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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:

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    2. Adriana Manna & Júlia Koltai & Márton Karsai, 2024. "Importance of social inequalities to contact patterns, vaccine uptake, and epidemic dynamics," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    3. 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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