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Application of Pooled Testing Methodologies in Tackling the COVID-19 Pandemic

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  • Pritha Guha

Abstract

The recent emergence of the COVID-19 pandemic has posed a healthcare challenge, as well as an economic threat to the world. The situation is even worse for the developing nations like India. Diagnostic tests are fundamental to the ability to detect and respond to the disease. However, finding adequate resources for that has proved to be a significant challenge. As a solution, a pooling approach for the diagnostic tests is proposed in the literature and the media. In this article, we explore several algorithms for pooling samples to reduce the number of such diagnostic tests. Through a comparative study, we look at their relative performance at different disease prevalence levels. We also discuss the impact of the sensitivity and specificity of the tests. In the context of using the quantitative reverse-transcription polymerase chain reaction (RT-qPCR) tests for diagnosing COVID-19 cases in India, we discuss the feasibility of using the pooling algorithms and possible avenues to improve their effectiveness.

Suggested Citation

  • Pritha Guha, 2022. "Application of Pooled Testing Methodologies in Tackling the COVID-19 Pandemic," Management and Labour Studies, XLRI Jamshedpur, School of Business Management & Human Resources, vol. 47(1), pages 7-21, February.
    • Handle: RePEc:sae:manlab:v:47:y:2022:i:1:p:7-21
    DOI: 10.1177/0258042X211018601
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    1. S. Vansteelandt & E. Goetghebeur & T. Verstraeten, 2000. "Regression Models for Disease Prevalence with Diagnostic Tests on Pools of Serum Samples," Biometrics, The International Biometric Society, vol. 56(4), pages 1126-1133, December.
    2. Lawrence M. Wein & Stefanos A. Zenios, 1996. "Pooled Testing for HIV Screening: Capturing the Dilution Effect," Operations Research, INFORMS, vol. 44(4), pages 543-569, August.
    3. Koen B. Pouwels & Laurence S. J. Roope & Adrian Barnett & David J. Hunter & Terry M. Nolan & Philip M. Clarke, 2020. "Group Testing for SARS-CoV-2: Forward to the Past?," PharmacoEconomics - Open, Springer, vol. 4(2), pages 207-210, June.
    4. Christopher S. McMahan & Joshua M. Tebbs & Christopher R. Bilder, 2012. "Informative Dorfman Screening," Biometrics, The International Biometric Society, vol. 68(1), pages 287-296, March.
    5. Hrayer Aprahamian & Douglas R. Bish & Ebru K. Bish, 2019. "Optimal Risk-Based Group Testing," Management Science, INFORMS, vol. 65(9), pages 4365-4384, September.
    6. Smriti Mallapaty, 2020. "The mathematical strategy that could transform coronavirus testing," Nature, Nature, vol. 583(7817), pages 504-505, July.
    7. Leon Mutesa & Pacifique Ndishimye & Yvan Butera & Jacob Souopgui & Annette Uwineza & Robert Rutayisire & Ella Larissa Ndoricimpaye & Emile Musoni & Nadine Rujeni & Thierry Nyatanyi & Edouard Ntagwabir, 2021. "A pooled testing strategy for identifying SARS-CoV-2 at low prevalence," Nature, Nature, vol. 589(7841), pages 276-280, January.
    8. Wang, Dewei & McMahan, Christopher S. & Tebbs, Joshua M. & Bilder, Christopher R., 2018. "Group testing case identification with biomarker information," Computational Statistics & Data Analysis, Elsevier, vol. 122(C), pages 156-166.
    9. Michael S. Black & Christopher R. Bilder & Joshua M. Tebbs, 2012. "Group testing in heterogeneous populations by using halving algorithms," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 61(2), pages 277-290, March.
    10. 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.
    11. H. M. Finucan, 1964. "The Blood Testing Problem," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 13(1), pages 43-50, March.
    12. Priyanka Pulla, 2020. "‘The epidemic is growing very rapidly’: Indian government adviser fears coronavirus crisis will worsen," Nature, Nature, vol. 583(7815), pages 180-180, July.
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