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An optimal design for hierarchical generalized group testing

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  • Yaakov Malinovsky
  • Gregory Haber
  • Paul S. Albert

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.

Suggested Citation

  • Yaakov Malinovsky & Gregory Haber & Paul S. Albert, 2020. "An optimal design for hierarchical generalized group testing," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(3), pages 607-621, June.
    • Handle: RePEc:bla:jorssc:v:69:y:2020:i:3:p:607-621
    DOI: 10.1111/rssc.12409
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    References listed on IDEAS

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    1. Michael S. Black & Christopher R. Bilder & Joshua M. Tebbs, 2015. "Optimal retesting configurations for hierarchical group testing," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 64(4), pages 693-710, August.
    2. D. V. Lindley, 1961. "Dynamic Programming and Decision Theory," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 10(1), pages 39-51, March.
    3. Bilder, Christopher R. & Tebbs, Joshua M. & Chen, Peng, 2010. "Informative Retesting," Journal of the American Statistical Association, American Statistical Association, vol. 105(491), pages 942-955.
    4. Yaakov Malinovsky & Paul S. Albert & Anindya Roy, 2016. "Reader reaction: A note on the evaluation of group testing algorithms in the presence of misclassification," Biometrics, The International Biometric Society, vol. 72(1), pages 299-302, March.
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    Cited by:

    1. Daniel K. Sewell, 2022. "Leveraging network structure to improve pooled testing efficiency," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1648-1662, November.

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