集合檢測方案用於識別低流行下的SARS-CoV-2感染
作者：
小柯機器人發布時間：2020/10/25 22:16:06
盧安達非洲數學研究所Wilfred Ndifon、Neil Turok等研究人員合作開發出集合檢測方案用於識別低流行下的SARS-CoV-2感染。相關論文於2020年10月21日在線發表於國際學術期刊《自然》。
研究人員提出了一種基於超立方體幾何的合併樣本算法，該算法在較低的流行率下，可以在少量測試和測試輪次中準確地識別出受感染的個體。研究人員討論了最佳群體規模，並解釋了鑑於該疾病的高度傳染性，為什麼首選平行搜索的原因。研究人員報告了概念驗證實驗，其中即使用陰性樣品稀釋100倍也能檢測到陽性樣品。
研究人員量化了由於稀釋引起的靈敏度損失，並討論了如何通過頻繁地重新測試組來減少損失。通過使用這些方法，可以將質量檢測的成本大幅度降低，而且隨著患病率的降低，檢測的成本也會增加。
這一方法在盧安達和南非正在進行現場試驗。使用大規模的集體測試來密切、連續地監控人群中的感染，以及快速有效地隔離感染人群，為長期控制COVID-19提供了一條有希望的途徑。
據介紹，SARS-CoV-2的遏制可能需要持續不斷地快速識別和隔離受感染的個體。逆轉錄聚合酶鏈反應（RT-PCR）測試準確但成本高昂，因此對每個人進行定期測試都很昂貴。成本對所有國家，特別是發展中國家都是一個挑戰。可以通過合併多個樣本並以成組的方式中對其進行測試來降低成本。組規模的增加和測試靈敏度之間需要取得平衡，因為在測試時，樣品稀釋會增加在採樣區域中病毒載量低的個體產生假陰性的可能性。同樣，測試次數的減少也必須與測試時間的降低相平衡。
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