Centre for Human Genetics, College of Medicine and Health Sciences, University of Rwanda, Kigali, Rwanda.
Rwanda Joint Task Force COVID-19, Rwanda Biomedical Centre, Ministry of Health, Kigali, Rwanda.
Nature. 2021 Jan;589(7841):276-280. doi: 10.1038/s41586-020-2885-5. Epub 2020 Oct 21.
Suppressing infections of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) will probably require the rapid identification and isolation of individuals infected with the virus on an ongoing basis. Reverse-transcription polymerase chain reaction (RT-PCR) tests are accurate but costly, which makes the regular testing of every individual expensive. These costs are a challenge for all countries around the world, but particularly for low-to-middle-income countries. Cost reductions can be achieved by pooling (or combining) subsamples and testing them in groups. A balance must be struck between increasing the group size and retaining test sensitivity, as sample dilution increases the likelihood of false-negative test results for individuals with a low viral load in the sampled region at the time of the test. Similarly, minimizing the number of tests to reduce costs must be balanced against minimizing the time that testing takes, to reduce the spread of the infection. Here we propose an algorithm for pooling subsamples based on the geometry of a hypercube that, at low prevalence, accurately identifies individuals infected with SARS-CoV-2 in a small number of tests and few rounds of testing. We discuss the optimal group size and explain why, given the highly infectious nature of the disease, largely parallel searches are preferred. We report proof-of-concept experiments in which a positive subsample was detected even when diluted 100-fold with negative subsamples (compared with 30-48-fold dilutions described in previous studies). We quantify the loss of sensitivity due to dilution and discuss how it may be mitigated by the frequent re-testing of groups, for example. With the use of these methods, the cost of mass testing could be reduced by a large factor. At low prevalence, the costs decrease in rough proportion to the prevalence. Field trials of our approach are under way in Rwanda and South Africa. The use of group testing on a massive scale to monitor infection rates closely and continually in a population, along with the rapid and effective isolation of people with SARS-CoV-2 infections, provides a promising pathway towards the long-term control of coronavirus disease 2019 (COVID-19).
抑制严重急性呼吸综合征冠状病毒 2（SARS-CoV-2）的感染可能需要持续快速识别和隔离病毒感染者。逆转录聚合酶链反应（RT-PCR）检测准确但昂贵，这使得对每个人进行定期检测的费用高昂。这些成本对世界各国都是一个挑战，但对于中低收入国家来说尤其如此。通过合并（或组合）亚样本并分组进行检测，可以降低成本。必须在增加组的大小和保留测试灵敏度之间取得平衡，因为随着样本稀释，在测试时被采样区域病毒载量低的个体的假阴性测试结果的可能性增加。同样，必须在降低测试成本和最小化测试时间之间取得平衡，以减少感染的传播。在这里，我们提出了一种基于超立方体几何形状的亚样本合并算法，在低流行率下，该算法可以在少数几次测试和几轮测试中准确识别 SARS-CoV-2 感染者。我们讨论了最佳的组大小，并解释了为什么在疾病具有高度传染性的情况下，更喜欢大致平行的搜索。我们报告了概念验证实验的结果，即使与先前研究中描述的 30-48 倍稀释相比，用阴性亚样本稀释 100 倍也能检测到阳性亚样本。我们量化了由于稀释导致的灵敏度损失，并讨论了如何通过频繁地重新测试组来减轻这种损失，例如。通过使用这些方法，大规模检测的成本可以大大降低。在低流行率下，成本的减少大致与流行率成比例。我们的方法正在卢旺达和南非进行实地试验。在人群中大规模使用分组检测来密切持续监测感染率，并迅速有效地隔离 SARS-CoV-2 感染者，为长期控制 2019 年冠状病毒病（COVID-19）提供了一个有希望的途径。
