Abstract
Travel restrictions have proven to be an effective strategy to control the spread of the COVID-19 epidemics, in part because they help delay disease propagation across territories. The question, however, as to how different types of travel behaviour, from commuting to holiday-related travel, contribute to the spread of infectious diseases remains open. Here, we address this issue by using factorization techniques to decompose the temporal network describing mobility flows throughout 2020 into interpretable components. Our results are based on two mobility datasets: the first is gathered from Danish mobile network operators; the second originates from the Facebook Data-For-Good project. We find that mobility patterns can be described as the aggregation of three mobility network components roughly corresponding to travel during workdays, weekends and holidays, respectively. We show that, across datasets, in periods of strict travel restrictions the component corresponding to workday travel decreases dramatically. Instead, the weekend component, increases. Finally, we study how each type of mobility (workday, weekend and holiday) contributes to epidemics spreading, by measuring how the effective distance, which quantifies how quickly a disease can travel between any two municipalities, changes across network components.This article is part of the theme issue ‘Data science approaches to infectious disease surveillance’.
Highlights
Throughout the COVID-19 pandemic, governments worldwide have restricted individual mobility for their citizens with the goal of reducing social contacts and limit spread of the virus
We investigated the effect of the restrictions imposed to contrast the spread of COVID-19 on mobility behaviour in four European countries
We used two mobility datasets describing daily trips between spatial units at the second administrative level throughout 2020, one collected by major Danish mobile phone operators and the other collected by Facebook in Spain, France and Italy
Summary
Throughout the COVID-19 pandemic, governments worldwide have restricted individual mobility for their citizens with the goal of reducing social contacts and limit spread of the virus Some of these interventions, e.g. travel bans within and across national borders and homeisolation orders, target travel behaviour . We study how non-pharmaceutical interventions have affected different components of travel behaviour using two large-scale datasets collected from mobile phones. Across different datasets, mobility patterns are well described as the combination of three components: the first mainly capturing commuting and work-day travel, the second describing weekend trips, and the latter describing holiday We show how these different components contributed to the effective distance, which determines how long it takes for a disease to diffuse across time and space. We identified periods of ‘lockdown’ using the stringency data released by the Oxford COVID-19 Government Response Tracker (for further details see electronic supplementary material, figure S6 and table S2) [16]
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