ReviewNature communications2020
The use of mobile phone data to inform analysis of COVID-19 pandemic epidemiology.
Review in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 162 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
162 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Pooled it
- A systematic review of using population-level human mobility data to understand SARS-CoV-2 transmission.Nature communications · 2024Pooled it
- Miller Fisher syndrome associated with COVID-19: an up-to-date systematic review.Environmental science and pollution research international · 2021Pooled it
- Human mobility acts as a lever for decarbonising wastewater treatment plants.Environmental science and ecotechnology · 2026Article
- Characterizing the impact of incorporating spatially aggregated human mobility data into infectious disease models.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- AI-Driven Approaches for the Detection, Classification, and Surveillance of Viral Pathogens: Current Advances, Challenges, and Future Directions.Pathogens (Basel, Switzerland) · 2026Review
- Mobility data resolution needed to inform predictive models of spatial epidemic spread from mobile phone data.PLoS computational biology · 2026Article
- An exploratory study of patient hospitalization patterns and behavioral risk factors using mobile phone location data.PLOS digital health · 2026Article
- Simulating population compliance with pandemic interventions using large language models.medRxiv : the preprint server for health sciences · 2026Article
- Article
- Inferring mobility reductions from COVID-19 disease spread along the urban-rural gradient.Frontiers in public health · 2026Article
- Comparison of contact diaries and wearable proximity sensors in measuring household contacts in low- and middle-income countries.medRxiv : the preprint server for health sciences · 2025Article
- Characterizing population-level changes in human behavior during the COVID-19 pandemic in the United States.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Assessing the impact of interregional mobility on COVID19 spread in Spain using transfer entropy.Scientific reports · 2025Article
- Revealing spatiotemporal variations in areas potentially linked to COVID-19 spread using fine-grained population data.Scientific reports · 2025Article
- Inferring the timing of individual mobility decisions from accommodation reservation data during the COVID-19 outbreak.Royal Society open science · 2025Article
- A graph-theoretic framework for integrating mobility data into mathematical epidemic models.Infectious Disease Modelling · 2025Article
- Aptamer-engaged nanotherapeutics against SARS-CoV-2.Discover nano · 2025Review
- Characterizing US Spatial Connectivity and Implications for Geographical Disease Dynamics and Metapopulation Modeling: Longitudinal Observational Study.JMIR public health and surveillance · 2025Observational
- Critical mobility in policy making for epidemic containment.Scientific reports · 2025Article
102 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
12 authors.
Funding
Abstract
The ongoing coronavirus disease 2019 (COVID-19) pandemic has heightened discussion of the use of mobile phone data in outbreak response. Mobile phone data have been proposed to monitor effectiveness of non-pharmaceutical interventions, to assess potential drivers of spatiotemporal spread, and to support contact tracing efforts. While these data may be an important part of COVID-19 response, their use must be considered alongside a careful understanding of the behaviors and populations they capture. Here, we review the different applications for mobile phone data in guiding and evaluating COVID-19 response, the relevance of these applications for infectious disease transmission and control, and potential sources and implications of selection bias in mobile phone data. We also discuss best practices and potential pitfalls for directly integrating the collection, analysis, and interpretation of these data into public health decision making.
Indexed as
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.