Full Citation

Title: Addressing the socioeconomic divide in computational modeling for infectious diseases

Citation Type: Journal Article

Publication Year: 2022

ISBN:

ISSN: 2041-1723

DOI: 10.1038/s41467-022-30688-8

NSFID:

PMCID:

PMID: 35610237

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. The COVID-19 pandemic has highlighted how structural social inequities fundamentally shape disease dynamics. Here, the authors provide a set of practical and methodological recommendations to address socioeconomic vulnerabilities in epidemic models.

Url: https://www.nature.com/articles/s41467-022-30688-8

User Submitted?: No

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

Periodical (Full): Nature Communications

Issue: 1

Volume: 13

Pages: 1-7

Data Collections: IPUMS IHGIS

Topics: Health, Methodology and Data Collection, Population Health and Health Systems

Countries: