binGroup2: Statistical Tools for Infection Identification via Group Testing.
| Autor: | Bilder CR; University of Nebraska-Lincoln, Department of Statistics, Lincoln, NE 68583, USA., Hitt BD; United States Air Force Academy, Department of Mathematical Sciences, Colorado Springs, CO 80840, USA., Biggerstaff BJ; Centers for Disease Control and Prevention, Division of Vector-Borne Diseases, Fort Collins, CO 80521, USA., Tebbs JM; University of South Carolina, Department of Statistics, Columbia, SC 29208, USA., McMahan CS; Clemson University, School of Mathematical and Statistical Sciences, Clemson, SC 29634, USA. |
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| Jazyk: | angličtina |
| Zdroj: | The R journal [R J] 2023 Dec; Vol. 15 (4), pp. 21-36. Date of Electronic Publication: 2024 Apr 10. |
| DOI: | 10.32614/rj-2023-081 |
| Abstrakt: | 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. |
| Databáze: | MEDLINE |
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