Size matters: Sample size assessments for chronic wasting disease surveillance using an agent-based modeling...

Area of Science:

• Wildlife epidemiology
• Ecological modeling
• Disease surveillance

Background:

• Epidemiological surveillance for wildlife diseases often relies on hunter-harvested animal samples.
• Current statistical methods assume random sampling, which may not reflect real-world biases in hunter-harvested data.
• Heterogeneities in disease distribution and harvest processes can lead to underestimated sample sizes and overestimated detection probabilities.

Purpose of the Study:

• To present a novel modeling framework for designing efficient wildlife disease surveillance programs.
• To address the limitations of traditional methods in accounting for sampling biases and disease heterogeneities.
• To enable accurate sample size calculations for prompt and reliable detection of wildlife diseases.

Main Methods:

• Development of an agent-based modeling framework (MOvPOP and MOvPOPsurveillance) incorporating host characteristics, disease dynamics, and sampling biases.
• Simulation of real-world heterogeneities in disease distribution and hunter harvest processes.
• Iterative analysis of locally relevant surveillance scenarios to determine optimal sample sizes.

Main Results:

• The agent-based models successfully integrate complex factors influencing surveillance data.
• The framework allows for population-specific sample size determination crucial for diseases like chronic wasting disease and bovine tuberculosis.
• Demonstrated ability to assess reproducibility and facilitate adaptation of the model to different regions and disease systems.

Conclusions:

• The developed modeling framework provides a robust tool for enhancing wildlife disease surveillance strategies.
• Accurate sample size calculations are essential for reliable disease detection and inference.
• This approach supports informed decision-making for effective wildlife health management.