  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.
