Effective, Fair and Equitable Pandemic Rationing

We study the problem of allocating COVID-19 vaccines or other scarce and heterogeneous medical resources to patients. We develop algorithms for maximizing aggregate match-effectiveness subject to fairness and distributional constraints. Our 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