CWD Sentinel

In development

The CWD Sentinel uses all SOP4CWD data in conjunction with deep learning and INLA to predict which sub-administrative areas may (1) turn CWD-positive for the first time, or (2) experience rapid spread of existing infection.

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 and spread for each sub-administrative area in your administrative area


The CWD Sentinel is the most powerful machine learning tool to date to predict CWD emergence and spread

More Information

For more information, go to the CWD Data Warehouse User Manual: CWD Sentinel.

Code

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

Citation

• Gonzalez-Crespo C, Schuler K, Hanley B, Hollingshead N, Middaugh C, Ballard J, Clemons B, Kelly J, Harms T, Caudell J, Benavidez Westrich K, McCallen E, Casey C, O'Brien L, Trudeau J, Stewart C, Carstensen M, Jennelle C, McKinley W, Hynes P, Stevens A, Miller L, Grove D, Storm D, Martinez-Lopez B. Fusing Bayesian inference and deep learning: A hybrid AI approach for predicting chronic wasting disease emergence and spread. In revision