# Modeling International Mobility using Roaming Cell Phone Traces during COVID-19 Pandemic

arXiv: https://arxiv.org/abs/2201.02470 · HTML: https://mlsanity.com/paper/2201.02470/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jan 2022.

## Built on by (most cited first, 5 of 5)

- [Generating Synthetic Mobility Networks with Generative Adversarial Networks](https://mlsanity.com/paper/2202.11028/) (2022)
- [Human Mobility Prediction with Causal and Spatial-constrained Multi-task Network](https://mlsanity.com/paper/2206.05731/) (2022)
- [Enhancing crowd flow prediction in various spatial and temporal granularities](https://mlsanity.com/paper/2203.07372/) (2022)
- [Reducing Computational Costs in Sentiment Analysis: Tensorized Recurrent Networks vs. Recurrent Networks](https://mlsanity.com/paper/2306.09705/) (2023)
- [Where to Go Next Day: Multi-scale Spatial-Temporal Decoupled Model for Mid-term Human Mobility Prediction](https://mlsanity.com/paper/2501.06561/) (2025)

## Abstract (arXiv)

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.