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Group Testing against COVID-19


  • Christian Gollier

    (Toulouse School of Economics, University of Toulouse-Capitole)

  • Olivier Gossner

    (CNRS – CREST, Ecole polytechnique, and London School of Economics)


It is well-known that group testing is an e cient strategy to screen for the presence of a virus. It consists in 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 optimized 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 20. The implementation of this strategy in the short run requires limited investments and could bypass the current immense shortage of testing capacity.

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  • Christian Gollier & Olivier Gossner, 2020. "Group Testing against COVID-19," Working Papers 2020-04, Center for Research in Economics and Statistics, revised 02 Apr 2020.
  • Handle: RePEc:crs:wpaper:2020-04

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    References listed on IDEAS

    1. Thunström, Linda & Newbold, Stephen C. & Finnoff, David & Ashworth, Madison & Shogren, Jason F., 2020. "The Benefits and Costs of Using Social Distancing to Flatten the Curve for COVID-19," Journal of Benefit-Cost Analysis, Cambridge University Press, vol. 11(2), pages 179-195, July.
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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. Lipnowski, Elliot & Ravid, Doron, 2021. "Pooled testing for quarantine decisions," Journal of Economic Theory, Elsevier, vol. 198(C).
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. Ely, Jeffrey & Galeotti, Andrea & Jann, Ole & Steiner, Jakub, 2021. "Optimal test allocation," Journal of Economic Theory, Elsevier, vol. 193(C).
    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. 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.
    12. 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.
    13. 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.
    14. Eslami, Keyvan & Lee, Hyunju, 2024. "Overreaction and the value of information in a pandemic," European Economic Review, Elsevier, vol. 161(C).
    15. 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.

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