用于计算传染病诊断测试的最佳测试池大小的基于web的计算工具

R. Singh, Kishan Khandelia
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引用次数: 0

摘要

在资源有限的条件下,将样本集中起来并对所得混合物进行测试,作为一种显著提高SARS-CoV-2检测率的潜在方法,正引起相当大的兴趣。这种汇集也可用于对其他传染病进行大规模诊断测试,特别是在可用资源有限的情况下。因此,设计一个用户友好的工具来帮助临床医生和政策制定者,为他们的特定情况确定最佳的测试池和子池大小变得非常重要。我们已经开发了这样一个工具;计算器web应用程序可在https://riteshsingh.github.io/poolsize/上获得。本文对所采用的算法进行了描述和分析,并讨论了它们在其他科学领域的应用。我们发现,池化总是以牺牲测试灵敏度为代价,减少所有条件下的预期测试数。No sub-pooling最优池大小计算器将是应用最广泛的一个,因为在大多数情况下,样本数量的限制会限制sub-pooling。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Web-based Computational Tools for Calculating Optimal Testing Pool Size for Diagnostic Tests of Infectious Diseases
Pooling together samples and testing the resulting mixture is gaining considerable interest as a potential method to markedly increase the rate of testing for SARS-CoV-2, given the resource limited conditions. Such pooling can also be employed for carrying out large scale diagnostic testing of other infectious diseases, especially when the available resources are limited. Therefore, it has become important to design a user-friendly tool to assist clinicians and policy makers, to determine optimal testing pool and sub-pool sizes for their specific scenarios. We have developed such a tool; the calculator web application is available at https://riteshsingh.github.io/poolsize/. The algorithms employed are described and analyzed in this paper, and their application to other scientific fields is also discussed. We find that pooling always reduces the expected number of tests in all the conditions, at the cost of test sensitivity. The No sub-pooling optimal pool size calculator will be the most widely applicable one, because limitations of sample quantity will restrict sub-pooling in most conditions.
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