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AI, Epidemiology and Public Health in the Covid Pandemic

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Artificial Intelligence in Covid-19

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Abstract

Epidemiology and public health are two closely related disciplines, and both rely in part on the quantification, the measurement of given characteristics—whether for the purpose of describing a health problem, researching the causes of this problem, or more broadly, any data and knowledge that may be useful for decision-making in the area of population health. The development of digital technology in all aspects of our lives and activities means that new sources of data are becoming available, presenting new or complementary qualities to those that could previously be collected in epidemiology. The processing capabilities of this data have also evolved hand in hand, and the resurgence of artificial intelligence (AI) techniques, in the broad sense, appears to be an opportunity to be explored for both epidemiologists and public health actors. The Covid19 pandemic has arisen as a textbook case addressed to epidemiology and public health, and for which it seems that any means were good to try. It is therefore both an opportunity to examine the practical application of these two disciplines in its classic aspects, and the proposals for the use of artificial intelligence in this context. The main question is then to know what types of use could have been suggested or employed. A targeted bibliographic search highlights six main types of uses of AI in epidemiology and public health in the context of the pandemic, as well as a seventh which appears more specific to the digital age: infodemics. We propose to examine to what extent these proposals have been reported in a reliable and documented manner, thus making it possible to assess the performance of the algorithms developed, as well as their degree of maturity. We conclude by placing the question of AI in epidemiology and public health in a broader context, to outline the main issues for the future.

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Authors and Affiliations

  1. IRIS Institut de Recherche Interdisciplinaire sur les enjeux Sociaux, UMR8156 CNRS - U997 Inserm - EHESS—Université Sorbonne Paris Nord, Villetaneuse, France

    Thomas Lefèvre

  2. AP-HP, Jean Verdier hospital, Department of forensic and social medicine, Bondy, France

    Thomas Lefèvre

  3. CERPOP UMR 1295, Université Toulouse III Paul Sabatier, INSERM, Toulouse, France

    Hélène Colineaux & Cyrille Delpierre

  4. Centre for Epidemiology on the Medical Causes of Death, Inserm, Le Kremlin-Bicêtre, France

    Claire Morgand

  5. Data and Health Studies Department, Regional Health Agency, Saint-Denis, France

    Claire Morgand

  6. BioSilicium, Riom, France

    Laurent Tournois

  7. CNRS UMR 8045 BABEL, Université Paris Cité, Paris, France

    Laurent Tournois

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    Niklas Lidströmer

  2. Weizmann Institute of Science, Rehovot, Israel

    Yonina C. Eldar

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Lefèvre, T., Colineaux, H., Morgand, C., Tournois, L., Delpierre, C. (2022). AI, Epidemiology and Public Health in the Covid Pandemic. In: Lidströmer, N., Eldar, Y.C. (eds) Artificial Intelligence in Covid-19. Springer, Cham. https://doi.org/10.1007/978-3-031-08506-2_13

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  • Print ISBN: 978-3-031-08505-5

  • Online ISBN: 978-3-031-08506-2

  • eBook Packages: MedicineMedicine (R0)

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