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Understanding Spatio-temporal Patterns of COVID-19 in the United States

Devi, Chayanika
http://rave.ohiolink.edu/etdc/view?acc_num=ucin172120835638492

Abstract Details

2024, MA, University of Cincinnati, Arts and Sciences: Geography.
This thesis studies the early detection of COVID-19 cases at the county level in the United States(U.S) by identifying significant clusters of reported cases in terms of both space and time. We performed a retrospective space-time scan statistical analysis to detect emerging clusters of the COVID-19 pandemic. Moreover, the proposed study has the potential to offer significant insights into the dynamics of disease spatial patterns among urban and rural communities. This understanding would allow for targeted interventions and the allocation of resources for the COVID-19 pandemic, as well as future global health challenges and emerging epidemics. Using a retrospective space-time scan statistic technique, we identified counties into high-risk, medium-risk, and low-risk categories for COVID-19 transmission. We achieved this by analyzing the incidence rates across the counties in in the U.S. from the beginning of the pandemic in late January 2020 to the end of April 2022, including of a total of 105 weeks. We systematically evaluated the incidence rates from the start of the pandemic to the 105th week, considering significant events like the first outbreak in March 2020, the relaxation of preventive measures during a later phase, the significant rise during the winter wave, and the emergence of the Delta variant's wave in September 2021. In order to test this hypothesis, we proposed a framework that implements data visualization and Geographic Information Science (GIS) to identify and categorize clusters according to the time at which they emerged during different stages of the pandemic.
Diego Cuadros, Ph.D. (Committee Chair)
Xi Chen, Ph.D. (Committee Member)
Robert South, Ph.D. (Committee Member)
40 p.
Geography
COVID-19; Retrospective space-time scan statistic; Geographic Information Science; Space-time clusters; United States of America; Urban and Rural

Recommended Citations

Citations

  • Devi, C. (2024). Understanding Spatio-temporal Patterns of COVID-19 in the United States [Master's thesis, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin172120835638492

    APA Style (7th edition)

  • Devi, Chayanika. Understanding Spatio-temporal Patterns of COVID-19 in the United States. 2024. University of Cincinnati, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin172120835638492.

    MLA Style (8th edition)

  • Devi, Chayanika. "Understanding Spatio-temporal Patterns of COVID-19 in the United States." Master's thesis, University of Cincinnati, 2024. http://rave.ohiolink.edu/etdc/view?acc_num=ucin172120835638492

    Chicago Manual of Style (17th edition)

ucin172120835638492
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© 2024, all rights reserved.
This open access ETD is published by University of Cincinnati and OhioLINK.
Release 3.2.12