In an epidemic, how should an organization with limited testing resources safely return to in-person activities after a period of lockdown? We study this question in a setting where the population at hand is heterogeneous in both utility for in-person activities and probability of infection. In such a period of re-integration, tests can be used as a certificate of non-infection, whereby those in negative tests are permitted to return to in-person activities for a designated amount of time. Under the assumption that samples can be pooled, the question of how to allocate a limited testing budget in the population to maximize the aggregate utility (i.e. welfare) of negatively-tested individuals who return to in-person activities is non-trivial, with a large space of potential testing allocations. We show that non-overlapping testing allocations, which are both conceptually and (crucially) logistically more simple to implement, are approximately optimal, and we design an efficient greedy algorithm for finding non-overlapping testing allocations with approximately optimal welfare. In computational experiments, we highlight the efficacy and viability of our greedy algorithm in practice. To the best of our knowledge, we are also first to implement and provide causal evidence on the benefits of utility-weighted pooled testing in a real-world setting. Surprisingly, 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 reopening without testing.

翻译：在流行病期间，检测资源有限的组织如何在封锁后安全恢复线下活动？我们研究了一个异质性人群在参与线下活动的效用和感染概率两方面均存在差异的环境下的这一问题。在此类重新融合阶段，检测可作为未感染的证明，允许检测结果为阴性者在一定时间内恢复线下活动。在样本可混合的假设下，如何分配有限的检测预算以最大化重返线下活动的阴性受检者的总效用（即福利）是一个复杂的问题，因为存在大量潜在的检测分配方案。我们证明，非重叠的检测分配方案在概念上和（关键的是）物流实施上更为简单，且近似最优；我们设计了一种高效的贪心算法，用于寻找福利近似最优的非重叠检测分配方案。通过计算实验，我们展示了贪心算法的实际有效性和可行性。据我们所知，我们也是首个在现实环境中实施加权效用混池检测并提供其益处的因果证据的研究。值得注意的是，我们在墨西哥某高等教育研究所进行的试点研究发现，与无检测的全开放这一最优反事实情景相比，参与我们检测方案的参与者在绩效和心理健康结果方面并无更差表现。