Models for managing wildlife disease.

Area of Science:

• Ecology
• Epidemiology
• Mathematical Biology

Background:

• Wildlife disease systems present unique challenges due to limited data compared to human or livestock systems.
• Assessing disease impact on population size is a primary concern in wildlife health.
• Dynamic models are increasingly applied to understand and manage wildlife diseases.

Purpose of the Study:

• To review the application of dynamic models in evaluating and guiding wildlife disease management strategies.
• To highlight the utility of models in assessing vaccination and culling interventions.
• To identify key research gaps in wildlife disease modelling.

Main Methods:

• This review syntheses existing literature on the application of dynamic models in wildlife disease management.
• Focus is placed on models used for vaccination strategies, culling strategies, and biological control.
• Analysis includes identifying areas where model development is lacking.

Main Results:

• Dynamic models are effective tools for evaluating vaccination strategies to protect endangered species and prevent disease spillover.
• Models are extensively used to assess the efficacy of culling strategies for conservation and spillover prevention.
• Models also play a role in evaluating parasites and pathogens as biological control agents.

Conclusions:

• Dynamic models are crucial for informing management decisions in wildlife disease ecology.
• Key research gaps include developing models for macroparasites, determining optimal model complexity, incorporating genetic management, and enhancing model-data connections.
• Further advancements in modelling are essential for effective wildlife disease control and conservation efforts.