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

TL;DR

This paper introduces the COVID Gravity Model, an extension of traditional gravity models, to accurately analyze international mobility using roaming cell phone data during the COVID-19 pandemic, surpassing existing models in accuracy.

Contribution

The paper proposes the COVID Gravity Model, which explicitly incorporates mobility restrictions, improving accuracy in modeling international mobility flows during pandemics.

Findings

COVID Gravity Model outperforms traditional models by 126.9% for incoming mobility.

It improves outgoing mobility modeling accuracy by 63.9%.

Roaming cell phone data effectively captures international mobility during COVID-19.

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.