Welfare-Maximizing Pooled Testing

This letter provides an overview of our recent work on COVID-19 testing mechanisms that appeared at EC'23. Large-scale testing is crucial in pandemics but resources are often prohibitively constrained. We study a scenario in which a population under lockdown utilizes a limited budget of tests to allow healthy individuals to resume in-person activities. Our work explores the optimal allocation of pooled tests in populations that are heterogeneous with respect to individual infection probabilities and utilities that materialize if included in a negative test (and being permitted to resume in-person activities). Non-overlapping allocations of tests, where no individual in the population is included in more than one pooled test, are both conceptually and logistically simpler to implement. We show that the welfare gain from overlapping testing over non-overlapping testing is bounded. Moreover, we design a heuristic mechanism for finding test allocations that is fast and empirically near-optimal. We also implement our mechanism in practice and provide experimental evidence on the benefits of utility-weighted pooled testing in a real-world setting. Our randomized trial at a higher education research institute in Mexico suggests that performance and mental health outcomes of participants under our testing mechanism are no worse than under the counterfactual of full access for individuals without testing.