binGroup2: Statistical Tools for Infection Identification via Group Testing

Authors

Christopher R. Bilder, University of Nebraska-LincolnFollow
Brianna D. Hitt, United States Air Force Academy
Brad J. Biggerstaff, Centers for Disease Control and Prevention
Joshua M. Tebbs, University of South Carolina
Christopher S. McMahan, Clemson University

Document Type

Article

Date of this Version

12-2023

Citation

The R Journal Vol. 15/4, December 2023 ISSN 2073-4859

Comments

Used by permission.

Abstract

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.