  Large-scale testing is crucial in pandemic containment, but resources are
often prohibitively constrained. We study the optimal application of pooled
testing for populations that are heterogeneous with respect to an individual's
infection probability and utility that materializes if included in a negative
test. We show that the welfare gain from overlapping testing over
non-overlapping testing is bounded. Moreover, non-overlapping allocations,
which are both conceptually and logistically simpler to implement, are
empirically near-optimal, and we design a heuristic mechanism for finding these
near-optimal test allocations. In numerical experiments, we highlight the
efficacy and viability of our heuristic in practice. We also implement and
provide experimental evidence on the benefits of utility-weighted pooled
testing in a real-world setting. Our pilot study at a higher education research
institute in Mexico finds no evidence that performance and mental health
outcomes of participants in our testing regime are worse than under the
first-best counterfactual of full access for individuals without testing.
