Bilder Christopher R, Hitt Brianna D, Biggerstaff Brad J, Tebbs Joshua M, McMahan Christopher S
University of Nebraska-Lincoln, Department of Statistics, Lincoln, NE 68583, USA.
United States Air Force Academy, Department of Mathematical Sciences, Colorado Springs, CO 80840, USA.
R J. 2023 Dec;15(4):21-36. doi: 10.32614/rj-2023-081. Epub 2024 Apr 10.
Group testing is the process of testing items as an amalgamation, rather than separately, to determine the binary status for each item. Its use was especially important during the COVID-19 pandemic through testing specimens for SARS-CoV-2. The adoption of group testing for this and many other applications is because members of a negative testing group can be declared negative with potentially only one test. This subsequently leads to significant increases in laboratory testing capacity. Whenever a group testing algorithm is put into practice, it is critical for laboratories to understand the algorithm's operating characteristics, such as the expected number of tests. Our paper presents the binGroup2 package that provides the statistical tools for this purpose. This R package is the first to address the identification aspect of group testing for a wide variety of algorithms. We illustrate its use through COVID-19 and chlamydia/gonorrhea applications of group testing.
分组检测是将检测项目合并在一起进行检测，而不是单独检测，以确定每个项目的二元状态的过程。在新冠疫情期间，通过检测新冠病毒2型（SARS-CoV-2）的样本，分组检测的应用尤为重要。在这种及许多其他应用中采用分组检测，是因为检测结果为阴性的组中的成员可能只需进行一次检测就可以被判定为阴性。这随后会显著提高实验室检测能力。每当实施分组检测算法时，对于实验室来说，了解该算法的操作特性（如预期检测次数）至关重要。我们的论文介绍了binGroup2软件包，它为此提供了统计工具。这个R软件包是第一个针对各种算法解决分组检测识别问题的软件包。我们通过新冠疫情以及衣原体/淋病分组检测应用来说明它的使用方法。
