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Ahmed MS, Hanley BJ, Mitchell CI, Abbott RC, Hollingshead NA, Booth JG, Guinness J, Jennelle CS, Hodel FH, Gonzalez-Crespo C, Middaugh CR, Ballard JR, Clemons B, Killmaster CH, Harms TM, Caudell JN, Benavidez Westrich KM, McCallen E, Casey C, O'Brien LM, Trudeau JK, Stewart C, Carstensen M, McKinley WT, Hynes KP, Stevens AE, Miller LA, Cook M, Myers RT, Shaw J, Tonkovich MJ, Kelly JD, Grove DM, Storm DJ, Schuler KL. Predicting chronic wasting disease in white-tailed deer at the county scale using machine learning. Sci Rep 2024; 14:14373. [PMID: 38909151 PMCID: PMC11193737 DOI: 10.1038/s41598-024-65002-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 06/15/2024] [Indexed: 06/24/2024] Open
Abstract
Continued spread of chronic wasting disease (CWD) through wild cervid herds negatively impacts populations, erodes wildlife conservation, drains resource dollars, and challenges wildlife management agencies. Risk factors for CWD have been investigated at state scales, but a regional model to predict locations of new infections can guide increasingly efficient surveillance efforts. We predicted CWD incidence by county using CWD surveillance data depicting white-tailed deer (Odocoileus virginianus) in 16 eastern and midwestern US states. We predicted the binary outcome of CWD-status using four machine learning models, utilized five-fold cross-validation and grid search to pinpoint the best model, then compared model predictions against the subsequent year of surveillance data. Cross validation revealed that the Light Boosting Gradient model was the most reliable predictor given the regional data. The predictive model could be helpful for surveillance planning. Predictions of false positives emphasize areas that warrant targeted CWD surveillance because of similar conditions with counties known to harbor CWD. However, disagreements in positives and negatives between the CWD Prediction Web App predictions and the on-the-ground surveillance data one year later underscore the need for state wildlife agency professionals to use a layered modeling approach to ensure robust surveillance planning. The CWD Prediction Web App is at https://cwd-predict.streamlit.app/ .
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Affiliation(s)
- Md Sohel Ahmed
- Wildlife Health Lab, Cornell University, Ithaca, NY, USA.
- Texas A & M Transportation Institute, Austin, TX, USA.
| | | | - Corey I Mitchell
- Desert Centered Ecology, LLC, Tucson, AZ, USA
- U.S. Fish and Wildlife Service, Tucson, AZ, USA
| | | | | | - James G Booth
- Department of Statistics and Data Science, Cornell University, Ithaca, NY, USA
| | - Joe Guinness
- Department of Statistics and Data Science, Cornell University, Ithaca, NY, USA
| | - Christopher S Jennelle
- Minnesota Department of Natural Resources, Nongame Wildlife Program, Saint Paul, MN, USA
| | - Florian H Hodel
- Department of Fisheries and Wildlife, Michigan State University, East Lansing, MI, USA
| | - Carlos Gonzalez-Crespo
- Center for Animal Disease Modelling and Surveillance, University of California, Davis, CA, USA
| | | | | | - Bambi Clemons
- Florida Fish and Wildlife Conservation Commission, Gainesville, FL, USA
| | | | | | - Joe N Caudell
- Indiana Department of Natural Resources, Bloomington, IN, USA
| | | | - Emily McCallen
- Indiana Department of Natural Resources, Bloomington, IN, USA
| | - Christine Casey
- Kentucky Department of Fish and Wildlife Resources, Frankfort, KY, USA
| | | | | | - Chad Stewart
- Michigan Department of Natural Resources, Grand Rapids, MI, USA
| | - Michelle Carstensen
- Minnesota Department of Natural Resources, Wildlife Health Program, Forest Lake, MN, USA
| | - William T McKinley
- Mississippi Department of Wildlife, Fisheries, and Parks, Jackson, MS, USA
| | - Kevin P Hynes
- New York State Department of Environmental Conservation, Delmar, NY, USA
| | - Ashley E Stevens
- New York State Department of Environmental Conservation, Delmar, NY, USA
| | - Landon A Miller
- New York State Department of Environmental Conservation, Delmar, NY, USA
| | - Merril Cook
- North Carolina Wildlife Resources Commission, Raleigh, NC, USA
| | - Ryan T Myers
- North Carolina Wildlife Resources Commission, Raleigh, NC, USA
| | - Jonathan Shaw
- North Carolina Wildlife Resources Commission, Raleigh, NC, USA
| | | | - James D Kelly
- Florida Fish and Wildlife Conservation Commission, Gainesville, FL, USA
| | | | - Daniel J Storm
- Wisconsin Department of Natural Resources, Madison, WI, USA
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Escobar LE, Morand S. Editorial: Disease Ecology and Biogeography. Front Vet Sci 2021; 8:765825. [PMID: 34778439 PMCID: PMC8586068 DOI: 10.3389/fvets.2021.765825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Accepted: 09/30/2021] [Indexed: 11/23/2022] Open
Affiliation(s)
- Luis E Escobar
- Department of Fish and Wildlife Conservation, Virginia Tech, Blacksburg, VA, United States.,Global Change Center, Virginia Tech, Blacksburg, VA, United States.,Center for Emerging Zoonotic and Arthropod-Borne Pathogens, Virginia Tech, Blacksburg, VA, United States.,Doctorado en Agrociencias, Facultad de Ciencias Agropecuarias, Universidad de La Salle, Bogotá, Colombia
| | - Serge Morand
- CNRS ISEM-CIRAD ASTRE, Montpellier University, Montpellier, France.,Faculty of Veterinary Technology, Kasetsart University, Bangkok, Thailand.,Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
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