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Addressing the socioeconomic divide in computational modeling for infectious diseases
Tizzoni, Michele Nsoesie, Elaine O. Gauvin, Laetitia Karsai, Márton Perra, Nicola Bansal, Shweta
Tizzoni, Michele
Nsoesie, Elaine O.
Gauvin, Laetitia
Karsai, Márton
Perra, Nicola
Bansal, Shweta
Title / Series / Name
Nature Communications
Publication Volume
13
Publication Issue
1
Pages
Editors
Keywords
General Chemistry
General Biochemistry,Genetics and Molecular Biology
Multidisciplinary
General Physics and Astronomy
SDG 3 - Good Health and Well-being
General Biochemistry,Genetics and Molecular Biology
Multidisciplinary
General Physics and Astronomy
SDG 3 - Good Health and Well-being
Files
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Karsai-Marton_2022.pdf
Adobe PDF, 702.58 KB
URI
https://hdl.handle.net/20.500.14018/28906
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.
Topic
Publisher
Place of Publication
Type
Journal article
Date
2022-05-24
Language
ISBN
Identifiers
10.1038/s41467-022-30688-8