Optimal Epidemic Control as a Contextual Combinatorial Bandit with Budget

TL;DR

This paper models epidemic control policy optimization as a contextual combinatorial bandit problem, aiming to balance resource use and epidemic suppression through real-time intervention planning.

Contribution

It introduces a novel bandit-based framework for dynamic epidemic policy optimization considering multiple criteria and resource constraints.

Findings

The approach achieves Pareto optimal solutions in simulated epidemic scenarios.

It effectively balances intervention stringency and epidemic control.

The method adapts to real-time data for improved policy recommendations.

Abstract

In light of the COVID-19 pandemic, it is an open challenge and critical practical problem to find a optimal way to dynamically prescribe the best policies that balance both the governmental resources and epidemic control in different countries and regions. To solve this multi-dimensional tradeoff of exploitation and exploration, we formulate this technical challenge as a contextual combinatorial bandit problem that jointly optimizes a multi-criteria reward function. Given the historical daily cases in a region and the past intervention plans in place, the agent should generate useful intervention plans that policy makers can implement in real time to minimizing both the number of daily COVID-19 cases and the stringency of the recommended interventions. We prove this concept with simulations of multiple realistic policy making scenarios and demonstrate a clear advantage in providing a pareto optimal solution in the epidemic intervention problem.