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Impact of socioeconomic determinants on the speed of epidemic diseases: a comparative analysis

Author

Listed:
  • Gilles Dufrénot

    (AMSE - Aix-Marseille Sciences Economiques - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique, CEPII - Centre d'Etudes Prospectives et d'Informations Internationales - Centre d'analyse stratégique)

  • Ewen Gallic

    (AMSE - Aix-Marseille Sciences Economiques - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique)

  • Pierre Michel

    (LTD - Laboratoire de théorie du droit - AMU - Aix Marseille Université)

  • Norgile Midopkè Bonou

    (AMSE - Aix-Marseille Sciences Economiques - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique)

  • Ségui Gnaba

    (AMSE - Aix-Marseille Sciences Economiques - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique)

  • Iness Slaoui

    (PSE - Paris School of Economics - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - EHESS - École des hautes études en sciences sociales - ENPC - École nationale des ponts et chaussées - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement)

Abstract

We study the impact of socioeconomic factors on two key parameters of epidemic dynamics. Specifically, we investigate a parameter capturing the rate of deceleration at the very start of an epidemic, and a parameter that reflects the pre-peak and post-peak dynamics at the turning point of an epidemic like coronavirus disease 2019 (COVID-19). We find two important results. The policies to fight COVID-19 (such as social distancing and containment) have been effective in reducing the overall number of new infections, because they influence not only the epidemic peaks, but also the speed of spread of the disease in its early stages. The second important result of our research concerns the role of healthcare infrastructure. They are just as effective as anti-COVID policies, not only in preventing an epidemic from spreading too quickly at the outset, but also in creating the desired dynamic around peaks: slow spreading, then rapid disappearance.

Suggested Citation

  • Gilles Dufrénot & Ewen Gallic & Pierre Michel & Norgile Midopkè Bonou & Ségui Gnaba & Iness Slaoui, 2024. "Impact of socioeconomic determinants on the speed of epidemic diseases: a comparative analysis," Post-Print hal-04538161, HAL.
    • Handle: RePEc:hal:journl:hal-04538161
    DOI: 10.1093/oep/gpae003
    Note: View the original document on HAL open archive server: https://hal.science/hal-04538161v1
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    References listed on IDEAS

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    1. Cannonier, Colin & Burke, Monica Galloway, 2025. "Trust during troubled times: Evidence from Sierra Leone's Ebola epidemic," Economic Modelling, Elsevier, vol. 144(C).

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    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • I12 - Health, Education, and Welfare - - Health - - - Health Behavior
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health

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