Socioeconomic disparities in mobility behavior during the COVID-19 pandemic in developing countries.

IF 2.5 2区 计算机科学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
EPJ Data Science Pub Date : 2025-01-01 Epub Date: 2025-03-24 DOI:10.1140/epjds/s13688-025-00532-2
Lorenzo Lucchini, Ollin D Langle-Chimal, Lorenzo Candeago, Lucio Melito, Alex Chunet, Aleister Montfort, Bruno Lepri, Nancy Lozano-Gracia, Samuel P Fraiberger
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引用次数: 0

Abstract

Mobile phone data have played a key role in quantifying human mobility during the COVID-19 pandemic. Existing studies on mobility patterns have primarily focused on regional aggregates in high-income countries, obfuscating the accentuated impact of the pandemic on the most vulnerable populations. Leveraging geolocation data from mobile-phone users and population census for 6 middle-income countries across 3 continents between March and December 2020, we uncovered common disparities in the behavioral response to the pandemic across socioeconomic groups. Users living in low-wealth neighborhoods were less likely to respond by self-isolating, relocating to rural areas, or refraining from commuting to work. The gap in the behavioral responses between socioeconomic groups persisted during the entire observation period. Among users living in low-wealth neighborhoods, those who commute to work in high-wealth neighborhoods pre-pandemic were particularly at risk of experiencing economic stress, facing both the reduction in economic activity in the high-wealth neighborhood and being more likely to be affected by public transport closures due to their longer commute distances. While confinement policies were predominantly country-wide, these results suggest that, when data to identify vulnerable individuals are not readily available, GPS-based analytics could help design targeted place-based policies to aid the most vulnerable.

Supplementary information: The online version contains supplementary material available at 10.1140/epjds/s13688-025-00532-2.

COVID-19大流行期间发展中国家流动行为的社会经济差异。
在2019冠状病毒病大流行期间,手机数据在量化人类流动性方面发挥了关键作用。现有的关于人口流动模式的研究主要集中在高收入国家的区域总量上,混淆了疫情对最脆弱人群的严重影响。利用2020年3月至12月期间来自三大洲6个中等收入国家的移动电话用户地理位置数据和人口普查数据,我们发现了不同社会经济群体对大流行的行为反应的共同差异。生活在低财富社区的用户不太可能通过自我隔离、搬迁到农村地区或不上下班来应对。在整个观察期间,社会经济群体之间的行为反应差距持续存在。在生活在低财富社区的用户中,那些在大流行前在高财富社区上班的人特别容易遭受经济压力,既面临高财富社区经济活动的减少,又更有可能受到公共交通关闭的影响,因为他们的通勤距离更长。虽然限制政策主要是在全国范围内实施的,但这些结果表明,当识别弱势群体的数据不容易获得时,基于gps的分析可以帮助设计有针对性的基于地方的政策,以帮助最弱势群体。补充信息:在线版本包含补充资料,可在10.1140/epjds/s13688-025-00532-2获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
EPJ Data Science
EPJ Data Science MATHEMATICS, INTERDISCIPLINARY APPLICATIONS -
CiteScore
6.10
自引率
5.60%
发文量
53
审稿时长
13 weeks
期刊介绍: EPJ Data Science covers a broad range of research areas and applications and particularly encourages contributions from techno-socio-economic systems, where it comprises those research lines that now regard the digital “tracks” of human beings as first-order objects for scientific investigation. Topics include, but are not limited to, human behavior, social interaction (including animal societies), economic and financial systems, management and business networks, socio-technical infrastructure, health and environmental systems, the science of science, as well as general risk and crisis scenario forecasting up to and including policy advice.
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