Stochastic Optimization for Vaccine and Testing Kit Allocation for the COVID-19 Pandemic

TL;DR

This paper introduces a reinforcement learning-based optimization framework for resource allocation during the COVID-19 pandemic, improving testing and vaccine distribution strategies through active learning and lookahead policies.

Contribution

It develops a general model combining active learning with a tunable lookahead policy for vaccine and testing kit allocation, outperforming traditional myopic strategies.

Findings

Lookahead policy outperforms myopic policy in simulations

Optimization improves resource allocation efficiency

Active learning enhances understanding of uncertain states

Abstract

The pandemic caused by the SARS-CoV-2 virus has exposed many flaws in the decision-making strategies used to distribute resources to combat global health crises. In this paper, we leverage reinforcement learning and optimization to improve upon the allocation strategies for various resources. In particular, we consider a problem where a central controller must decide where to send testing kits to learn about the uncertain states of the world (active learning); then, use the new information to construct beliefs about the states and decide where to allocate resources. We propose a general model coupled with a tunable lookahead policy for making vaccine allocation decisions without perfect knowledge about the state of the world. The lookahead policy is compared to a population-based myopic policy which is more likely to be similar to the present strategies in practice. Each vaccine allocation policy works in conjunction with a testing kit allocation policy to perform active learning. Our simulation results demonstrate that an optimization-based lookahead decision making strategy will outperform the presented myopic policy.