An optimal design for hierarchical generalized group testing
Loading...
Links to Files
Author/Creator
Author/Creator ORCID
Date
2020-04-22
Type of Work
Department
Program
Citation of Original Publication
Malinovsky, Yaakov, Gregory Haber, and Paul S. Albert. “An Optimal Design for Hierarchical Generalized Group Testing.” Journal of the Royal Statistical Society: Series C (Applied Statistics) 69, no. 3 (22 April 2020): 607–21. https://doi.org/10.1111/rssc.12409.
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
Public Domain
Abstract
Choosing an optimal strategy for hierarchical group testing is an important problem for practitioners who are interested in disease screening with limited resources. For example, when screening for infectious diseases in large populations, it is important to use algorithms that minimize the cost of potentially expensive assays. Black and co-workers described this as an intractable problem unless the number of individuals to screen is small. They proposed an approximation to an optimal strategy that is difficult to implement for large population sizes. We develop an optimal design with respect to the expected total number of tests that can be obtained by using a novel dynamic programming algorithm. We show that this algorithm is substantially more efficient than the approach that was proposed by Black and co-workers. In addition, we compare the two designs for imperfect tests. R code is provided for practitioners.