ArticleRoyal Society open science2017
Assessing the use of mobile phone data to describe recurrent mobility patterns in spatial epidemic models.
Article in Royal Society open science, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- How host mobility formulations shape estimates of pathogen dispersal and epidemic risk in non endemic regions.PLoS computational biology · 2026Article
- Mobility data resolution needed to inform predictive models of spatial epidemic spread from mobile phone data.PLoS computational biology · 2026Article
- Modeling Intraday Aedes-human exposure dynamics enhances dengue risk prediction.Scientific reports · 2025Article
- Finding women in fishing communities around Lake Victoria: "Feasibility and acceptability of using phones and tracking devices".PloS one · 2024Article
- Bayesian spatial modelling of localised SARS-CoV-2 transmission through mobility networks across England.PLoS computational biology · 2023Article
- Call detail record aggregation methodology impacts infectious disease models informed by human mobility.PLoS computational biology · 2023Article
- A typology of Jordanian consumers after Covid-19: The rational, the suspicious, and the cautious consumer.The Journal of consumer affairs · 2022Article
- Early warning of COVID-19 hotspots using human mobility and web search query data.Computers, environment and urban systems · 2022Article
- Texting in a crisis-using SMS for information and emotional support during COVID-19: A mixed methods research study.Frontiers in sociology · 2022Article
- Analysis of international traveler mobility patterns in Tokyo to identify geographic foci of dengue fever risk.Theoretical biology & medical modelling · 2021Article
- Article
- A data driven agent-based model that recommends non-pharmaceutical interventions to suppress Coronavirus disease 2019 resurgence in megacities.Journal of the Royal Society, Interface · 2021Article
- Connectivity, reproduction number, and mobility interact to determine communities' epidemiological superspreader potential in a metapopulation network.PLoS computational biology · 2021Article
- Understanding mobility and sexual risk behaviour among women in fishing communities of Lake Victoria in East Africa: a qualitative study.BMC public health · 2020Article
- Time-aggregated mobile phone mobility data are sufficient for modelling influenza spread: the case of Bangladesh.Journal of the Royal Society, Interface · 2020Article
- COVID-19 Mobile Positioning Data Contact Tracing and Patient Privacy Regulations: Exploratory Search of Global Response Strategies and the Use of Digital Tools in Nigeria.JMIR mHealth and uHealth · 2020Article
- Entropy as a Measure of Attractiveness and Socioeconomic Complexity in Rio de Janeiro Metropolitan Area.Entropy (Basel, Switzerland) · 2020Article
- Using Mobile Phone Data for Emergency Management: a Systematic Literature Review.Information systems frontiers : a journal of research and innovation · 2020Article
- Infectious Diseases Spreading on an Adaptive Metapopulation Network.IEEE access : practical innovations, open solutions · 2020Article
- Intervention threshold for epidemic control in susceptible-infected-recovered metapopulation models.Physical review. E · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The recent availability of large-scale call detail record data has substantially improved our ability of quantifying human travel patterns with broad applications in epidemiology. Notwithstanding a number of successful case studies, previous works have shown that using different mobility data sources, such as mobile phone data or census surveys, to parametrize infectious disease models can generate divergent outcomes. Thus, it remains unclear to what extent epidemic modelling results may vary when using different proxies for human movements. Here, we systematically compare 658 000 simulated outbreaks generated with a spatially structured epidemic model based on two different human mobility networks: a commuting network of France extracted from mobile phone data and another extracted from a census survey. We compare epidemic patterns originating from all the 329 possible outbreak seed locations and identify the structural network properties of the seeding nodes that best predict spatial and temporal epidemic patterns to be alike. We find that similarity of simulated epidemics is significantly correlated to connectivity, traffic and population size of the seeding nodes, suggesting that the adequacy of mobile phone data for infectious disease models becomes higher when epidemics spread between highly connected and heavily populated locations, such as large urban areas.
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