Addressing the socioeconomic divide in computational modeling for infectious diseases

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 se...

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Autores principales: Tizzoni, Michele, Nsoesie, Elaine O., Gauvin, Laetitia, Karsai, Márton, Perra, Nicola, Bansal, Shweta
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Comment
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9130127/
https://www.ncbi.nlm.nih.gov/pubmed/35610237
http://dx.doi.org/10.1038/s41467-022-30688-8
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author Tizzoni, Michele
Nsoesie, Elaine O.
Gauvin, Laetitia
Karsai, Márton
Perra, Nicola
Bansal, Shweta
author_facet Tizzoni, Michele
Nsoesie, Elaine O.
Gauvin, Laetitia
Karsai, Márton
Perra, Nicola
Bansal, Shweta
author_sort Tizzoni, Michele
collection PubMed
description 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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spelling pubmed-91301272022-05-26 Addressing the socioeconomic divide in computational modeling for infectious diseases Tizzoni, Michele Nsoesie, Elaine O. Gauvin, Laetitia Karsai, Márton Perra, Nicola Bansal, Shweta Nat Commun Comment 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. Nature Publishing Group UK 2022-05-24 /pmc/articles/PMC9130127/ /pubmed/35610237 http://dx.doi.org/10.1038/s41467-022-30688-8 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Comment
Tizzoni, Michele
Nsoesie, Elaine O.
Gauvin, Laetitia
Karsai, Márton
Perra, Nicola
Bansal, Shweta
Addressing the socioeconomic divide in computational modeling for infectious diseases
title Addressing the socioeconomic divide in computational modeling for infectious diseases
title_full Addressing the socioeconomic divide in computational modeling for infectious diseases
title_fullStr Addressing the socioeconomic divide in computational modeling for infectious diseases
title_full_unstemmed Addressing the socioeconomic divide in computational modeling for infectious diseases
title_short Addressing the socioeconomic divide in computational modeling for infectious diseases
title_sort addressing the socioeconomic divide in computational modeling for infectious diseases
topic Comment
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9130127/
https://www.ncbi.nlm.nih.gov/pubmed/35610237
http://dx.doi.org/10.1038/s41467-022-30688-8
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