ArticlePNAS nexus2023
The limits of human mobility traces to predict the spread of COVID-19: A transfer entropy approach.
Article in PNAS nexus, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Mobility-informed metapopulation models predict the spatio-temporal spread of respiratory epidemics across scales.BMC global and public health · 2026Article
- Mobility-driven synthetic contact matrices as a scalable solution for real-time pandemic response modeling.Nature communications · 2026Article
- Utilizing wastewater surveillance to model behavioural responses and prevent healthcare overload during "Disease X" outbreaks.Emerging microbes & infections · 2025Article
- Explaining COVID-19 dynamics through user activity data from digital platforms with Yandex's self-isolation index as a case study.Scientific reports · 2025Article
- Human mobility and sewage data correlate with COVID-19 epidemic evolution in a 3-year surveillance of the metropolitan area of Bologna.BMC infectious diseases · 2025Article
- Physical partisan proximity outweighs online ties in predicting US voting outcomes.PNAS nexus · 2025Article
- Assessing the impact of interregional mobility on COVID19 spread in Spain using transfer entropy.Scientific reports · 2025Article
- Characterising non-household contact patterns relevant to respiratory transmission in the USA: analysis of a cross-sectional survey.The Lancet. Digital health · 2025Article
- Comparative evaluation of behavioral epidemic models using COVID-19 data.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Socioeconomic correlates of urban mobility trends in two Australian cities during transitional periods of the COVID-19 pandemic.Royal Society open science · 2025Article
- Optimizing Contact Network Topological Parameters of Urban Populations Using the Genetic Algorithm.Entropy (Basel, Switzerland) · 2024Article
- Impacts of human mobility on the citywide transmission dynamics of 18 respiratory viruses in pre- and post-COVID-19 pandemic years.Nature communications · 2024Article
- Quality assessment and community detection methods for anonymized mobility data in the Italian Covid context.Scientific reports · 2024Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Mobile phone data have been widely used to model the spread of COVID-19; however, quantifying and comparing their predictive value across different settings is challenging. Their quality is affected by various factors and their relationship with epidemiological indicators varies over time. Here, we adopt a model-free approach based on transfer entropy to quantify the relationship between mobile phone-derived mobility metrics and COVID-19 cases and deaths in more than 200 European subnational regions. Using multiple data sources over a one-year period, we found that past knowledge of mobility does not systematically provide statistically significant information on COVID-19 spread. Our approach allows us to determine the best metric for predicting disease incidence in a particular location, at different spatial scales. Additionally, we identify geographic and demographic factors, such as users' coverage and commuting patterns, that explain the (non)observed relationship between mobility and epidemic patterns. Our work provides epidemiologists and public health officials with a general-not limited to COVID-19-framework to evaluate the usefulness of human mobility data in responding to epidemics.
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