Effective, Fair and Equitable Pandemic Rationing

Abstract

We study the problem of allocating scarce and heterogeneous medical resources to patients. Our solutions optimize for aggregate match-effectiveness subject to fairness and distributional constraints. The main solution uses a novel cutoff characterization result for fair and equitable allocations and a minimum-cost flow formulation. Match-effectiveness gains from optimization are substantial. Even when there are only two types of vaccines, in equal quantities, our algorithm results in more than 33% larger aggregate match-effectiveness compared to the random allocation benchmark.