2020 IEEE International Conference on Big Data (Big Data) 2020
DOI: 10.1109/bigdata50022.2020.9377932
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Factors Influencing Human Mobility During The COVID-19 Pandemic in Selected Countries of Europe and North America

Abstract: A novel coronavirus was first reported in Wuhan, China in December 2109 and was declared a global pandemic by the World Health Organization on 11 March 2020. Identification of the critical factors that predict reduced mobility and human interaction is critical to developing successful transmission mitigation efforts globally. Governments and localities around the world have responded with wide-ranging policies related to containment and closure such as travel restrictions and stay-at-home-orders, as well as e… Show more

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Cited by 4 publications
(3 citation statements)
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“…Of the 10 natural experiments, five found that when face covering policies were in place mobility increased (i.e., people were less likely to stay at home) [47][48][49][50][51], four found that mobility was unchanged [52][53][54][55], and one found that mobility decreased (i.e., people were more likely…”
Section: Mobility (See Table 2b In S4 Appendix For Full Details)mentioning
confidence: 99%
“…Of the 10 natural experiments, five found that when face covering policies were in place mobility increased (i.e., people were less likely to stay at home) [47][48][49][50][51], four found that mobility was unchanged [52][53][54][55], and one found that mobility decreased (i.e., people were more likely…”
Section: Mobility (See Table 2b In S4 Appendix For Full Details)mentioning
confidence: 99%
“…Anonymized mobile phone data was also used in another study in Austria [13] where the authors assess the effect of the lock-down quantitatively for all regions and present an analysis of daily changes of human mobility throughout Austria. In [15], the relationships between containment and closure policies, disease trends, and human mobility patterns in 40 countries in Western, Eastern, Northern, and Southern Europe and North America is explored. Finally, in Rio de Janeiro, network usage data was analysed and compared from pre-lockdown, during lockdown, and post-lockdown phases to understand human mobility patterns during the pandemic, and to evaluate the effect of lockdowns on mobility.…”
Section: Related Workmentioning
confidence: 99%
“…In [13], authors explore the relationship between various factors that influenced mobility such as containment and closure policies, disease trends, and human mobility patterns in 40 countries in Western, Eastern, Northern, and Southern Europe and North America. The model parameter estimations allowed to conclude that the total number of cases, the cancellation of several activities (schools, events, remote work), mask policies and the pandemic declaration all were significant predictors of change in workplace mobility from baseline.…”
Section: Related Workmentioning
confidence: 99%