Speaker
Description
The onset of the SARS-COV-2 pandemic has drastically changed the everyday lives of people, communities, and countries. Humans quickly adapted work methods for productivity, while mobility and social life underwent dramatic transformations. In this work, we study how the mobility patterns between administrative regions changed during two years of the pandemic. Specifically, we are focused on the change in the network structure of mobility between regions, both in topology and intensity. We obtained data from the Data For Good initiative about mobility between regions in four countries: Italy, Sweden, Great Britain, and Brazil. We map this data onto binary and weighted networks and measure how they change with time, focusing on their differences. Specifically, we study how the difference between successive networks, daily networks, and weekly networks change with time during a two-year period by using network portrait divergence as measure of change. We create a time series of divergence for three types of binary networks, adjacent, daily, and weekly, and therefore weighted networks for each country. We study the multifractality of these time series, the temporal patterns for each time series, and trends.
Our results show that the average differences are increasing with the time window size between the two compared networks, with adjacent networks being the most and weekly networks being the least similar. The average difference is changing between countries, with Brazil showing the most stable mobility networks, both binary and weighted, and Great Britain having the highest average difference for both types of networks. While one would expect more stable, predictable, and less variable signals for countries with strict rules, such as Italy and Great Britain, our results show that this is not the case.
