An Interstate Trips Analysis during COVID-19 in the United States

The worldwide outbreak of COVID-19 has posed a dire threat to the public. Human mobility has changed in various ways over the course of the pandemic. Despite current studies on common mobility metrics, research specifically on state-to-state mobility is very limited. By leveraging the mobile phone location data from over 100 million anonymous devices, we estimate the population flow between all states in the United States. We first analyze the temporal pattern and spatial differences of between-state flow from January 1, 2020 to May 15, 2020. Then, with repeated measures ANOVA and post-hoc analysis, we discern different time-course patterns of between-state population flow by pandemic severity groups. A further analysis shows moderate to high correlation between the flow reduction and the pandemic severity, the strength of which varies with different policies. This paper is promising in predicting imported cases.

[1]  M. Marathe,et al.  Interplay of global multi-scale human mobility, social distancing, government interventions, and COVID-19 dynamics , 2020, medRxiv.

[2]  F. Dominici,et al.  Aggregated mobility data could help fight COVID-19 , 2020, Science.

[3]  M. Lynn,et al.  Examining amount and pattern of change: comparing repeated measures ANOVA and individual regression analysis. , 1993, Nursing research.

[4]  Marco De Nadai,et al.  Mobile phone data for informing public health actions across the COVID-19 pandemic life cycle , 2020, Science Advances.

[5]  J. Stevens.  Applied Multivariate Statistics for the Social Sciences , 1986 .

[6]  L. Meyers,et al.  Risk for Transportation of Coronavirus Disease from Wuhan to Other Cities in China , 2020, Emerging infectious diseases.

[7]  Ellen R. Girden,et al.  ANOVA: Repeated Measures , 1991 .

[8]  Zunyou Wu,et al.  Characteristics of and Important Lessons From the Coronavirus Disease 2019 (COVID-19) Outbreak in China: Summary of a Report of 72 314 Cases From the Chinese Center for Disease Control and Prevention. , 2020, JAMA.

[9]  Nuno R. Faria,et al.  The effect of human mobility and control measures on the COVID-19 epidemic in China , 2020, Science.

[10]  Lei Zhang,et al.  Human Mobility Trends during the COVID-19 Pandemic in the United States , 2020, 2005.01215.

[11]  A. Kabiri,et al.  Interactive COVID-19 Mobility Impact and Social Distancing Analysis Platform , 2020, medRxiv.

[12]  J. Stevens,et al.  Applied multivariate statistics for the social sciences, 4th ed. , 2002 .

[13]  Ralph Müller,et al.  Monitoring individual morphological changes over time in ovariectomized rats by in vivo micro-computed tomography. , 2006, Bone.

[14]  Jianmin Jia,et al.  Population flow drives spatio-temporal distribution of COVID-19 in China , 2020, Nature.

[15]  David Van Dijcke,et al.  The COVID-19 Pandemic: Government vs. Community Action Across the United States , 2020 .

[16]  Jessica Gurevitch,et al.  Design and Analysis of Ecological Experiments , 1993 .

[17]  Lei Zhang,et al.  Observed mobility behavior data reveal social distancing inertia , 2020, ArXiv.

[18]  S. Brooks,et al.  Applied Multivariate Statistics for the Social Sciences , 1993 .

[19]  Mofeng Yang,et al.  Data-Driven Modeling Reveals the Impact of Stay-at-Home Orders on Human Mobility during the COVID-19 Pandemic in the U.S , 2020, 2005.00667.

[20]  Sophia Decker,et al.  Design And Analysis Of Ecological Experiments , 2016 .

[21]  Fan Zhang,et al.  Modeling real-time human mobility based on mobile phone and transportation data fusion , 2018, Transportation Research Part C: Emerging Technologies.

[22]  Quanyi Wang,et al.  Early evaluation of the Wuhan City travel restrictions in response to the 2019 novel coronavirus outbreak , 2020 .

[23]  W. Edmunds,et al.  Precautionary Behavior in Response to Perceived Threat of Pandemic Influenza , 2007, Emerging infectious diseases.

[24]  Jakob Puchinger,et al.  Inferring dynamic origin-destination flows by transport mode using mobile phone data , 2019, Transportation Research Part C: Emerging Technologies.

[25]  Chenfeng Xiong,et al.  Quantifying human mobility behavior changes in response to non-pharmaceutical interventions during the COVID-19 outbreak in the United States , 2020, ArXiv.