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Group testing against Covid-19

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  • Christian Gollier

    (TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - Comue de Toulouse - Communauté d'universités et établissements de Toulouse - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement)

  • Olivier Gossner

    (CREST - Centre de Recherche en Économie et Statistique - ENSAI - Ecole Nationale de la Statistique et de l'Analyse de l'Information [Bruz] - Groupe ENSAE-ENSAI - Groupe des Écoles Nationales d'Économie et Statistique - X - École polytechnique - IP Paris - Institut Polytechnique de Paris - ENSAE Paris - École Nationale de la Statistique et de l'Administration Économique - Groupe ENSAE-ENSAI - Groupe des Écoles Nationales d'Économie et Statistique - IP Paris - Institut Polytechnique de Paris - CNRS - Centre National de la Recherche Scientifique)

Abstract

It is well-known that group testing is an efficient strategy to screen for the presence of a virus. It consists of pooling n individual samples with a single test using RT-PCR. If no individual in the group is infected, the group test is negative. Thus, a single test may reveal this crucial information. We show how group testing can be optimised in three applications to multiply the power of tests against Covid-19: Estimating virus prevalence to measure the evolution of the pandemic, bringing negative groups back to work to exit the current lockdown, and testing for individual infectious status to treat sick people. For an infection level around 2%, group testing could multiply the power of testing by a factor of 20. The implementation of this strategy in the short run requires limited investments and could bypass the current immense shortage of testing capacity.

Suggested Citation

  • Christian Gollier & Olivier Gossner, 2020. "Group testing against Covid-19," Post-Print hal-02550740, HAL.
    • Handle: RePEc:hal:journl:hal-02550740
    Note: View the original document on HAL open archive server: https://hal.science/hal-02550740v1
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    RePEc Biblio mentions

    As found on the RePEc Biblio, the curated bibliography for Economics:
    1. > Economics of Welfare > Health Economics > Economics of Pandemics > Specific pandemics > Covid-19 > Health > Testing

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    Cited by:

    1. Christian Gollier, 2020. "Cost–benefit analysis of age‐specific deconfinement strategies," Journal of Public Economic Theory, Association for Public Economic Theory, vol. 22(6), pages 1746-1771, December.
    2. 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.
    3. C. Baunez & Mickael Degoulet & Stéphane Luchini & Patrick Pintus & Miriam Teschl, 2020. "Sub-National Allocation of COVID-19 Tests: An Efficiency Criterion with an Application to Italian Regions," Post-Print hal-03140005, HAL.
    4. Jiayi Lin & Hrayer Aprahamian & George Golovko, 2024. "An optimization framework for large-scale screening under limited testing capacity with application to COVID-19," Health Care Management Science, Springer, vol. 27(2), pages 223-238, June.
    5. Rahul Deb & Mallesh Pai & Akhil Vohra & Rakesh Vohra, 2022. "Testing alone is insufficient," Review of Economic Design, Springer;Society for Economic Design, vol. 26(1), pages 1-21, March.
    6. Nina Bobkova & Ying Chen & Hülya Eraslan, 2024. "Optimal group testing with heterogeneous risks," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 77(1), pages 413-444, February.
    7. Karolina Drela & Agnieszka Malkowska & Anna Bera & Anna Tokarz-Kocik, 2021. "Instruments for Managing the EU Labour Market in the Face of the COVID-19 Crisis," European Research Studies Journal, European Research Studies Journal, vol. 0(1), pages 984-998.
    8. Lipnowski, Elliot & Ravid, Doron, 2021. "Pooled testing for quarantine decisions," Journal of Economic Theory, Elsevier, vol. 198(C).
    9. Abel Brodeur & David Gray & Anik Islam & Suraiya Bhuiyan, 2021. "A literature review of the economics of COVID‐19," Journal of Economic Surveys, Wiley Blackwell, vol. 35(4), pages 1007-1044, September.
    10. Jean-Noël Barrot & Basile Grassi & Julien Sauvagnat, 2020. "Estimating the Costs and Benefits of Mandated Business Closures in a Pandemic," Working Papers hal-02896739, HAL.
    11. Lu Tang & Yiwang Zhou & Lili Wang & Soumik Purkayastha & Leyao Zhang & Jie He & Fei Wang & Peter X.‐K. Song, 2020. "A Review of Multi‐Compartment Infectious Disease Models," International Statistical Review, International Statistical Institute, vol. 88(2), pages 462-513, August.
    12. Hanno Beck & Aloys Prinz & Elmar Wolfstetter, 2022. "Vaccination Gap, Vaccination Fraud and Inefficient Testing," Intereconomics: Review of European Economic Policy, Springer;ZBW - Leibniz Information Centre for Economics;Centre for European Policy Studies (CEPS), vol. 57(5), pages 330-336, September.
    13. Ely, Jeffrey & Galeotti, Andrea & Jann, Ole & Steiner, Jakub, 2021. "Optimal test allocation," Journal of Economic Theory, Elsevier, vol. 193(C).
    14. Vincent Brault & Bastien Mallein & Jean-François Rupprecht, 2021. "Group testing as a strategy for COVID-19 epidemiological monitoring and community surveillance," PLOS Computational Biology, Public Library of Science, vol. 17(3), pages 1-25, March.
    15. Eslami, Keyvan & Lee, Hyunju, 2024. "Overreaction and the value of information in a pandemic," European Economic Review, Elsevier, vol. 161(C).
    16. Tarun Jain & Bijendra Nath Jain, 2021. "Infection Testing at Scale: An Examination of Pooled Testing Diagnostics," Vikalpa: The Journal for Decision Makers, , vol. 46(1), pages 13-26, March.
    17. Long Hai Vo & Thai‐Ha Le, 2022. "COVID‐19 test‐kit trade and trade policy: Implications for developing countries," The World Economy, Wiley Blackwell, vol. 45(10), pages 3246-3268, October.

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