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COVID-19 Pandemic with Human Mobility Across Countries

ABSTRACT: This study develops a holistic view of the novel coronavirus (COVID-19) spread worldwide through a spatial–temporal model with network dynamics. By using a unique human mobility dataset containing 547 166 flights with a total capacity of 101 455 913 passengers from January 22 to April 24, 2020, we analyze the epidemic correlations across 22 countries in six continents and particularly the changes in such correlations before and after implementing the international travel restriction policies targeting different countries. Results show that policymakers should move away from the previous practices that focus only on restricting hotspot areas with high infection rates. Instead, they should develop a new holistic view of global human mobility to impose the international movement restriction. The study further highlights potential correlations between international human mobility and focal countries’ epidemic situations in the global network of COVID-19 pandemic.

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The online version of this article (10.1007/s40305-020-00317-6) contains supplementary material, which is available to authorized users.

SUBMITTER: Zhang C

PROVIDER: S-EPMC7396460 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Project description:PurposeThe COVID-19 pandemic has illuminated various heterogeneities between urban and rural environments in public health. The SARS-CoV-2 virus initially spread into the United States from international ports of entry and into urban population centers, like New York City. Over the course of the pandemic, cases emerged in more rural areas, implicating issues of transportation and mobility. Additionally, many rural areas developed into national hotspots of prevalence and transmission. Our aim was to investigate the preliminary impacts of road travel on the spread of COVID-19. This investigation has implications for future public health mitigation efforts and travel restrictions in the United States.MethodsCounty-level COVID-19 data were analyzed for spatiotemporal patterns in time-to-event distributions using animated choropleth maps. Data were obtained from The New York Times and the Bureau of the Census. The arrival event was estimated by examining the number of days between the first reported national case (January 21, 2020) and the date that each county attained a given prevalence rate. Of the 3108 coterminous US counties, 2887 were included in the analyses. Data reflect cases accumulated between January 21, 2020, and May 17, 2020.FindingsAnimations revealed that COVID-19 was transmitted along the path of interstates. Quantitative results indicated rural-urban differences in the estimated arrival time of COVID-19. Counties that are intersected by interstates had an earlier arrival than non-intersected counties. The arrival time difference was the greatest in the most rural counties and implicates road travel as a factor of transmission into rural communities.ConclusionHuman mobility via road travel introduced COVID-19 into more rural communities. Interstate travel restrictions and road travel restrictions would have supported stronger mitigation efforts during the earlier stages of the COVID-19 pandemic and reduced transmission via network contact.

Project description:Mobility restrictions have been identified as key non-pharmaceutical interventions to limit the spread of the SARS-COV-2 epidemics. However, these interventions present significant drawbacks to the social fabric and negative outcomes for the real economy. In this paper we propose a real-time monitoring framework for tracking the economic consequences of various forms of mobility reductions involving European countries. We adopt a granular representation of mobility patterns during both the first and second waves of SARS-COV-2 in Italy, Germany, France and Spain to provide an analytical characterization of the rate of losses of industrial production by means of a nowcasting methodology. Our approach exploits the information encoded in massive datasets of human mobility provided by Facebook and Google, which are published at higher frequencies than the target economic variables, in order to obtain an early estimate before the official data becomes available. Our results show, in first place, the ability of mobility-related policies to induce a contraction of mobility patterns across jurisdictions. Besides this contraction, we observe a substitution effect which increases mobility within jurisdictions. Secondly, we show how industrial production strictly follows the dynamics of population commuting patterns and of human mobility trends, which thus provide information on the day-by-day variations in countries' economic activities. Our work, besides shedding light on how policy interventions targeted to induce a mobility contraction impact the real economy, constitutes a practical toolbox for helping governments to design appropriate and balanced policy actions aimed at containing the SARS-COV-2 spread, while mitigating the detrimental effect on the economy. Our study reveals how complex mobility patterns can have unequal consequences to economic losses across countries and call for a more tailored implementation of restrictions to balance the containment of contagion with the need to sustain economic activities.

Dataset Information

COVID-19 Pandemic with Human Mobility Across Countries

Electronic supplementary material

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