Pooled testing offers an efficient solution to the unprecedented testing demands of the COVID-19 pandemic, although with potentially lower sensitivity and increased costs to implementation in some settings. Assessments of this trade-off typically assume pooled specimens are independent and identically distributed. Yet, in the context of COVID-19, these assumptions are often violated: testing done on networks (housemates, spouses, co-workers) captures correlated individuals, while infection risk varies substantially across time, place and individuals. Neglecting dependencies and heterogeneity may bias established optimality grids and induce a sub-optimal implementation of the procedure. As a lesson learned from this pandemic, this paper highlights the necessity of integrating field sampling information with statistical modeling to efficiently optimize pooled testing. Using real data, we show that (a) greater gains can be achieved at low logistical cost by exploiting natural correlations (non-independence) between samples -- allowing improvements in sensitivity and efficiency of up to 30% and 90% respectively; and (b) these gains are robust despite substantial heterogeneity across pools (non-identical). Our modeling results complement and extend the observations of Barak et al (2021) who report an empirical sensitivity well beyond expectations. Finally, we provide an interactive tool for selecting an optimal pool size using contextual information
翻译:集合测试为COVID-19大流行前所未有的测试需求提供了一个有效的解决方案,尽管其敏感度可能降低,在某些情况下执行成本可能增加。对这一交易的评估通常假定集合标本是独立的,分布相同。然而,在COVID-19大流行的背景下,这些假设常常被违反:在网络(室友、配偶、同事)捕获相关个人时进行测试,而感染风险因时间、地点和个人而异差异很大。忽视依赖性和异质性可能会偏向于建立最佳性网格,并导致程序实施不优化。作为从这一流行病中汲取的教训,本文件强调必须将实地抽样信息与统计模型相结合,以高效优化集合测试。我们利用真实数据表明:(a) 利用样本之间的自然关联(不独立),可以以较低的后勤成本取得更大收益 -- -- 使敏感度和效率分别提高30%和90%;(b) 这些收益是稳健的,尽管集合体之间存在实质性差异(不相同的),因此,本文件强调,有必要将实地抽样信息与统计模型结合起来,以便高效优化集合测试。我们利用一个互动的模型,最后选择一个背景性工具,以便提供最佳的敏感度报告(2021)。