Addressing the socioeconomic divide in computational modeling for infectious diseases

Michele Tizzoni; Elaine O. Nsoesie; Laetitia Gauvin; Márton Karsai; Nicola Perra; Shweta Bansal

doi:10.1038/s41467-022-30688-8

Addressing the socioeconomic divide in computational modeling for infectious diseases

Michele Tizzoni^*
, Elaine O. Nsoesie
, Laetitia Gauvin
, Márton Karsai
, Nicola Perra
, Shweta Bansal

^*Corresponding author for this work

Department of Network and Data Science

Institute for Scientific Interchange Foundation
Boston University
Alfréd Rényi Institute of Mathematics
Queen Mary University of London
Georgetown University

Research output: Contribution to journal › Comment/debate

Abstract (may include machine translation)

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.

Original language	English
Article number	2897
Number of pages	7
Journal	Nature Communications
Volume	13
Issue number	1
DOIs	https://doi.org/10.1038/s41467-022-30688-8
State	Published - 24 May 2022

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

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10.1038/s41467-022-30688-8License: CC BY

Karsai-Marton_2022Publisher version, 703 KBLicense: CC BY

Cite this

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AU - Perra, Nicola

AU - Bansal, Shweta

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AB - 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.

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Addressing the socioeconomic divide in computational modeling for infectious diseases

Abstract (may include machine translation)

UN SDGs

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