The role of mobility in the dynamics of the COVID-19 epidemic in Andalusia

TL;DR

This study demonstrates that incorporating dynamic, cell-phone-based mobility data into metapopulation models is crucial for accurately understanding and predicting the second wave of COVID-19 in Andalusia, outperforming static or no-mobility models.

Contribution

It introduces a method to include time-dependent mobility matrices based on cell-phone data into metapopulation models for COVID-19 spread.

Findings

Dynamic mobility models better predict second wave emergence.

Static or no-mobility models fail to capture key trends.

Cell-phone data enhances model accuracy.

Abstract

Metapopulation models have been a popular tool for the study of epidemic spread over a network of highly populated nodes (cities, provinces, countries) and have been extensively used in the context of the ongoing COVID-19 pandemic. In the present work, we revisit such a model, bearing a particular case example in mind, namely that of the region of Andalusia in Spain during the period of the summer-fall of 2020 (i.e., between the first and second pandemic waves). Our aim is to consider the possibility of incorporation of mobility across the province nodes focusing on mobile-phone time dependent data, but also discussing the comparison for our case example with a gravity model, as well as with the dynamics in the absence of mobility. Our main finding is that mobility is key towards a quantitative understanding of the emergence of the second wave of the pandemic and that the most accurate way to capture it involves dynamic (rather than static) inclusion of time-dependent mobility matrices based on cell-phone data. Alternatives bearing no mobility are unable to capture the trends revealed by the data in the context of the metapopulation model considered herein.