论文标题
在部分干预措施下优化大流行反应的成本效益的一般框架
A general framework for optimising cost-effectiveness of pandemic response under partial intervention measures
论文作者
论文摘要
19009年的大流行构成了巨大的公共卫生和社会经济挑战。疫苗接种和非药物干预措施（NPI）的健康影响通常与巨大的社会和经济成本形成鲜明对比。我们描述了一个旨在获得自适应成本有效的干预措施的一般框架，适合最近和新兴的大流行威胁。我们还量化了净健康益处，并提出了一种强化学习方法，以优化适应性NPI。该方法利用基于代理的模型模拟了澳大利亚的大流行反应，并解释了异质种群，其依从性水平会随着时间和整个个体的变化而波动。我们的分析表明，通过部分社会疏远措施形成的自适应NPI可以实现重大的净健康益处，并加上社会愿意为健康增长付出的中等水平（避免健康损失）。我们证明，尽管可能会遇到早期的挫折，但可以长期实现健康影响与经济成本之间的社会可接受的平衡。
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.
