Optimizing infectious disease mitigation under dynamic conditions

Area of Science:

• Epidemiology
• Mathematical Biology
• Public Health Policy

Background:

• Mitigation measures are crucial for controlling infectious disease outbreaks but incur significant societal and economic costs.
• Balancing disease spread and mitigation expenses is a key challenge in public health.

Purpose of the Study:

• To develop a general framework combining disease dynamics simulation and optimal control theory.
• To identify optimal mitigation strategies that minimize the combined costs of infection and intervention.

Main Methods:

• Developed a general framework integrating disease spread simulations with optimal control algorithms.
• Analyzed the trade-off between infection severity and mitigation costs under various scenarios.
• Characterized optimal strategies analytically for different disease and intervention parameters.

Main Results:

• Optimal mitigation is typically 'all-or-nothing' based on disease severity, with intermediate levels only in specific cases.
• Seasonal variations necessitate stricter winter mitigation, potentially shifting infection waves to spring.
• Even optimal mitigation can lead to transient infection waves during vaccination campaigns.
• Delayed mitigation onset significantly increases total costs, especially for severe diseases.

Conclusions:

• The developed framework offers a versatile tool for exploring optimal mitigation strategies in diverse infectious disease settings.
• Findings provide insights into the complex dynamics of disease control, informing public health policy.
• Understanding cost-benefit trade-offs is essential for effective pandemic and epidemic management.