ReviewJournal of travel medicine2019
Measuring mobility, disease connectivity and individual risk: a review of using mobile phone data and mHealth for travel medicine.
Review in Journal of travel medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 1 of them a synthesis 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
40 citing papers in PubMed, 1 synthesis or guideline pooled it, 100 citations in OpenAlex.
- Pooled it
- 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
- Mobility data resolution needed to inform predictive models of spatial epidemic spread from mobile phone data.PLoS computational biology · 2026Article
- From Concept to Implementation: A Policy and Standards Roadmap for a Global Medical Record System for Travelers with Chronic Disease.Clinical epidemiology · 2026Article
- Review
- The limitations of mobile phone data for measuring movement patterns of populations at risk of malaria.Malaria journal · 2025Observational
- Mapping seasonal human mobility across Africa using mobile phone location history and geospatial data.Research square · 2025Article
- Review
- Technological trends in epidemic intelligence for infectious disease surveillance: a systematic literature review.PeerJ. Computer science · 2025Article
- Correlation between notifiable infectious diseases and transportation passenger traffic from 2013 to 2019 in mainland China.BMC public health · 2024Article
- Knowledge, attitudes and practices regarding the use of mobile travel health apps.Journal of travel medicine · 2024Article
- Factors Associated with Non-Adherence to Treatment Among Migrants with MDR-TB in Wuhan, China: A Cross-Sectional Study.Risk management and healthcare policy · 2024Article
- Human-centred artificial intelligence for mobile health sensing: challenges and opportunities.Royal Society open science · 2023Review
- Public Health Importance of Emporiatrics: A Review.Cureus · 2023Review
- Mobile Phone Data: A Survey of Techniques, Features, and Applications.Sensors (Basel, Switzerland) · 2023Review
- Virtual Access to Subspecialty Care.Primary care · 2022Review
- Lack of ownership of mobile phones could hinder the rollout of mHealth interventions in Africa.eLife · 2022Article
- Using social media listening and data mining to understand travellers' perspectives on travel disease risks and vaccine-related attitudes and behaviours.Journal of travel medicine · 2022Article
- Generating mobility networks with generative adversarial networks.EPJ data science · 2022Article
- How effective has the Spanish lockdown been to battle COVID-19? A spatial analysis of the coronavirus propagation across provinces.Health economics · 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 3 institutions in 4 countries.
Funding
Abstract
rationale for reviewThe increasing mobility of populations allows pathogens to move rapidly and far, making endemic or epidemic regions more connected to the rest of the world than at any time in history. However, the ability to measure and monitor human mobility, health risk and their changing patterns across spatial and temporal scales using traditional data sources has been limited. To facilitate a better understanding of the use of emerging mobile phone technology and data in travel medicine, we reviewed relevant work aiming at measuring human mobility, disease connectivity and health risk in travellers using mobile geopositioning data. KEY
findingsDespite some inherent biases of mobile phone data, analysing anonymized positions from mobile users could precisely quantify the dynamical processes associated with contemporary human movements and connectivity of infectious diseases at multiple temporal and spatial scales. Moreover, recent progress in mobile health (mHealth) technology and applications, integrating with mobile positioning data, shows great potential for innovation in travel medicine to monitor and assess real-time health risk for individuals during travel.
conclusionsMobile phones and mHealth have become a novel and tremendously powerful source of information on measuring human movements and origin-destination-specific risks of infectious and non-infectious health issues. The high penetration rate of mobile phones across the globe provides an unprecedented opportunity to quantify human mobility and accurately estimate the health risks in travellers. Continued efforts are needed to establish the most promising uses of these data and technologies for travel health.
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.