Modeling international mobility using roaming cell phone traces during COVID-19 pandemic

Area of Science:

• Mobility modeling
• Epidemiology
• Data science

Background:

• Intra-country mobility studies dominate human mobility research.
• Timely international commuter data is vital for disease spread and migration.
• Mobile phones offer a unique data source for monitoring international mobility flows.

Purpose of the Study:

• To propose a novel model for monitoring incoming and outgoing international mobility using mobile phone roaming data.
• To address limitations in traditional mobility models regarding mobility restrictions during pandemics.
• To enhance the accuracy of international mobility flow modeling.

Main Methods:

• Utilized mobile phone roaming data to capture international mobility.
• Applied traditional gravity and radiation models to model mobility flows.
• Developed and implemented the COVID Gravity Model (CGM) as an extension of the gravity model.

Main Results:

• The COVID Gravity Model (CGM) demonstrated superior accuracy compared to traditional models.
• CGM improved incoming international mobility modeling accuracy by 126.9%.
• CGM enhanced outgoing international mobility modeling accuracy by 63.9%.

Conclusions:

• The COVID Gravity Model (CGM) effectively models international mobility, especially during pandemic scenarios.
• Mobile phone roaming data provides a valuable resource for real-time international mobility analysis.
• The proposed CGM offers a significant advancement in understanding cross-border movements and their implications.