Group Testing Procedures for Screening: Probabilistic Model

Files

GroupTestingProceduresforScreening.pdf (342.11 KB)

Links to Files

https://link.springer.com/rwe/10.1007/978-3-662-69359-9_262

Permanent Link

https://doi.org/10.1007/978-3-662-69359-9_262
 http://hdl.handle.net/11603/39532

Collections

UMBC Mathematics and Statistics Department
UMBC Faculty Collection

Author/Creator

Author/Creator ORCID

https://orcid.org/0000-0003-2888-674X

Date

2025-06-20

Type of Work

book chapters
Text

Department

Program

Citation of Original Publication

Malinovsky, Yaakov, and Paul S. Albert. “Group Testing Procedures for Screening: Probabilistic Model.” In International Encyclopedia of Statistical Science. Springer, Berlin, Heidelberg, 2025. https://doi.org/10.1007/978-3-662-69359-9_262.

Rights

This work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.
Public Domain

Subjects

Abstract

The use of group testing for disease screening has been reinvigorated in the past few years due to COVID-19. To save resources and time, many countries have used group testing strategies as the basis of their COVID-19 national screening programs. Discussions focused on the applications of these testing strategies have appeared both in leading scientific journals (e.g., Shental et al. 2020; Mercer and Salit 2021; Yelin et al. 2020) and in the popular press (as in the Washington Post, NY Times, Scientific American). This article reviews a class of adaptive group testing procedures that operate under a probabilistic model and the error-free testing assumption described below. In nonadaptive group testing, all tests are fixed in advance before the testing. In adaptive group testing, the information from previous tests can be used.