Predicting disease spread from host movement data: Chronic wasting disease in North America as a case study

Area of Science:

• Wildlife ecology
• Disease ecology
• Movement ecology

Background:

• Long-distance animal movements can facilitate invasive species spread and range shifts.
• Seasonal migrations and juvenile dispersal are critical factors in wildlife disease transmission.
• High-resolution animal movement data offer potential for improved disease spread predictions, but their application is not fully understood.

Purpose of the Study:

• To predict the speed of chronic wasting disease (CWD) spread using mule deer and white-tailed deer movement metrics.
• To compare model predictions with observed CWD spreading rates across North America (2005-2022).
• To investigate the relationship between deer mobility, migration patterns, and CWD transmission dynamics.

Main Methods:

• Utilized high-resolution movement data for mule deer (Odocoileus hemionus) and white-tailed deer (Odocoileus virginianus).
• Developed three predictive models for CWD spread incorporating dispersal and migration metrics.
• Compared predicted CWD spread rates with observed data from North American epicenters.

Main Results:

• Observed CWD spread was faster in western US regions with mobile mule deer (14-18 km/year) compared to areas with only white-tailed deer (5-7 km/year).
• CWD spread speed did not consistently correlate with migration distances; faster spread was observed in Canadian Prairies despite shorter deer movements.
• Predicted CWD spread rates from all models were consistently slower than observed rates, highlighting a gap in understanding local spread dynamics.

Conclusions:

• The hypothesis that CWD spreads faster with long-distance migratory mule deer was only partially supported.
• Discrepancies between predicted and observed CWD spread underscore the need for refined analytical methods and metrics for movement data.
• Anthropogenic movements may significantly contribute to long-distance CWD spread, while factors driving faster-than-predicted local spread require further investigation.