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Group Testing with Consideration of the Dilution Effect

Author

Listed:
  • Haoran Jiang

    (Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794-3600, USA)

  • Hongshik Ahn

    (Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794-3600, USA)

  • Xiaolin Li

    (Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794-3600, USA)

Abstract

We propose a method of group testing by taking dilution effects into consideration. We estimate the dilution effect based on massively collected RT-PCR threshold cycle data and incorporate them into optimizing group tests. The new constraint helps find a robust solution of a nonlinear equation. The proposed framework has the flexibility to incorporate geographic and demographic information. We conduct a Monte Carlo simulation to compare different group testing approaches under the estimated dilution effect. This study suggests that increased group size adversely impacts the false negative rate significantly when the infection rate is relatively low. Group tests with optimal pool sizes improve the sensitivity over group tests with a fixed pool size. Based on our simulation study, we recommend single group testing with optimal group sizes.

Suggested Citation

  • Haoran Jiang & Hongshik Ahn & Xiaolin Li, 2022. "Group Testing with Consideration of the Dilution Effect," Mathematics, MDPI, vol. 10(3), pages 1-14, February.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:3:p:497-:d:741808
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    References listed on IDEAS

    as
    1. Hae-Young Kim & Michael G. Hudgens & Jonathan M. Dreyfuss & Daniel J. Westreich & Christopher D. Pilcher, 2007. "Comparison of Group Testing Algorithms for Case Identification in the Presence of Test Error," Biometrics, The International Biometric Society, vol. 63(4), pages 1152-1163, December.
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