binGroup2: Statistical Tools for Infection Identification via Group Testing.

Area of Science:

• Biostatistics
• Infectious Disease Diagnostics
• Computational Biology

Background:

• Group testing, or pooling samples, significantly increases laboratory testing capacity by reducing the number of individual tests required.
• This strategy proved vital during the COVID-19 pandemic for SARS-CoV-2 testing, enabling higher throughput.
• Understanding the operating characteristics of group testing algorithms is crucial for effective implementation.

Purpose of the Study:

• To introduce the binGroup2 R package, a novel statistical toolkit for group testing analysis.
• To provide tools for identifying the operating characteristics of various group testing algorithms.
• To demonstrate the utility of the package in real-world diagnostic scenarios.

Main Methods:

• Development of the binGroup2 R package in the R statistical programming language.
• Implementation of statistical methods for analyzing group testing algorithms.
• Application of the package to simulated and real-world datasets for COVID-19 and STI testing.

Main Results:

• The binGroup2 package offers comprehensive statistical tools for group testing, including identification aspects.
• The package supports a wide variety of group testing algorithms.
• Illustrative examples show the package's effectiveness in COVID-19 and chlamydia/gonorrhea testing applications.

Conclusions:

• The binGroup2 R package is a valuable resource for researchers and laboratories utilizing group testing.
• It facilitates a deeper understanding and more efficient application of group testing strategies.
• The package enhances diagnostic capacity and accuracy in public health settings.