Addressing the socioeconomic divide in computational modeling for infectious diseases

被引：21

作者：

Tizzoni, Michele ^{[1
]}

Nsoesie, Elaine O. ^{[2
,3
]}

Gauvin, Laetitia ^{[1
]}

Karsai, Marton ^{[4
,5
]}

Perra, Nicola ^{[6
]}

Bansal, Shweta ^{[7
]}

机构：

[1] ISI Fdn, Turin, Italy

[2] Boston Univ, Sch Publ Hlth, Dept Global Hlth, Boston, MA USA

[3] Boston Univ, Ctr Antiracist Res, Boston, MA USA

[4] Cent European Univ, Dept Network & Data Sci, A-1100 Vienna, Austria

[5] Alfred Renyi Inst Math, H-1053 Budapest, Hungary

[6] Queen Mary Univ London, Sch Math Sci, London, England

[7] Georgetown Univ, Dept Biol, Washington, DC 20057 USA

来源：

NATURE COMMUNICATIONS | 2022年 / 13卷 / 01期

基金：

欧盟地平线“2020”; 美国国家卫生研究院;

关键词：

HEALTH; INEQUALITIES; DISPARITIES; INFLUENZA;

D O I：

10.1038/s41467-022-30688-8

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

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. 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.

引用

页数：7

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