ArticleEPJ data science2022
Modeling international mobility using roaming cell phone traces during COVID-19 pandemic.
Article in EPJ data science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Human mobility under disasters: a systematic review and framework for equitable and resilient mobility governance.npj natural hazards · 2025Article
- Human mobility and the infectious disease transmission: A systematic review.Geo-spatial information science = Diqui kongjian xinxi kexue xuebao · 2024Article
- Mobility constraints in segregation models.Scientific reports · 2023Article
- Crime, inequality and public health: a survey of emerging trends in urban data science.Frontiers in big data · 2023Review
- Article
- Generating mobility networks with generative adversarial networks.EPJ data science · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Most of the studies related to human mobility are focused on intra-country mobility. However, there are many scenarios (e.g., spreading diseases, migration) in which timely data on international commuters are vital. Mobile phones represent a unique opportunity to monitor international mobility flows in a timely manner and with proper spatial aggregation. This work proposes using roaming data generated by mobile phones to model incoming and outgoing international mobility. We use the gravity and radiation models to capture mobility flows before and during the introduction of non-pharmaceutical interventions. However, traditional models have some limitations: for instance, mobility restrictions are not explicitly captured and may play a crucial role. To overtake such limitations, we propose the COVID Gravity Model (CGM), namely an extension of the traditional gravity model that is tailored for the pandemic scenario. This proposed approach overtakes, in terms of accuracy, the traditional models by 126.9% for incoming mobility and by 63.9% when modeling outgoing mobility flows.
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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.