Predicting the odds of chronic wasting disease with Habitat Risk software.

Area of Science:

• Wildlife epidemiology
• Disease ecology
• Spatial risk assessment

Background:

• Chronic Wasting Disease (CWD), a transmissible spongiform encephalopathy, has spread significantly in wild cervid populations across North America and internationally.
• Existing epidemiological models require extension into broader, semi-automated tools for effective use by wildlife management agencies.
• There is a critical need for accessible, data-driven methods to predict CWD spread and inform management strategies.

Purpose of the Study:

• To introduce Habitat Risk software, linking epidemiological models with environmental and disease surveillance data.
• To enable wildlife agencies to generate localized, up-to-date predictions of CWD detection probability.
• To facilitate broader access to advanced scientific methods for CWD risk assessment.

Main Methods:

• Integration of a published epidemiological model with spatially referenced environmental data.
• Standardization of disease testing (surveillance) data for wild cervid populations.
• Development of an autonomous computational workflow culminating in a user interface with an interactive risk map.

Main Results:

• The Habitat Risk software successfully links environmental and surveillance data to predict CWD risk.
• Demonstrated application with white-tailed deer surveillance data from Tennessee, USA, showing localized risk predictions.
• The software provides an interactive map visualizing disease risk based on integrated data.

Conclusions:

• Habitat Risk software offers a valuable tool for wildlife agencies to enhance CWD surveillance and management.
• The semi-automated approach democratizes access to complex epidemiological modeling for practical application.
• This approach supports data-driven decision-making to mitigate the spread of Chronic Wasting Disease in wild populations.