Lindstrom, TomGrear, Daniel A.Buhnerkempe, MichaelWebb, Colleen T.Miller, Ryan S.Portacci, KatieWennergren, Uno2019-02-192019-02-192019https://hdl.handle.net/10217/194169https://dx.doi.org/10.25675/10217/194169Download 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, Colorado, United States of America, and United States Department of Agriculture, Animal and Plant Health Inspection Service, Center for Epidemiology and Animal Health, Fort Collins, Colorado, United States of America.Each file represents a single simulated cattle shipment network among the counties of the contiguous United States, generated from the U.S. Animal Movement Model (USAMM). Each file within each zipped folder is a space-delimited .txt file with three columns: shipment origin county (FIPS code), shipment destination county (FIPS code), and number of shipments. Files in USAMM-BEEF_Generated_Networks.zip describe shipment networks for beef production only. Files in USAMM-DAIRY_Generated_networks.zip describe shipment networks for dairy production only. Files in USAMM-ALL_Generated_Networks.zip describe shipment networks for both beef and dairy. Each folder contains 1000 files, representing 1000 shipment networks.Department of BiologyNetworks 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.TXTZIPenganimal movementnetwork predictionDatasetFor data license information, see USAMM license file (LICENSE.txt)