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.

翻译：大规模检测在疫情防控中至关重要，但资源往往受到严重制约。本研究针对感染概率异质且个体在阴性检测结果中可获得效用的群体，探讨混样检测的最优应用方案。研究表明，重叠检测相对于非重叠检测的福利增益存在边界。此外，在概念和操作上更为简单的非重叠分配方案在实证中接近最优，我们设计了一种启发式机制以寻找这些近优检测分配方案。数值实验验证了该启发式方法在实际应用中的有效性与可行性。我们还在真实场景中实施了效用加权混样检测，并提供了其实验效果证据。在墨西哥某高等教育研究机构开展的试点研究中，未发现检测方案参与者的表现和心理健康状况较之"无检测者完全检测"的一阶最优反事实情景更差的证据。