用路径积分控制优化防疫策略，兼顾公平与效果。
Learning Fair Policies for Infectious Diseases Mitigation using Path Integral Control
- 基于随机多群体SIR模型，学习公平的疫苗与封控策略
- 相比传统方法，显著提升政策公平性，降低不平等风险
- 适合关注公平性与公共政策设计的研究者和决策者
传染病对社会构成重大公共卫生挑战，设计有效政策以减少经济损失和死亡人数至关重要。本文提出一种在不确定性下的序贯决策框架，用于设计注重公平性的疾病缓解策略，整合多种不公平度量。具体而言，我们的方法基于随机多群体SIR模型，学习公平的疫苗分配与封锁策略。为应对由此产生的序贯决策难题，采用路径积分控制算法作为高效求解方案。通过案例研究，我们证明该方法能有效提升公平性，相较传统方法显著改善不平等状况，并为政策制定者提供重要参考。
原文摘要 · Abstract (English)
Infectious diseases pose major public health challenges to society, highlighting the importance of designing effective policies to reduce economic loss and mortality. In this paper, we propose a framework for sequential decision-making under uncertainty to design fairness-aware disease mitigation policies that incorporate various measures of unfairness. Specifically, our approach learns equitable vaccination and lockdown strategies based on a stochastic multi-group SIR model. To address the challenges of solving the resulting sequential decision-making problem, we adopt the path integral control algorithm as an efficient solution scheme. Through a case study, we demonstrate that our approach effectively improves fairness compared to conventional methods and provides valuable insights for policymakers.
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