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Understanding Spatiotemporal Pattern...

Devi, Chayanika,

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Understanding Spatiotemporal Patterns of COVID-19 in the United States /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Understanding Spatiotemporal Patterns of COVID-19 in the United States // Chayanika Devi.
作者:	Devi, Chayanika,
面頁冊數:	1 electronic resource (43 pages)
附註:	Source: Masters Abstracts International, Volume: 86-03.
Contained By:	Masters Abstracts International86-03.
標題:	Geography. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31628892
ISBN:	9798384034605

Understanding Spatiotemporal Patterns of COVID-19 in the United States /
Devi, Chayanika,

Understanding Spatiotemporal Patterns of COVID-19 in the United States /Chayanika Devi. - 1 electronic resource (43 pages)

Source: Masters Abstracts International, Volume: 86-03.

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.

English

ISBN: 9798384034605Subjects--Topical Terms:

524010
Geography.
Subjects--Index Terms:

COVID-19

Understanding Spatiotemporal Patterns of COVID-19 in the United States /
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