Abstract

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.

Notes

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Graduation Date

2021

Semester

Fall

Advisor

Karwowski, Waldemar

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering and Computer Science

Department

Industrial Engineering and Management Systems

Degree Program

Industrial Engineering

Format

application/pdf

Identifier

CFE0008819; DP0026098

URL

https://purls.library.ucf.edu/go/DP0026098

Language

English

Release Date

December 2021

Length of Campus-only Access

None

Access Status

Doctoral Dissertation (Open Access)

Restricted to the UCF community until December 2021; it will then be open access.

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