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An optimal design for hierarchical generalized group testing
Author(s) -
Malinovsky Yaakov,
Haber Gregory,
Albert Paul S.
Publication year - 2020
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.1111/rssc.12409
Subject(s) - imperfect , computer science , group testing , mathematical optimization , test strategy , optimal design , dynamic programming , population , sequential analysis , machine learning , algorithm , mathematics , statistics , medicine , programming language , software , philosophy , linguistics , environmental health , combinatorics
Summary 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.