Use of deep learning to predict chronic wasting disease status based on animal movement

Chronic Wasting Disease (CWD) is an invariably fatal prion disease that impacts cervid populations and wildlife management across North America. Infected cervids often remain asymptomatic for months and movement-based anomaly detection from Global Positioning System collaring data offers a potential tool for understanding early and late stage CWD-based behavioral changes. Here we evaluate whether deep learning behavioral anomaly detection models such as autoencoders (AE) and conditional autoencoders (cAE) can effectively identify anomalous movement changes in free-ranging mule deer (Odocoileus hemionus) that may have been associated with CWD infection. Unsupervised AEs achieved ≥ 84% accuracy and 89% precision irrespective of whether the model was trained using only CWD^- or a 60/40 split of CWD⁺ / CWD^- individuals. Important movement features in distinguishing between CWD⁺ and CWD^- animals included metrics related to velocity, direction, and sinuosity. In contrast, supervised cAEs only achieved 54-77% accuracy and ≤ 53% precision across models trained with incidence rates of CWD⁺ individuals ranging from 10 to 40%; the models also had inconsistent results reducing their generality. Finally, using the best fitting model (AE trained using only CWD^- animals), we found that early-stage animals exhibited reduced space use, whereas late-stage individuals presented more pronounced declines in velocity and altered directional patterns. These findings indicate that AEs can accurately identify CWD-related behavioral anomalies using movement data and that variation in what movement metrics matter depends on the stage of the disease.