In December 2019, China announced the breakout of a new virus identified as coronavirus SARS-CoV-2 (COVID-19), which soon grew exponentially and became a global pandemic. Despite strict actions to mitigate the spread of the virus in various countries, the COVID-19 pandemic resulted in a significant loss of human life in 2020 and 2021. To better understand the pandemic, this doctoral research incorporated data analytics to evaluate the behavior and impacts of the virus. The doctoral research contributed to the scientific body of the knowledge in different ways including (1) presenting a systematic literature review of current research and topics about impacts of the COVID-19 pandemic; (2) predicting the dynamics of the COVID-19 pandemic using deterministic and stochastic Recurrent Neural Networks; (3) predicting the dynamics of the COVID-19 pandemic using graph neural networks; and (4) analyzing the dynamics of the COVID-19 pandemic using graph theoretical method. This dissertation is sorted out as a manuscript-style including four published journal articles. The results of this doctoral research provide a comprehensive view of the behavior and impacts of the COVID-19 pandemic.
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Doctor of Philosophy (Ph.D.)
College of Engineering and Computer Science
Industrial Engineering and Management Systems
Length of Campus-only Access
Doctoral Dissertation (Open Access)
Davahli, Mohammad Reza, "Understanding the Behavior of the COVID-19 Pandemic Using Data Analytics" (2021). Electronic Theses and Dissertations, 2020-. 848.
Restricted to the UCF community until December 2021; it will then be open access.