Abstract

On March 12, 2020, The World Health Organization (WHO) announced the new coronavirus 2019 as a global pandemic. In January 2022, the number of COVID-19 cases reached 300 million confirmed cases with a total death count of 5.5 million cases. Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) was identified in a seafood wholesale market in Wuhan, China. At the time of the outbreak, there was no effective treatment to protect from the novel coronavirus; the only way to control the pandemic was through reducing person-to-person contact and taking preventive measures based on the severity of the disease among the population. The first aim of this thesis is to visualize the confirmed, deaths, and recovered cases across the most affected countries and to analyze if non-pharmaceutical interventions (NPI), such as government interventions, are effective in flattening the curve. The second aim of this thesis is to use machine learning models to predict the severity of COVID-19 and clinical outcomes based on demographic, epidemiological, comorbidities, and laboratory findings among the Egyptian population during the lockdown period.

School

School of Sciences and Engineering

Department

Biotechnology Program

Degree Name

MS in Biotechnology

Graduation Date

Winter 1-31-2023

Submission Date

8-1-2022

First Advisor

Ahmed Moustafa

Committee Member 1

Suher Zada

Committee Member 2

Dalia Omran

Committee Member 3

Walid Fouad

Extent

89 p.

Document Type

Master's Thesis

Institutional Review Board (IRB) Approval

Not necessary for this item

2013 IRB form to submit with thesis (1) (1).pdf (195 kB)
IRB Approval Form, Hanya

Hanya's approval.pdf (310 kB)
Signature page, Hanya

receipt_Turnitin.pdf (206 kB)
Turitin receipt, Hanya

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