TY - JOUR

T1 - Understanding components of mobility during the COVID-19 pandemic

AU - Edsberg Møllgaard, Peter

AU - Lehmann, Sune

AU - Alessandretti, Laura

N1 - Publisher Copyright: © 2021 The Authors.

PY - 2022

Y1 - 2022

N2 - 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'.

AB - 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'.

KW - COVID-19

KW - human mobility

KW - non-negative matrix factorization

KW - human mobility

KW - CoVID-19

KW - non-negative matrix factorization

U2 - 10.1098/rsta.2021.0118

DO - 10.1098/rsta.2021.0118

M3 - Journal article

C2 - 34802271

AN - SCOPUS:85122287105

VL - 380

JO - Philosophical transactions. Series A, Mathematical, physical, and engineering sciences

JF - Philosophical transactions. Series A, Mathematical, physical, and engineering sciences

SN - 1364-503X

IS - 2214

M1 - 20210118

ER -