ArticleJournal of the Royal Society, Interface2016
Assessing reliable human mobility patterns from higher order memory in mobile communications.
Article in Journal of the Royal Society, Interface, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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.
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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
9 citing papers in PubMed, 33 citations in OpenAlex.
- How host mobility formulations shape estimates of pathogen dispersal and epidemic risk in non endemic regions.PLoS computational biology · 2026Article
- Slower searching yields higher efficiency: A case study of taxi drivers.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Obesity and age are transmission risk factors for SARS-CoV-2 infection among exposed individuals.PNAS nexus · 2024Article
- Understanding Hazardous Materials Transportation Accidents Based on Higher-Order Network Theory.International journal of environmental research and public health · 2022Article
- Heterogeneity in social and epidemiological factors determines the risk of measles outbreaks.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- Article
- Using Mobile Phone Data for Emergency Management: a Systematic Literature Review.Information systems frontiers : a journal of research and innovation · 2020Article
- From networks to optimal higher-order models of complex systems.Nature physics · 2019Article
- Epidemic spreading in localized environments with recurrent mobility patterns.Physical review. E · 2018Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Understanding how people move within a geographical area, e.g. a city, a country or the whole world, is fundamental in several applications, from predicting the spatio-temporal evolution of an epidemic to inferring migration patterns. Mobile phone records provide an excellent proxy of human mobility, showing that movements exhibit a high level of memory. However, the precise role of memory in widely adopted proxies of mobility, as mobile phone records, is unknown. Here we use 560 million call detail records from Senegal to show that standard Markovian approaches, including higher order ones, fail in capturing real mobility patterns and introduce spurious movements never observed in reality. We introduce an adaptive memory-driven approach to overcome such issues. At variance with Markovian models, it is able to realistically model conditional waiting times, i.e. the probability to stay in a specific area depending on individuals' historical movements. Our results demonstrate that in standard mobility models the individuals tend to diffuse faster than observed in reality, whereas the predictions of the adaptive memory approach significantly agree with observations. We show that, as a consequence, the incidence and the geographical spread of a disease could be inadequately estimated when standard approaches are used, with crucial implications on resources deployment and policy-making during an epidemic outbreak.
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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.