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

TL;DR

This paper presents a reinforcement learning framework using an agent-based model to optimize adaptive, cost-effective pandemic interventions balancing health benefits and economic costs.

Contribution

It introduces a novel adaptive framework for pandemic response that incorporates reinforcement learning and agent-based modeling to optimize interventions.

Findings

Adaptive NPIs can significantly improve net health benefits.

Partial social distancing measures are effective when combined with moderate willingness to pay.

Long-term balance between health and economic costs is achievable.

Abstract

The COVID-19 pandemic created enormous public health and socioeconomic challenges. The health effects of vaccination and non-pharmaceutical interventions (NPIs) were often contrasted with significant social and economic costs. We describe a general framework aimed to derive adaptive cost-effective interventions, adequate for both recent and emerging pandemic threats. We also quantify the net health benefits and propose a reinforcement learning approach to optimise adaptive NPIs. The approach utilises an agent-based model simulating pandemic responses in Australia, and accounts for a heterogeneous population with variable levels of compliance fluctuating over time and across individuals. Our analysis shows that a significant net health benefit may be attained by adaptive NPIs formed by partial social distancing measures, coupled with moderate levels of the society's willingness to pay for health gains (health losses averted). We demonstrate that a socially acceptable balance between health effects and incurred economic costs is achievable over a long term, despite possible early setbacks.