The COVID-19 pandemic, caused by SARS-CoV-2, has emerged as one of most significant health emergencies in recent history. SARS-CoV-2 has been characterized by the emergence of highly mutated variants that exhibit high transmissibility, virulence, and the capability of immune escape. The constantly evolving nature of the COVID-19 pandemic has underscored the necessity for a thorough comprehension of viral transmission dynamics, the effectiveness of novel monitoring techniques, and the determinants of health inequalities. This study explored several aspects of the pandemic, specifically emphasizing the emergence and dissemination of the Delta variant in Florida, the significance of environmental surveillance, and the factors associated with COVID-19 outcomes. Phylogenetic analysis using SARS-CoV-2 genome revealed that multiple independent introductions of the Delta variant fueled its spread within Florida. Further, we hypothesized that high-touch surface monitoring can be an alternative noninvasive approach to determine infection trend and detect variants. The study found high contamination rate on high-touch surfaces and the viral gene copy was positively correlated to the clinical cases in the university. Moreover, genome sequencing of environmental surface samples detected circulating and emerging variants. Additionally, spatial autocorrelation and regression analysis was conducted to investigate the relationship between county-level demographic, socioeconomic, and health-related factors and variation in COVID-19 cases, mortality, and case fatality rates. This study identified significant variations in COVID-19 outcomes across Florida counties, with factors such as age, obesity, rurality and importantly, vaccination rates playing key roles in explaining these disparities. Overall, this study emphasizes the importance of robust genomic surveillance for monitoring the emergence and spread of viral variants, the potential of environmental surface monitoring as a complementary public health tool, and the urgent need to address the underlying drivers of health disparities. These findings contribute to a more nuanced understanding of pandemic dynamics and inform data-driven strategies to mitigate the impact of future public health emergencies.
Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd2023-1178 |
Date | 01 January 2024 |
Creators | Ali, Md Sobur |
Publisher | STARS |
Source Sets | University of Central Florida |
Language | English |
Detected Language | English |
Type | text |
Format | application/pdf |
Source | Graduate Thesis and Dissertation 2023-2024 |
Rights | In copyright |
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