Tiffany M Wolf
Persistent link for this collectionhttps://hdl.handle.net/11299/213235
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listelement.badge.dso-type Item , Supporting Dataset for "RT-QuIC Optimization for Prion Detection in Two Minnesota Soil Types" and "Detection of Chronic Wasting Disease Prions in Soil at an Illegal White-tailed Deer Carcass Disposal Site"(2025-02-24) Grunklee, Madeline K; Bartz, Jason C; Karwan, Diana L; Lichtenberg, Stuart S; Lurndahl, Nicole A; Larsen, Peter A; Schwabenlander, Marc D; Rowden, Gage R; Li, E Anu; Yuan, Qi; Wolf, Tiffany M; wolfx305@umn.edu; Wolf, Tiffany M; Minnesota Center for Prion Research and Outreach (MNPRO)These data describe prion detections in soil using real-time quaking-induced conversion (RT-QuIC) assay with various metric calculations common to RT-QuIC. The Soil_Cntrl_Expmts_Data.xlsx file contains data from a series of control experiments aimed at optimizing and applying RT-QuIC for the detection of chronic wasting disease prions in environmental soil samples. We focused negative control experiments on refining RT-QuIC and sample processing to use on Minnesota native soils, which included limiting background noise from the samples. Starting on 2023-05-08, we used spiked soil control experiments to distinguish true prion signal from background noise and validate detection reliability. Following soil control experiments, the Soil_Test_Samples_Data.xlsx file describes our sample testing in RT-QuIC collected from our study site, an illegal white-tailed deer (Odocoileus virginianus, WTD) carcass disposal site and a nearby captive WTD farm in Beltrami County, Minnesota. We analyzed study site soil samples for prion presence to assess potential environmental contamination associated with improper carcass disposal practices.listelement.badge.dso-type Item , Data Sharing and Ownership Agreement for the Research Project “Building a One Health Research Collaboration between UMN and Grand Portage Indian Reservation” between the Grand Portage Band and the Regents of the University of Minnesota, on behalf of its College of Veterinary Medicine, Department of Veterinary Population Medicine(2015) Moore, Seth A; Wolf, Tiffany M; Travis, Dominic Alistelement.badge.dso-type Item , The zoonotic risk of echinococcosis transmission warrants renewed attention in northern Minnesota.(2018) Sokolik, Sara J; Moore, Seth; Boufana, Belgees; Travis, Dominic; Wolf, Tiffany Mlistelement.badge.dso-type Item , Data, Model Documentation, and Output Supporting "Optimizing syndromic health surveillance in free ranging great apes: the case of Gombe National Park"(2018-05-24) Wolf, Tiffany, M; Wang, Wenchun, A; Lonsdorf, Elizabeth V; Gillespie, Thomas; Pusey, Anne; Gilby, Ian; Travis, Dominic A; Singer, Randall; wolfx305@umn.edu; Wolf, Tiffany MSyndromic surveillance is an incipient approach to early wildlife disease detection. Consequently, systematic assessments are needed for methodology validation in wildlife populations. We evaluated the sensitivity of a syndromic surveillance protocol for respiratory disease detection among chimpanzees in Gombe National Park, Tanzania. Empirical health, behavioral and demographic data were integrated with an agent-based, network model to simulate disease transmission and surveillance. Surveillance sensitivity was estimated as 66% (95% Confidence Interval: 63.1, 68.8%) and 59.5% (95% Confidence Interval: 56.5%, 62.4%) for two monitoring methods (weekly count and prevalence thresholds, respectively), but differences among calendar quarters in outbreak size and surveillance sensitivity suggest seasonal effects. We determined that a threshold weekly detection of ≥2 chimpanzees with clinical respiratory disease leading to outbreak response protocols (enhanced observation and biological sampling) is an optimal algorithm for outbreak detection in this population. Synthesis and applications: This is the first quantitative assessment of syndromic surveillance in wildlife, providing a model approach addressing disease emergence. Coupling syndromic surveillance with targeted diagnostic sampling in the midst of suspected outbreaks will provide a powerful system for detecting disease transmission and understanding population impacts.