Understanding components of mobility during the COVID-19 pandemic
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Understanding components of mobility during the COVID-19 pandemic. / Edsberg Møllgaard, Peter; Lehmann, Sune; Alessandretti, Laura.
I: Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, Bind 380, Nr. 2214, 20210118, 2022.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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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 -
ID: 346592100