Cheryl Ann Alexander1* and Lidong Wang2
1Institute for IT innovation and Smart Health, Mississippi, USA
2Institute for Systems Engineering Research, Mississippi state university, Vicksburg, USA
*Corresponding Author: Cheryl Ann Alexander, Institute for IT Innovation and Smart Health, Mississippi, USA.
Received: November 03, 2020; Published: December 09, 2020
SARS-CoV-2 is a novel coronavirus that developed in Wuhan, China in the Hubei Province at the Huanan Wet Market, a fresh market that sells and prepares live and exotic animals. The SARS-CoV-2 virus was a moderately fatal, highly contagious disease like that of its sister SARS-CoV. SARS-CoV-2 was initially named the novel coronavirus 2019 by the World Health Organization (WHO); however, in February 2020, the WHO named the viral disease COVID-19. COVID-19 causes a potentially fatal pneumonia among other symptoms. The most common symptoms are fever greater than 38° Celsius, dry cough, shortness of breath, etc. The most interesting symptom of COVID-19 is the inflammatory response, often called the “cytokine storm”, which can cause an acute respiratory distress syndrome (ARDS) and cascade failure which can be potentially fatal. Therefore, because COVID-19 is potentially fatal and highly contagious, with very few therapeutics available in the beginning and no vaccine as of today’s publication in November 2020, the containment and limitation of the spread of the disease and mathematical projections of cases is critical to mitigating the spread. Big Data analytics can be used to project numbers of cases and using datasets of formerly published early papers, this paper uses a regression analysis to prove the projections of some experts were erroneous. This paper introduces COVID-19, data management systems, and conducts a regression analysis on a formerly published paper’s dataset.
Keywords: SARS-CoV-2; COVID-19; Regression Analysis; Big Data; Data Management Systems
Citation: Cheryl Ann Alexander and Lidong Wang. “COVID-19: A Data Analysis Using Regression Analysis". Acta Scientific Computer Sciences 3.1 (2021): 03-09.
Copyright: © 2021 Cheryl Ann Alexander and Lidong Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.