Daon Yair, Huppert Amit, Obolski Uri
School of Public Health, The Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
Porter School of the Environment and Earth Sciences, The Faculty of Exact Sciences, Tel Aviv University, Tel Aviv, Israel.
R Soc Open Sci. 2021 Nov 3;8(11):210704. doi: 10.1098/rsos.210704. eCollection 2021 Nov.
Pooling is a method of simultaneously testing multiple samples for the presence of pathogens. Pooling of SARS-CoV-2 tests is increasing in popularity, due to its high testing throughput. A popular pooling scheme is Dorfman pooling: test individuals simultaneously, if the test is positive, each individual is then tested separately; otherwise, all are declared negative. Most analyses of the error rates of pooling schemes assume that including more than a single infected sample in a pooled test does not increase the probability of a positive outcome. We challenge this assumption with experimental data and suggest a novel and parsimonious probabilistic model for the outcomes of pooled tests. As an application, we analyse the false-negative rate (i.e. the probability of a negative result for an infected individual) of Dorfman pooling. We show that the false-negative rates under Dorfman pooling increase when the prevalence of infection decreases. However, low infection prevalence is exactly the condition when Dorfman pooling achieves highest throughput efficiency. We therefore urge the cautious use of pooling and development of pooling schemes that consider correctly accounting for tests' error rates.
混合检测是一种同时检测多个样本中病原体存在情况的方法。由于其高检测通量，SARS-CoV-2检测的混合检测越来越受欢迎。一种流行的混合检测方案是 Dorfman 混合检测：同时检测个体，如果检测呈阳性，则对每个个体进行单独检测；否则，全部判定为阴性。大多数对混合检测方案错误率的分析都假定在一个混合检测中包含多个感染样本不会增加阳性结果的概率。我们用实验数据对这一假设提出质疑，并为混合检测的结果提出了一种新颖且简洁的概率模型。作为一个应用，我们分析了 Dorfman 混合检测的假阴性率（即感染个体检测结果为阴性的概率）。我们表明，当感染率降低时，Dorfman 混合检测下的假阴性率会增加。然而，低感染率恰恰是 Dorfman 混合检测实现最高通量效率的条件。因此，我们敦促谨慎使用混合检测，并开发能够正确考虑检测错误率的混合检测方案。
