Classifying CWD-Infected Elk Using Recurrent Neural Networks on GPS Movement Data
Description
Chronic Wasting Disease is difficult and costly to diagnose using traditional biological testing. However, the disease affects an elk's behavior and movement over time. By analyzing GPS tracking data with AI — specifically, Recurrent Neural Networks (RNNs) — the model aims to: Automatically identify movement patterns indicative of CWD infection, Enable earlier or non-invasive detection of disease, and Support wildlife disease surveillance and management decisions
Detailed example
The AI system outputs a classification label for each elk—CWD-infected or not infected—based on patterns in their GPS movement data.
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.
Expected benefit
helps wildlife managers detect CWD in elk early using GPS movement data, reducing the need for costly and invasive testing. It supports faster, data-driven decisions for disease control and wildlife management, protecting both animal health and resources
Controls / human review
ATO: No; PIA: Not published
Data needed
The model is trained and evaluated using GPS movement data from collared elk, labeled with confirmed CWD infection status through biological testing.