Manchester Centre for Genomic Medicine, St. Mary's Hospital, Manchester University NHS Foundation Trust, Manchester, United Kingdom; Division of Evolution and Genomic Sciences, School of Biological Sciences, University of Manchester, Manchester, United Kingdom.
DS Analytics and Machine Learning Ltd., London, United Kingdom.
J Mol Diagn. 2021 May;23(5):532-540. doi: 10.1016/j.jmoldx.2021.01.010. Epub 2021 Feb 4.
Routine testing for severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) in health care workers (HCWs) is critical. Group testing strategies to increase capacity facilitate mass population testing but do not prioritize turnaround time, an important consideration for HCW screening. We propose a nonadaptive combinatorial (NAC) group testing strategy to increase throughput while facilitating rapid turnaround. NAC matrices were constructed for sample sizes of 700, 350, and 250. Matrix performance was tested by simulation under different SARS-CoV-2 prevalence scenarios of 0.1% to 10%. NAC matrices were compared versus Dorfman sequential (DS) group testing approaches. NAC matrices performed well at low prevalence levels, with an average of 97% of samples resolved after a single round of testing via the n = 700 matrix at a prevalence of 1%. In simulations of low to medium (0.1% to 3%) prevalence, all NAC matrices were superior to the DS strategy, measured by fewer repeated tests required. At very high prevalence levels (10%), the DS matrix was marginally superior, although both group testing approaches performed poorly at high prevalence levels. This strategy maximizes the proportion of samples resolved after a single round of testing, allowing prompt return of results to HCWs. This methodology may allow laboratories to adapt their testing scheme based on required throughput and the current population prevalence, facilitating a data-driven testing strategy.
对医护人员（HCWs）进行严重急性呼吸综合征冠状病毒 2（SARS-CoV-2）的常规检测至关重要。群体检测策略可提高检测能力，便于对大量人群进行检测，但无法优先考虑周转时间，这是 HCW 筛查的一个重要考虑因素。我们提出了一种非自适应组合（NAC）群体检测策略，以提高检测通量，同时促进快速周转。构建了样本量为 700、350 和 250 的 NAC 矩阵。在 SARS-CoV-2 流行率为 0.1%至 10%的不同情况下，通过模拟对矩阵性能进行了测试。将 NAC 矩阵与 Dorfman 序贯（DS）群体检测方法进行了比较。NAC 矩阵在低流行率水平下表现良好，在流行率为 1%时，通过 n = 700 矩阵进行一轮测试，平均有 97%的样本得到解决。在低至中等流行率（0.1%至 3%）的模拟中，所有 NAC 矩阵在所需重复测试次数较少的情况下均优于 DS 策略。在非常高的流行率水平（10%）下，DS 矩阵略有优势，尽管这两种群体检测方法在高流行率水平下的性能都不佳。这种策略最大限度地提高了一轮测试后解决样本的比例，允许迅速将结果返回给 HCWs。这种方法可以使实验室根据所需的检测通量和当前人群的流行率来调整检测方案，从而促进数据驱动的检测策略。
