Management Science · 2026 · Jiayue Wan、Yujia Zhang、Peter I. Frazier
中文摘要
全员筛查是控制传染病的有力工具。混样检测能够在资源有限的条件下实现此类筛查。各混合样本的病毒浓度之间往往呈正相关,原因可能是患病率与样本采集受地理位置的影响,也可能是出于有意强化,即按照风险或家庭对样本进行混合。已知当检测灵敏度固定时,这种相关性能够提高检测效率。然而在现实中,检测的灵敏度取决于分析物(如病毒RNA)的浓度,即所谓的稀释效应:混合池规模越大,灵敏度越低。我们表明,在最广泛使用的混样检测程序——两阶段Dorfman程序下,浓度依赖性检测误差会改变相关性的影响。我们证明,当检测灵敏度随浓度提高时:将相关样本混合在一起(相关混合)相比独立地混合样本(朴素混合)可实现渐近更高的灵敏度。相反,在与浓度无关的情形下,相关性不影响灵敏度。此外,在浓度依赖性误差下,相关性可能使检测效率相比朴素混合有所下降;而在与浓度无关的误差下,相关性总能提高效率。我们提出一种衡量检测资源占用的替代指标——每消耗一次检测所发现的阳性数量,并论证该指标更契合感染控制的目标;我们证明,在这一指标上相关混合优于朴素混合。在模拟中,我们表明,在现实的浓度依赖性检测误差下相关性的影响,与在灵敏度固定假设下的相关性影响存在实质性差异。我们的发现凸显了政策制定者使用纳入自然发生相关性的模型、并考虑强化这种相关性的方式的重要性。本文由Carri Chan(医疗管理)接收。资助:本研究得到康奈尔大学教务长办公室、空军科学研究办公室[资助号FA9550-19-1-0283]以及美国国家科学基金会数学科学部[资助号DMS2230023]的支持。补充材料:在线附录与数据文件可从 https://doi.org/10.1287/mnsc.2021.04217 获取。
Abstract
Population-wide screening is a powerful tool for controlling infectious diseases. Group testing can enable such screening despite limited resources. Viral concentration of pooled samples are often positively correlated, either because prevalence and sample collection are influenced by location, or through intentional enhancement via pooling samples according to risk or household. Such correlation is known to improve efficiency when test sensitivity is fixed. However, in reality, a test’s sensitivity depends on the concentration of the analyte (e.g., viral RNA), as in the so-called dilution effect, where sensitivity decreases for larger pools. We show that concentration-dependent test error alters correlation’s effect under the most widely used group testing procedure, the two-stage Dorfman procedure. We prove that when test sensitivity increases with concentration: pooling correlated samples together (correlated pooling) achieves asymptotically higher sensitivity than independently pooling the samples (naive pooling). In contrast, in the concentration-independent case, correlation does not affect sensitivity. Moreover, with concentration-dependent errors, correlation can degrade test efficiency compared with naive pooling, whereas under concentration-independent errors, correlation always improves efficiency. We propose an alternative measure of test resource usage, the number of positives found per test consumed, which we argue is better aligned with infection control, and show that correlated pooling outperforms naive pooling on this measure. In simulation, we show that the effect of correlation under realistic concentration-dependent test error is meaningfully different from correlation’s effect assuming fixed sensitivity. Our findings underscore the importance for policy makers of using models that incorporate naturally occurring correlation and of considering ways of strengthening this correlation. This paper was accepted by Carri Chan, healthcare management. Funding: This work was supported by the Provost’s Office of Cornell University, the Air Force Office of Scientific Research [Grant FA9550-19-1-0283], and the National Science Foundation Division of Mathematical Sciences [Grant DMS2230023]. Supplemental Material: The online appendices and data files are available at https://doi.org/10.1287/mnsc.2021.04217 .