ArticleEPJ data science2025
Socioeconomic disparities in mobility behavior during the COVID-19 pandemic in developing countries.
Article in EPJ data science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Assessing the usefulness of digital contact tracing using real-world contact data.Scientific reports · 2026Article
- Evaluating the reach and equity of public spaces in Mexico City using mobility data: the case of the Utopías in Iztapalapa.Frontiers in big data · 2026Article
- Article
- Human mobility under disasters: a systematic review and framework for equitable and resilient mobility governance.npj natural hazards · 2025Article
- Temporal clustering of social interactions trades-off disease spreading and knowledge diffusion.Journal of the Royal Society, Interface · 2024Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
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