Learning Fair Policies for Infectious Diseases Mitigation using Path Integral Control
This addresses the problem of unfairness in public health policies for infectious diseases, but it is incremental as it applies an existing method to a new fairness context.
The paper tackled designing fairness-aware disease mitigation policies under uncertainty, demonstrating improved fairness compared to conventional methods in a case study.
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.