ArticleNature computational science2021
Insights into population behavior during the COVID-19 pandemic from cell phone mobility data and manifold learning.
Article in Nature computational science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 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
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Who cites it
26 citing papers in PubMed, 58 citations in OpenAlex.
- Public announcements and mask use during COVID-19 bridging regression and machine learning.Scientific reports · 2026Article
- The persistence of behavioral disparities post-pandemic: Insights from activity time series data.Scientific reports · 2026Article
- A graph-theoretic framework for integrating mobility data into mathematical epidemic models.Infectious Disease Modelling · 2025Article
- Embedding risk monitoring in infectious disease surveillance for timely and effective outbreak prevention and control.BMJ global health · 2025Review
- Mobile phone data analyses for public health research: a scoping review.Frontiers in public health · 2025Article
- Article
- Mobility census for monitoring rapid urban development.Journal of the Royal Society, Interface · 2024Article
- Analysis of the relationship between urban dynamics and prevalence of remote work based on population data generated from cellular networks.Scientific reports · 2023Article
- The limits of human mobility traces to predict the spread of COVID-19: A transfer entropy approach.PNAS nexus · 2023Article
- Differences in COVID-19 cyclicity and predictability among U.S. counties and states reflect the effectiveness of protective measures.Scientific reports · 2023Article
- A global perspective on the intrinsic dimensionality of COVID-19 data.Scientific reports · 2023Article
- Effects of non-compulsory and mandatory COVID-19 interventions on travel distance and time away from home, Norway, 2021.Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin · 2023Article
- Predicting COVID-19 positivity and hospitalization with multi-scale graph neural networks.Scientific reports · 2023Article
- Adaptive behaviors and vaccination on curbing COVID-19 transmission: Modeling simulations in eight countries.Journal of theoretical biology · 2023Article
- A global portrait of expressed mental health signals towards COVID-19 in social media space.International journal of applied earth observation and geoinformation : ITC journal · 2023Article
- Phase-wise evaluation and optimization of non-pharmaceutical interventions to contain the COVID-19 pandemic in the U.S.Frontiers in public health · 2023Article
- Changes in tourist mobility after COVID-19 outbreaks.Annals of tourism research · 2023Article
- Article
- Variability in Opioid-Related Drug Overdoses, Social Distancing, and Area-Level Deprivation during the COVID-19 Pandemic: a Bayesian Spatiotemporal Analysis.Journal of urban health : bulletin of the New York Academy of Medicine · 2022Article
- Influence of trip distance and population density on intra-city mobility patterns in Tokyo during COVID-19 pandemic.PloS one · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Understanding the complex interplay between human behavior, disease transmission and non-pharmaceutical interventions during the COVID-19 pandemic could provide valuable insights with which to focus future public health efforts. Cell phone mobility data offer a modern measurement instrument to investigate human mobility and behavior at an unprecedented scale. We investigate aggregated and anonymized mobility data, which measure how populations at the census-block-group geographic scale stayed at home in California, Georgia, Texas and Washington from the beginning of the pandemic. Using manifold learning techniques, we show that a low-dimensional embedding enables the identification of patterns of mobility behavior that align with stay-at-home orders, correlate with socioeconomic factors, cluster geographically, reveal subpopulations that probably migrated out of urban areas and, importantly, link to COVID-19 case counts. The analysis and approach provide local epidemiologists a framework for interpreting mobility data and behavior to inform policy makers' decision-making aimed at curbing the spread of COVID-19.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.