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Addressing the socioeconomic divide in computational modeling for infectious diseases

Author

Listed:
  • Michele Tizzoni

    (ISI Foundation)

  • Elaine O. Nsoesie

    (Boston University
    Boston University)

  • Laetitia Gauvin

    (ISI Foundation)

  • Márton Karsai

    (Central European University
    Alfréd Rényi Institute of Mathematics)

  • Nicola Perra

    (Queen Mary University of London)

  • Shweta Bansal

    (Georgetown University)

Abstract

The COVID-19 pandemic has highlighted how structural social inequities fundamentally shape disease dynamics, yet these concepts are often at the margins of the computational modeling community. Building on recent research studies in the area of digital and computational epidemiology, we provide a set of practical and methodological recommendations to address socioeconomic vulnerabilities in epidemic models.

Suggested Citation

  • Michele Tizzoni & Elaine O. Nsoesie & Laetitia Gauvin & Márton Karsai & Nicola Perra & Shweta Bansal, 2022. "Addressing the socioeconomic divide in computational modeling for infectious diseases," Nature Communications, Nature, vol. 13(1), pages 1-7, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-30688-8
    DOI: 10.1038/s41467-022-30688-8
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    References listed on IDEAS

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    Cited by:

    1. Marco Pangallo & Alberto Aleta & R. Maria del Rio-Chanona & Anton Pichler & David Martín-Corral & Matteo Chinazzi & François Lafond & Marco Ajelli & Esteban Moro & Yamir Moreno & Alessandro Vespignani, 2024. "The unequal effects of the health–economy trade-off during the COVID-19 pandemic," Nature Human Behaviour, Nature, vol. 8(2), pages 264-275, February.
    2. Nicolò Gozzi & Matteo Chinazzi & Natalie E. Dean & Ira M. Longini Jr & M. Elizabeth Halloran & Nicola Perra & Alessandro Vespignani, 2023. "Estimating the impact of COVID-19 vaccine inequities: a modeling study," Nature Communications, Nature, vol. 14(1), pages 1-10, December.

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