Bayesian optimisation of restriction zones for bluetongue control

Area of Science:

• Veterinary Epidemiology
• Mathematical Biology
• Climate Change Impacts

Background:

• Bluetongue (BT) is an infectious livestock disease with increasing prevalence linked to climate change.
• Effective control strategies for vector-borne diseases are crucial due to rising global temperatures.
• Current UK government strategies for bluetongue control are in place.

Purpose of the Study:

• To investigate animal movement restrictions as a method for controlling bluetongue spread in the UK.
• To develop and evaluate optimized control policies using Bayesian optimization and simulation.
• To compare a generalized national strategy with region-specific policies.

Main Methods:

• Utilized a simulation-based model of bluetongue transmission.
• Employed Bayesian optimization to derive control policies minimizing infected farms.
• Assessed a generalized national strategy and region-specific policies for seven UK regions.

Main Results:

• A generalized national strategy for restricting animal movements proved as effective as current UK government strategies.
• Proposed national controls are simpler, affect fewer farms, and minimize economic implications.
• Region-specific policies demonstrated improved efficiency in bluetongue outbreak control.

Conclusions:

• Bayesian optimization is valuable for developing cost-effective disease management strategies.
• Optimized animal movement restrictions offer an efficient alternative for bluetongue control.
• Climate change necessitates advanced, adaptable strategies for vector-borne disease management.