COVID-19 Pandemic Data Visualization with Moment about Midpoint: Exploratory and Expository Analyses
Stephen Olusegun Are, Matthew Iwada Ekum · Asian Journal of Probability and Statistics · 2020
Aims: To visualize COVID-19 data using Exploratory Data Analysis (EDA) to tell the COVID-19 story expository. Study Design: The study uses EDA approach to visualize the COVID-19 data. The study uses secondary data collected from World Health Organization (WHO) in a panel form and partition the world using WHO regions. Moment about a midpoint and EDA are jointly used to analyze the data. Place and Duration of Study: Department of Mathematics & Statistics, Statistical Laboratory, Lagos State Polytechnic and Federal Polytechnic, Ilaro. The data used covered all regions of the world from January 2020 to July 2020. Methodology: We included 198 countries (cross-sections) partitioned into 7 WHO regions over 7 months (190 days) time period, spanning 3000 datasets. The EDA and moment about a midpoint is used for the analysis. This is a purely descriptive and expository analysis to tell the story of the novel coronavirus disease (COVID-19). Results: The total sample points used for this analysis are 30,010, which can be taken as a big data and it is large enough to assume the central limit theorem. The results of the analysis showed that cumulative cases and deaths are increasing but at a slower rate. Some WHO region curves are already flattening. Conclusion: The study concluded that average number of new cases and new deaths will decrease in coming months but there will be increase in the cumulative cases and deaths but at a slower rate.