Learning Fair Policies for Infectious Diseases Mitigation using Path Integral Control

TL;DR

This paper introduces a novel framework for designing fair and effective infectious disease mitigation policies using path integral control, addressing uncertainty and fairness in vaccination and lockdown strategies.

Contribution

It presents a new approach combining stochastic multi-group SIR modeling with path integral control to optimize fair disease mitigation policies.

Findings

Improves fairness in vaccination and lockdown strategies

Efficiently solves complex sequential decision problems

Provides actionable insights for policymakers

Abstract

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.