activities such as forecasting epidemic development, evaluating mitigation strategies, or determining important targets for disease surveillance, incorporating a model component describing between-premises shipments is often a necessity. In the cases when up-to-date and comprehensive shipment data is available, this is a relatively simple task; when data is nonexistent or patchy, researchers need to model the shipments in addition to the disease dynamics, a task that can be complex and time consuming. In the United States (U.S.), livestock shipment data is not generally collected, and when it is, it is not easily available and mostly concerned with between-state shipments. To cover this gap in knowledge and provide insight into the complete shipment networks of livestock animals, the U.S. Animal Movement Model (USAMM) was developed. Previously, USAMM has only modeled cattle shipments, but here we present a version for the U.S. swine shipment network. Like previous versions, USAMM for swine is a Bayesian model fit to premises demography data, and county-level livestock industry variables and the available data of between-state swine movements. The model is then used to simulate, nation-wide networks of both within- and between-state shipments at the level of individual premises for the U.S. swine industry. Here we describe the model in detail and demonstrate its usefulness in 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 TADs and other topics involving the movements of swine in the U.S., we also make a set of 250 simulated swine shipment networks freely available to the research community as a useful surrogate for the missing data.
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    ItemOpen Access
    Simulated cattle shipment networks from the U.S. animal movement model
    (Colorado State University. Libraries, 2019) Lindstrom, Tom; Grear, Daniel A.; Buhnerkempe, Michael; Webb, Colleen T.; Miller, Ryan S.; Portacci, Katie; Wennergren, Uno
    Networks are rarely completely observed and prediction of unobserved edges is an important problem, especially in disease spread modeling where networks are used to represent the pattern of contacts. We focus on a partially observed cattle movement network in the U.S. and present a method for scaling up to a full network based on Bayesian inference, with the aim of informing epidemic disease spread models in the United States. The observed network is a 10% state stratified sample of Interstate Certificates of Veterinary Inspection that are required for interstate movement; describing approximately 20,000 movements from 47 of the contiguous states, with origins and destinations aggregated at the county level. We address how to scale up the 10% sample and predict unobserved intrastate movements based on observed movement distances. Edge prediction based on a distance kernel is not straightforward because the probability of movement does not always decline monotonically with distance due to underlying industry infrastructure. Hence, we propose a spatially explicit model where the probability of movement depends on distance, number of premises per county and historical imports of animals. Our model performs well in recapturing overall metrics of the observed network at the node level (U.S. counties), including degree centrality and betweenness; and performs better compared to randomized networks. Kernel generated movement networks also recapture observed global network metrics, including network size, transitivity, reciprocity, and assortativity better than randomized networks. In addition, predicted movements are similar to observed when aggregated at the state level (a broader geographic level relevant for policy) and are concentrated around states where key infrastructures, such as feedlots, are common. We conclude that the method generally performs well in predicting both coarse geographical patterns and network structure and is a promising method to generate full networks that incorporate the uncertainty of sampled and unobserved contacts.