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Positive Predictor Model

In development

The Positive Predictor Model uses machine learning in conjunction with all available continent-wide CWD data to predict which sub-administrative areas may turn CWD-positive in upcoming years.

Geographical Scale

  • Administrative area, subdivided into a sub-administrative areas

Required Data

  • Sample data
  • Cervid facility data

Suggested Data

  • Demography data
  • Taxidermist data
  • Meat processor data

User Inputs

  • Season-year

Outputs

  • A map containing the predictions of CWD emergence for each sub-administrative area in your administrative area

More Information

For more information, go to the CWD Data Warehouse User Manual: Positive Predictor Model.

Code

To view the code once deployed, go to the GitHub Repository: Positive Predictor Model.

Citation

  • 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 J, Stewart C, Carstensen M, McKinley W, Hynes KP, Stevens AE, Miller LA, Cook M, Myers RT, Shaw J, Tonkovich M, Kelly JD, Grove DM, Storm DJ, & Schuler KL. 2024. Predicting chronic wasting disease in white-tailed deer at the county scale using machine learning. Scientific Reports. 14, 14373.