Group testing in heterogeneous populations by using halving algorithms

Michael S. Black, Christopher R. Bilder, Joshua M. Tebbs

Research output: Contribution to journalArticle

12 Scopus citations

Abstract

Group (pooled) testing is often used to reduce the total number of tests that are needed to screen a large number of individuals for an infectious disease or some other binary characteristic. Traditionally, research in group testing has assumed that each individual is independent with the same risk of positivity. More recently, there has been a growing set of literature generalizing previous work in group testing to include heterogeneous populations so that each individual has a different risk of positivity. We investigate the effect of acknowledging population heterogeneity on a commonly used group testing procedure which is known as 'halving'. For this procedure, positive groups are successively split into two equal-sized halves until all groups test negatively or until individual testing occurs. We show that heterogeneity does not affect the mean number of tests when individuals are randomly assigned to subgroups. However, when individuals are assigned to subgroups on the basis of their risk probabilities, we show that our proposed procedures reduce the number of tests by taking advantage of the heterogeneity. This is illustrated by using chlamydia and gonorrhoea screening data from the state of Nebraska.

Original languageEnglish (US)
Pages (from-to)277-290
Number of pages14
JournalJournal of the Royal Statistical Society. Series C: Applied Statistics
Volume61
Issue number2
DOIs
StatePublished - Mar 1 2012

Keywords

  • Binary response
  • Classification
  • Identification
  • Pooled testing
  • Retesting
  • Screening

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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