Predicting chronic wasting disease in white-tailed deer at the county scale using machine learning.

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 validati

SUBMITTER: Ahmed MS 

PROVIDER: S-EPMC11193737 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature