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Leveraging network structure to improve pooled testing efficiency

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  • Daniel K. Sewell

Abstract

Screening is a powerful tool for infection control, allowing for infectious individuals, whether they be symptomatic or asymptomatic, to be identified and isolated. The resource burden of regular and comprehensive screening can often be prohibitive, however. One such measure to address this is pooled testing, whereby groups of individuals are each given a composite test; should a group receive a positive diagnostic test result, those comprising the group are then tested individually. Infectious disease is spread through a transmission network, and this paper shows how assigning individuals to pools based on this underlying network can improve the efficiency of the pooled testing strategy, thereby reducing the resource burden. We designed a simulated annealing algorithm to improve the pooled testing efficiency as measured by the ratio of the expected number of correct classifications to the expected number of tests performed. We then evaluated our approach using an agent‐based model designed to simulate the spread of SARS‐CoV‐2 in a school setting. Our results suggest that our approach can decrease the number of tests required to regularly screen the student body, and that these reductions are quite robust to assigning pools based on partially observed or noisy versions of the network.

Suggested Citation

  • 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.
    • Handle: RePEc:bla:jorssc:v:71:y:2022:i:5:p:1648-1662
    DOI: 10.1111/rssc.12594
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    References listed on IDEAS

    as
    1. Yaakov Malinovsky, 2019. "Sterrett Procedure for the Generalized Group Testing Problem," Methodology and Computing in Applied Probability, Springer, vol. 21(3), pages 829-840, September.
    2. 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.
    3. Samuel D. Lendle & Michael G. Hudgens & Bahjat F. Qaqish, 2012. "Group Testing for Case Identification with Correlated Responses," Biometrics, The International Biometric Society, vol. 68(2), pages 532-540, June.
    4. 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.
    5. 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.
    6. 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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