论文标题
福利最大化的合并测试
Welfare-Maximizing Pooled Testing
论文作者
论文摘要
大规模测试对于大流行遏制至关重要，但是资源通常受到严格限制。我们研究了对个人的感染概率和效用，该人群的合并测试最佳应用，如果包括在阴性测试中，则可以实现。我们表明，在非重叠测试上重叠测试中的福利增益是有限的。此外，在概念上和逻辑上更简单地实施的非重叠分配在经验上是近乎最佳的，我们设计了一种启发式机制，用于查找这些近乎最佳的测试分配。在数值实验中，我们在实践中强调了我们启发式的功效和生存能力。我们还实施并提供有关在现实世界中实用加权汇总测试的好处的实验证据。我们在墨西哥一家高等教育研究所的试点研究没有证据表明，我们的测试制度参与者的表现和心理健康成果要比在没有测试的情况下完全访问的第一最好的反事实。
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.
