Stochastic Optimization for Vaccine and Testing Kit Allocation for the COVID-19 Pandemic
This work aims to improve resource allocation strategies for global health crises, specifically for vaccine and testing kit distribution during pandemics, which is a critical problem for public health decision-makers.
This paper addresses the challenge of allocating vaccines and testing kits during a pandemic under uncertainty. It proposes an optimization-based lookahead decision-making strategy for vaccine allocation, coupled with a testing kit allocation policy for active learning, and demonstrates that this strategy outperforms a population-based myopic policy in simulations.
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.