A general framework for optimising cost-effectiveness of pandemic response under partial intervention measures

Area of Science:

• Public Health
• Epidemiology
• Health Economics

Background:

• The COVID-19 pandemic highlighted the need for effective public health strategies balancing health outcomes with socioeconomic impacts.
• Non-pharmaceutical interventions (NPIs) and vaccination campaigns presented trade-offs between health benefits and societal costs.
• Existing frameworks often struggle to adapt to emerging pandemic threats and evolving population behaviors.

Purpose of the Study:

• To develop a general framework for deriving adaptive, cost-effective interventions for current and future pandemics.
• To quantify the net health benefits of optimized adaptive NPIs.
• To propose a reinforcement learning approach for optimizing adaptive NPIs.

Main Methods:

• Utilized an agent-based model to simulate pandemic responses in Australia.
• Incorporated a heterogeneous population with variable and fluctuating compliance levels.
• Employed a reinforcement learning approach to optimize adaptive NPI strategies.

Main Results:

• Adaptive NPIs, particularly partial social distancing, yield significant net health benefits.
• Moderate societal willingness to pay for health gains is crucial for achieving these benefits.
• The analysis demonstrated that a sustainable balance between health and economic costs is attainable.

Conclusions:

• Adaptive NPIs represent a viable strategy for managing pandemic threats effectively.
• Optimized adaptive NPIs can achieve a socially acceptable balance between public health and economic considerations.
• The proposed framework offers a pathway for resilient pandemic preparedness and response.