Mapping the Spatiotemporal Patterns of COVID-19 Risk in the United States
14 Pages Posted: 7 Jul 2020
Date Written: June 6, 2020
The newly discovered Novel Coronavirus’s (COVID-19) transmission rate has been found by scientists to surpass any similar disease in history. As of May 2020, COVID-19 cases have reached 6,000,000 and over 350,000 people have died globally. Beginning in March, the abrupt outbreaks in many states in the United States have brought on unprecedented public health and humanitarian crises, globally the most severely hit country. This study collected twenty-seven measurements in the recent five years to evaluate the COVID-19 risk in different U.S. counties by applying an INFORM ERI framework and map the spatiotemporal pattern changes in risk from March to May in the United States. Results showed that:
(1) at the early stage in March, there were more high-risk areas concentrated in states in the South and West than in the Midwest and the Northeast;
(2) The high-risk areas in the Northeast emerged in April as New York became the epicenter of COVID-19 outbreaks in the U.S.;
(3) As of May, while the high-risk regions shifted to local major metropolitan areas, a new epicenter of high COVID-19 risk emerged in counties in Midwestern states such as Illinois, Michigan, Indiana, and Ohio.
Conclusions: we suggested major metropolitans in high risk areas reopening plans more cautiously, especially those in the Midwest may be better off pausing their plans altogether. During the reopening process, it is critical to put more attention on vulnerable groups to avoid further humanitarian crises.
Note: Funding: The research was funded by the Good Systems Program and the Cooperative Mobility for Competitive Megaregions University Transportation Center at the University of Texas at Austin.
Declaration of Interest: None to declare
Ethics: The data included in this study were from publicly available data-sharing portals and all sources were properly cited in the manuscript.
Keywords: COVID-19, INFORM risk, spatiotemporal patterns, Vulnerable populations
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