Browsing by Author "Department of Veterinary Population Medicine, College of Veterinary Medicine, University of Minnesota"
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Item Comparison of Spatiotemporal Patterns of Historic Natural Anthrax Outbreaks in Minnesota and Kazakhstan (Supplementary data)(2018-12-06) Kanankege, Kaushi; Abdrakhmanov, Sarsenbay; Glaser, Linda; Bender, Jeffery; Korennoy, Fedor; Perez, Andres; kanan009@umn.edu; Kanankege, Kaushi; Department of Veterinary Population Medicine, College of Veterinary Medicine, University of Minnesota; S. Seifullin Kazakh Agrotechnical University, Astana, Kazakhstan; Minnesota Board of Animal Health, St Paul, MinnesotaWe compared the spatiotemporal patterns of historic animal Anthrax records in Minnesota and Kazakhstan. In Minnesota, 289 animal Anthrax cases reported between 1912 and 2014 to the Minnesota Board of Animal Health were used in the analysis. For events occurred between 1920 and 1999 the geographical coordinates were obtained using historic aerial images whereas, for those cases that occurred after 2000, coordinates were recorded during site visits. For the Republic of Kazakhstan, laboratory confirmed Anthrax cases reported by the Cadastral register of stationary unfavorable foci on Anthrax between 1933 and 2014 (n=3,997) were analyzed. Because of the sensitivity of providing the actual geographical locations/coordinates, these data on reported Anthrax cases were summarized by administrative unit, by year. The administrative units were Minnesota counties and districts of Kazakhstan. This repository contains two separate EXCEL sheets summarizing the data accordingly.Item Monitoring the spatiotemporal patterns of wildlife health using rehabilitation databases(2022-04-07) Kanankege, Kaushi; Willette, Michelle; Jenni, Phil; Ponder, Julia; Schott, Renee; Bueno, Irene; Muellner, Ulrich; Muellner, Petra; VanderWaal, Kimberly; kanan009@umn.edu; Kanankege, Kaushi; Department of Veterinary Population Medicine, College of Veterinary Medicine, University of MinnesotaWildlife health surveillance is challenging. An alternative is to use wildlife rehabilitation data as potential sentinels, where anomalies in the pattern of submissions may indicate an underlying event that deviates from the baseline and warrants further investigation. Such anomalies may affect multiple species, leading submissions to be clustered in a certain area or time period. To determine spatiotemporal submission patterns and the feasibility of identifying anomalies potentially associated with underlying events, we aggregated databases from two major wildlife rehabilitation centers in Minnesota, drawing on 66,472 submissions from the 2015 – 2019 period, and pertaining to 29 ”species groups” and 12 “circumstances” associated with submission. The infants and juveniles of wildlife, including fledglings, hatchlings, and after-hatch year birds (raptor-specific), submitted as a group from the same location on the same day were grouped and considered as one submission. Hence, the number of records included in the spatiotemporal cluster analysis was 49,352. The multivariate multinomial space-time model of the scan statistic was used to identify statistically significant spatiotemporal clusters of either wildlife species groups or circumstances, simultaneously (Cluster: an area capturing 10% of the submissions aggregated within a maximum radius of 30km during a maximum temporal window of 3-months). This repository contains the data arranged to be used for the spatial cluster analysis.Item Network connectivity patterns of Minnesota waterbodies and implications for aquatic invasive species prevention(2020-10-28) Kao, Szu-Yu; Enns, Eva A; Tomamichel, Megan; Doll, Adam; Escobar, Luis E; Qiao, Huijie; Craft, Meggan E; Phelps, Nicholas B D; phelp083@umn.edu; Phelps, Nicholas B D; Minnesota Aquatic Invasive Species Research Center, University of Minnesota; Division of Health Policy and Management, School of Public Health, University of Minnesota; Odum School of Ecology, University of Georgia; Department of Fish and Wildlife Conservation, Virginia Polytechnic Institute and State University; Key Laboratory of Animal Ecology and Conservation Biology, Chinese Academy of Sciences; Department of Veterinary Population Medicine, College of Veterinary Medicine, University of MinnesotaThe data contains simulated boater movements across lakes in the state of Minnesota (MN). The data were simulated based on the boater inspection program conducted by the Minnesota Department of Natural Resources in 2014-2017. Using the inspection survey, we employed machine learning technique, XGBoost, to construct three predictive boater movement models. First, we predicted the number of boater traffic on a lake for a year. Second, we predicted the boater connection between any pair of lakes in MN. Third, we predicted the number of boaters between two lakes that were predicted to have connection.