ABSTRACT
In the summer of 2020, in collaboration with the Greek government, we designed and deployed Eva-the first national-scale, reinforcement learning system for targeted COVID-19 testing. In this paper, we detail the rationale for three major design/algorithmic elements: Eva's testing supply chain, estimating COVID-19 prevalence, and test allocation. Specifically, we describe the design of Eva's supply chain to collect and process thousands of biological samples per day with special emphasis on capacity procurement. Then, we propose a novel, empirical Bayes estimation strategy to estimate COVID-19 prevalence among various passenger types with limited data and showcase how these estimates were instrumental in making a variety of downstream decisions. Finally, we propose a novel, multiarmed bandit algorithm that dynamically allocates tests to arriving passengers in a nonstationary environment with delayed feedback and batched decisions. All our design and algorithmic choices emphasize the need for transparent reasoning to enable human-in-the-loop analytics. Such transparency was crucial to building trust and acceptance among policymakers and public health experts in a period of global crisis.