Non-linear spatial linkage between COVID-19 pandemic and mobility in ten countries: A lesson for future wave.

Yasir Habib, Enjun Xia, Shujahat Haider Hashmi, Zeeshan Fareed
Author Information
  1. Yasir Habib: School of Management and Economics, Beijing Institute of Technology, Beijing 100081, China. Electronic address: yasirhabib.ch@outlook.com.
  2. Enjun Xia: School of Management and Economics, Beijing Institute of Technology, Beijing 100081, China. Electronic address: enjunxia@bit.edu.cn.
  3. Shujahat Haider Hashmi: School of Economics, Huazhong University of Science and Technology, Wuhan, China; Research Associate, MUSLIM Institute, Islamabad, Pakistan. Electronic address: shujahat_hashmi@hotmail.com.
  4. Zeeshan Fareed: School of Economics and Management, Huzhou University, Huzhou, Zhejiang, China. Electronic address: zeeshanfareed@hotmail.com.

Abstract

BACKGROUND: Restrictive measures enacted in response to the COVID-19 pandemic have resulted in dramatic and substantial variations in people's travel habits and behaviors worldwide. This paper empirically examines the asymmetric inter-linkages between transportation mobility and COVID-19.
METHODS: Using daily data from 1st March 2020 to 15th July 2020, this study draws the dynamic and causal relationships between transportation mobility and COVID-19 in ten selected countries (i.e., USA, Brazil, Mexico, UK, Spain, Italy, France, Germany, Canada, and Belgium). To systematically analyze how the quantiles of COVID-19 (transportation mobility) affect the quantiles of transportation mobility (COVID-19), a complete set of non-linear modeling including the quantile-on-quantile (QQ) regression and quantile Granger causality in mean is applied.
RESULTS: Our preliminary findings strictly reject the preposition of data normality and highlight that the observed relationship is highly correlated and quantile-dependent. The empirical results demonstrate the heterogeneous dependence between COVID-19 and transportation mobility across quantiles. The findings acclaim the presence of a significant positive association between COVID-19 and transportation mobility in the USA, UK, Spain, Italy, Canada, France, Germany and Belgium, predominantly at upper quantiles, but results are contrasting in the case of Brazil and Mexico. In addition, either lower or upper quantiles of both variables indicate a declining negative effect of transportation mobility on COVID-19. Furthermore, the outcomes of quantile Granger causality in mean conclude a bidirectional causal link between COVID-19 and transportation mobility for almost all sample countries. Unlike them, France has found unidirectional causality that extends from COVID-19 to transportation mobility.
CONCLUSIONS: We may conclude that COVID-19 leads to a reduction in transportation mobility. On the other hand, the empirical results quantify that excessive transportation mobility levels stimulate pandemic cases, and social distancing is one of the primary measures to encounter infection transmission. Imperative country-specific policy implications pertaining to public health, potential virus spread, transportation, and the environment may be drawn from these findings.

Keywords

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MeSH Term

COVID-19
Humans
Pandemics
SARS-CoV-2
Transportation
Travel

Word Cloud

Created with Highcharts 10.0.0COVID-19mobilitytransportationquantilespandemicFrancequantilecausalityfindingsresultsmeasuresdata2020causaltencountriesUSABrazilMexicoUKSpainItalyGermanyCanadaBelgiumQQGrangermeanempiricalupperconcludemayBACKGROUND:Restrictiveenactedresponseresulteddramaticsubstantialvariationspeople'stravelhabitsbehaviorsworldwidepaperempiricallyexaminesasymmetricinter-linkagesMETHODS:Usingdaily1stMarch15thJulystudydrawsdynamicrelationshipsselectediesystematicallyanalyzeaffectcompletesetnon-linearmodelingincludingquantile-on-quantileregressionappliedRESULTS:preliminarystrictlyrejectprepositionnormalityhighlightobservedrelationshiphighlycorrelatedquantile-dependentdemonstrateheterogeneousdependenceacrossacclaimpresencesignificantpositiveassociationpredominantlycontrastingcaseadditioneitherlowervariablesindicatedecliningnegativeeffectFurthermoreoutcomesbidirectionallinkalmostsampleUnlikefoundunidirectionalextendsCONCLUSIONS:leadsreductionhandquantifyexcessivelevelsstimulatecasessocialdistancingoneprimaryencounterinfectiontransmissionImperativecountry-specificpolicyimplicationspertainingpublichealthpotentialvirusspreadenvironmentdrawnNon-linearspatiallinkagecountries:lessonfuturewaveQuantile-onapproachTransportation

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