Identifying the Socioeconomic, Demographic, and Political Determinants of Social Mobility and Their Effects on COVID-19 Cases and Deaths: Evidence From US Counties

被引:2
作者
Jalali, Niloofar [1 ]
Tran, N. Ken [2 ]
Sen, Anindya [3 ]
Morita, Plinio Pelegrini [2 ,4 ,5 ,6 ]
机构
[1] Univ Waterloo, Fac Appl Hlth Sci, Sch Publ Hlth & Hlth Syst, Waterloo, ON, Canada
[2] Univ Waterloo, Sch Publ Hlth & Hlth Syst, 200 Univ Ave West, Waterloo, ON N2L 3G1, Canada
[3] Univ Waterloo, Dept Econ, Waterloo, ON, Canada
[4] Univ Waterloo, Dept Syst Design Engn, Waterloo, ON, Canada
[5] Univ Waterloo, Appl Hlth Informat, Waterloo, ON, Canada
[6] Univ Toronto, Inst Hlth Policy Management & Evaluat, Toronto, ON, Canada
来源
JMIR INFODEMIOLOGY | 2022年 / 2卷 / 01期
关键词
COVID-19; cases; deaths; mobility; Google mobility data; clustering;
D O I
10.2196/31813
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
Background: The spread of COVID-19 at the local level is significantly impacted by population mobility. The U.S. has had extremely high per capita COVID-19 case and death rates. Efficient nonpharmaceutical interventions to control the spread of COVID-19 depend on our understanding of the determinants of public mobility. Objective: This study used publicly available Google data and machine learning to investigate population mobility across a sample of US counties. Statistical analysis was used to examine the socioeconomic, demographic, and political determinants of mobility and the corresponding patterns of per capita COVID-19 case and death rates.Methods: Daily Google population mobility data for 1085 US counties from March 1 to December 31, 2020, were clustered based on differences in mobility patterns using K-means clustering methods. Social mobility indicators (retail, grocery and pharmacy, workplace, and residence) were compared across clusters. Statistical differences in socioeconomic, demographic, and political variables between clusters were explored to identify determinants of mobility. Clusters were matched with daily per capita COVID-19 cases and deaths. Results: Our results grouped US counties into 4 Google mobility clusters. Clusters with more population mobility had a higher percentage of the population aged 65 years and over, a greater population share of Whites with less than high school and college education, a larger percentage of the population with less than a college education, a lower percentage of the population using public transit to work, and a smaller share of voters who voted for Clinton during the 2016 presidential election. Furthermore, clusters with greater population mobility experienced a sharp increase in per capita COVID-19 case and death rates from November to December 2020.Conclusions: Republican-leaning counties that are characterized by certain demographic characteristics had higher increases in social mobility and ultimately experienced a more significant incidence of COVID-19 during the latter part of 2020.(JMIR Infodemiology 2022;2(1):e31813) doi: 10.2196/31813
引用
收藏
页数:14
相关论文
共 46 条
  • [21] Geo-located Twitter as proxy for global mobility patterns
    Hawelka, Bartosz
    Sitko, Izabela
    Beinat, Euro
    Sobolevsky, Stanislav
    Kazakopoulos, Pavlos
    Ratti, Carlo
    [J]. CARTOGRAPHY AND GEOGRAPHIC INFORMATION SCIENCE, 2014, 41 (03) : 260 - 271
  • [22] Understanding User Behavior Through the Use of Unsupervised Anomaly Detection: Proof of Concept Using Internet of Things Smart Home Thermostat Data for Improving Public Health Surveillance
    Jalali, Niloofar
    Sahu, Kirti Sundar
    Oetomo, Arlene
    Morita, Plinio Pelegrini
    [J]. JMIR MHEALTH AND UHEALTH, 2020, 8 (11):
  • [23] Jones B., 2018, An Examination of the 2016 Electorate, Based on Validated Voters
  • [24] Face masks, public policies and slowing the spread of COVID-19: Evidence from Canada *
    Karaivanov, Alexander
    Lu, Shih En
    Shigeoka, Hitoshi
    Chen, Cong
    Pamplona, Stephanie
    [J]. JOURNAL OF HEALTH ECONOMICS, 2021, 78
  • [25] Keras Team, KERAS PYTHON DEEP LE
  • [26] Knittel C.R., 2020, What Does and Does Not Correlate with COVID-19 Death Rates
  • [27] Monitoring the Spatial Spread of COVID-19 and Effectiveness of Control Measures Through Human Movement Data: Proposal for a Predictive Model Using Big Data Analytics
    Li, Zhenlong
    Li, Xiaoming
    Porter, Dwayne
    Zhang, Jiajia
    Jiang, Yuqin
    Olatosi, Bankole
    Weissman, Sharon
    [J]. JMIR RESEARCH PROTOCOLS, 2020, 9 (12):
  • [28] McLaren J., 2020, RACIAL DISPARITY COV, DOI [10.3386/w27407, DOI 10.3386/W27407]
  • [29] HVGH: Unsupervised Segmentation for High-Dimensional Time Series Using Deep Neural Compression and Statistical Generative Model
    Nagano, Masatoshi
    Nakamura, Tomoaki
    Nagai, Takayuki
    Mochihashi, Daichi
    Kobayashi, Ichiro
    Takano, Wataru
    [J]. FRONTIERS IN ROBOTICS AND AI, 2019, 6
  • [30] Demographic, jurisdictional, and spatial effects on social distancing in the United States during the COVID-19 pandemic
    Narayanan, Rajesh P.
    Nordlund, James
    Pace, R. Kelley
    Ratnadiwakara, Dimuthu
    [J]. PLOS ONE, 2020, 15 (09):