小柯机器人

集合检测方案用于识别低流行下的SARS-CoV-2感染
2020-10-25 22:16

卢旺达非洲数学研究所Wilfred Ndifon、Neil Turok等研究人员合作开发出集合检测方案用于识别低流行下的SARS-CoV-2感染。相关论文于2020年10月21日在线发表于国际学术期刊《自然》。

研究人员提出了一种基于超立方体几何的合并样本算法,该算法在较低的流行率下,可以在少量测试和测试轮次中准确地识别出受感染的个体。研究人员讨论了最佳群体规模,并解释了鉴于该疾病的高度传染性,为什么首选平行搜索的原因。研究人员报告了概念验证实验,其中即使用阴性样品稀释100倍也能检测到阳性样品。
 
研究人员量化了由于稀释引起的灵敏度损失,并讨论了如何通过频繁地重新测试组来减少损失。通过使用这些方法,可以将质量检测的成本大幅度降低,而且随着患病率的降低,检测的成本也会增加。
 
这一方法在卢旺达和南非正在进行现场试验。使用大规模的集体测试来密切、连续地监控人群中的感染,以及快速有效地隔离感染人群,为长期控制COVID-19提供了一条有希望的途径。
 
据介绍,SARS-CoV-2的遏制可能需要持续不断地快速识别和隔离受感染的个体。逆转录聚合酶链反应(RT-PCR)测试准确但成本高昂,因此对每个人进行定期测试都很昂贵。成本对所有国家,特别是发展中国家都是一个挑战。可以通过合并多个样本并以成组的方式中对其进行测试来降低成本。组规模的增加和测试灵敏度之间需要取得平衡,因为在测试时,样品稀释会增加在采样区域中病毒载量低的个体产生假阴性的可能性。同样,测试次数的减少也必须与测试时间的降低相平衡。
 
附:英文原文

Title: A pooled testing strategy for identifying SARS-CoV-2 at low prevalence

Author: Leon Mutesa, Pacifique Ndishimye, Yvan Butera, Jacob Souopgui, Annette Uwineza, Robert Rutayisire, Ella Larissa Ndoricimpaye, Emile Musoni, Nadine Rujeni, Thierry Nyatanyi, Edouard Ntagwabira, Muhammed Semakula, Clarisse Musanabaganwa, Daniel Nyamwasa, Maurice Ndashimye, Eva Ujeneza, Ivan Emile Mwikarago, Claude Mambo Muvunyi, Jean Baptiste Mazarati, Sabin Nsanzimana, Neil Turok, Wilfred Ndifon

Issue&Volume: 2020-10-21

Abstract: Suppressing SARS-CoV-2 will likely require the rapid identification and isolation of infected individuals on an ongoing basis. Reverse transcription polymerase chain reaction (RT-PCR) tests are accurate but costly, making regular testing of every individual expensive. The costs are a challenge for all countries and particularly for developing countries. Cost reductions can be achieved by pooling (or combining) subsamples and testing them in groups1–7. A balance must be struck between increasing the group size and retaining test sensitivity, since sample dilution increases the likelihood of false negatives for individuals with low viral load in the sampled region at the time of the test8. Likewise, minimising the number of tests to reduce costs must be balanced against minimising the time testing takes to reduce the spread of infection. Here we propose an algorithm for pooling subsamples based on the geometry of a hypercube that, at low prevalence, accurately identifies infected individuals in a small number of tests and 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 (cf. 30-fold to 48-fold dilution in Refs. 9–11). We quantify the loss of sensitivity due to dilution and discuss how it may be mitigated by 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 which, furthermore, increases as the prevalence falls. Field trials of our approach are under way in Rwanda and South Africa. The use of group testing on a massive scale to closely and continually monitor infection in a population, along with rapid and effective isolation of infected people, provides a promising pathway to the longterm control of COVID-19.

DOI: 10.1038/s41586-020-2885-5

Source: https://www.nature.com/articles/s41586-020-2885-5

Nature:《自然》,创刊于1869年。隶属于施普林格·自然出版集团,最新IF:43.07
官方网址:http://www.nature.com/
投稿链接:http://www.nature.com/authors/submit_manuscript.html


本期文章:《自然》:Online/在线发表

分享到:

0