binGroup2:通过分组测试进行感染识别的统计工具。

IF 2.3 4区 计算机科学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS R Journal Pub Date : 2023-12-01 Epub Date: 2024-04-10 DOI:10.32614/rj-2023-081
Christopher R Bilder, Brianna D Hitt, Brad J Biggerstaff, Joshua M Tebbs, Christopher S McMahan
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引用次数: 0

摘要

分组检测是将检测项目合并而不是单独进行检测,以确定每个项目的二进制状态。在 COVID-19 大流行期间,通过对标本进行 SARS-CoV-2 检测,分组检测的使用尤为重要。在这一应用和许多其他应用中采用分组检测的原因是,阴性检测组的成员可能只需一次检测就可宣布为阴性。这就大大提高了实验室的检测能力。无论何时将分组检测算法付诸实践,实验室都必须了解该算法的运行特性,如预期的检测次数。本文介绍的 binGroup2 软件包为此提供了统计工具。该 R 软件包是首个针对各种算法的分组测试识别问题的软件包。我们通过 COVID-19 和衣原体/淋病的分组测试应用来说明其用途。
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binGroup2: Statistical Tools for Infection Identification via Group Testing.

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.

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来源期刊
R Journal
R Journal COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-STATISTICS & PROBABILITY
CiteScore
2.70
自引率
0.00%
发文量
40
审稿时长
>12 weeks
期刊介绍: The R Journal is the open access, refereed journal of the R project for statistical computing. It features short to medium length articles covering topics that should be of interest to users or developers of R. The R Journal intends to reach a wide audience and have a thorough review process. Papers are expected to be reasonably short, clearly written, not too technical, and of course focused on R. Authors of refereed articles should take care to: - put their contribution in context, in particular discuss related R functions or packages; - explain the motivation for their contribution; - provide code examples that are reproducible.
期刊最新文献
binGroup2: Statistical Tools for Infection Identification via Group Testing. glmmPen: High Dimensional Penalized Generalized Linear Mixed Models. Three-Way Correspondence Analysis in R nlstac: Non-Gradient Separable Nonlinear Least Squares Fitting A Workflow for Estimating and Visualising Excess Mortality During the COVID-19 Pandemic
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