Addressing the socioeconomic divide in computational modeling for infectious diseases

Michele Tizzoni (), Elaine O. Nsoesie, Laetitia Gauvin, Márton Karsai, Nicola Perra and Shweta Bansal
Additional contact information
Michele Tizzoni: ISI Foundation
Elaine O. Nsoesie: Boston University
Laetitia Gauvin: ISI Foundation
Márton Karsai: Central European University
Nicola Perra: Queen Mary University of London
Shweta Bansal: Georgetown University

Nature Communications, 2022, vol. 13, issue 1, 1-7

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.

Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.nature.com/articles/s41467-022-30688-8 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-30688-8

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-022-30688-8

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().