Sellman, StefanBeck-Johnson, LindsayHallman, ClaytonMiller, Ryan S.Owers Bonner, Katharine A.Portacci, KatieWebb, Colleen T.Lindström, Tom2022-05-232022-05-232022https://hdl.handle.net/10217/235130http://dx.doi.org/10.25675/10217/235130Download county FIPS code tables at https://www.census.gov/geo/reference/codes/cou.html.Authors are from Department of Physics, Chemistry and Biology, Linköping University, Linköping, Sweden, Department of Biology, Colorado State University, Fort Collins.Each of the 250 files in the zipped folder represents a single simulated swine shipment network among the counties of the contiguous United States, generated from the U.S. Animal Movement Model (USAMM) version 3. Each file is a tab-delimited .txt file with 15 columns: oCountyId - Origin county FIPS code. dCountyId - Destination county FIPS code. dayOfYear - Day 1 = 1/1, day 365 = 12/31. volume - Number of animals shipped. commodity - swine (s). period - Quarter of the year. oStateAbbr - Origin state abbreviation. dStateAbbr - Destination state abbreviation. producer – If ‘1’, indicates that this shipment is associated with a producer with a swine production health plan, otherwise it is ‘none’. oPremId - Origin premises id. Matches ids from the premises data (FLAPS) file. dPremId - Destination premises id. Matches ids from the premises data (FLAPS) file. oPty - Origin premises type (Frm, farm). oBinnedSize - Binned herd size of origin premises. dPty - Destination premises type (Frm, farm). dBinnedSize - Binned herd size of destination premises. The premises demography data (FLAPS file) consists of the following columns: Id - Premises id matching id columns in network files. County- County FIPS code. X/Y - Projected coordinates. Lat/Lon - Latitude and longitude. type - s (swine farm) s - Number of swine.Department of BiologyThe 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.ZIPTXTenganimal movementnetwork predictionswineDataset