ArticleScientific data2020
Multiscale dynamic human mobility flow dataset in the U.S. during the COVID-19 epidemic.
Article in Scientific data, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 86 papers, 1 of them a synthesis that pooled it.
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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.
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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
86 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Spatio-Temporal COVID-19 Modeling: A Global Systematic Review of Data Integration, Equity, and Lessons for Pandemic Preparedness.International journal of environmental research and public health · 2026Pooled it
- Outfitting the quest for spatial spread of infections: A review of mobility datasets for population health modelling.Infectious Disease Modelling · 2026Review
- How host mobility formulations shape estimates of pathogen dispersal and epidemic risk in non endemic regions.PLoS computational biology · 2026Article
- Structural inequality shapes the daily rhythm of stationarity and mobility.Nature communications · 2026Article
- Risk and Spatial Spread of a Measles Outbreak in Texas.Viruses · 2026Article
- Fairness Correction in COVID-19 Predictive Models Using Demographic Optimization: Algorithm Development and Validation Study.Online journal of public health informatics · 2026Article
- Spatial and spatio-temporal county-level trends in COVID-19 mortality and emergency department visits in U.S. with R-INLA.Spatial and spatio-temporal epidemiology · 2026Article
- Reinforcement Learning-Based Control of Epidemics on Networks of Communities and Correctional Facilities.Medical decision making : an international journal of the Society for Medical Decision Making · 2026Article
- Network-Based Epidemic Control Through Optimal Travel and Quarantine Management.IEEE transactions on control of network systems · 2025Article
- Anonymised human location data in England for urban mobility research.Scientific data · 2025Article
- Predicting human mobility flows in cities using deep learning on satellite imagery.Nature communications · 2025Article
- Multilayer Network Analysis of European Regional Flows.Entropy (Basel, Switzerland) · 2025Article
- An LLM driven dataset on the spatiotemporal distributions of street and neighborhood crime in China.Scientific data · 2025Article
- An intercity investment network dataset of China based on the enterprise registration records (2000-2020).Scientific data · 2025Article
- High-resolution traffic flow data from the urban traffic control system in Glasgow.Scientific data · 2025Article
- Human mobility is well described by closed-form gravity-like models learned automatically from data.Nature communications · 2025Article
- A research agenda for GIScience in a time of disruptions.International journal of geographical information science : IJGIS · 2025Article
- Human mobility under disasters: a systematic review and framework for equitable and resilient mobility governance.npj natural hazards · 2025Article
- Variant patterns and influence of inter-regional travel during the SARS-CoV-2 expansion in South Africa.PloS one · 2025Article
- Rare and highly destructive wildfires drive human migration in the U.S.Nature communications · 2024Article
26 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Understanding dynamic human mobility changes and spatial interaction patterns at different geographic scales is crucial for assessing the impacts of non-pharmaceutical interventions (such as stay-at-home orders) during the COVID-19 pandemic. In this data descriptor, we introduce a regularly-updated multiscale dynamic human mobility flow dataset across the United States, with data starting from March 1st, 2020. By analysing millions of anonymous mobile phone users' visits to various places provided by SafeGraph, the daily and weekly dynamic origin-to-destination (O-D) population flows are computed, aggregated, and inferred at three geographic scales: census tract, county, and state. There is high correlation between our mobility flow dataset and openly available data sources, which shows the reliability of the produced data. Such a high spatiotemporal resolution human mobility flow dataset at different geographic scales over time may help monitor epidemic spreading dynamics, inform public health policy, and deepen our understanding of human behaviour changes under the unprecedented public health crisis. This up-to-date O-D flow open data can support many other social sensing and transportation applications.
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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.