Abstract: The coronavirus disease (COVID-19) is a global pandemic that was discovered by a Chinese physician in Wuhan, the capital city of Hubei province in mainland China, in December 2019. Visualization techniques have been front-and-center in the efforts to communicate the science around COVID-19 to the very broad audience of policy makers, scientists, healthcare providers, and the general public. In particular, the project is focused on visualizing live COVID-19 trends. In this paper, the authors develop a data visualization module that specializes in improvising current COVID-19 Data Visualization Techniques and providing methodologies which enhance viewer-friendliness and boost visual clarity. The module uses authentic sources of data, like the John Hopkins University Covid GitHub Repository, to develop accurate, real-time visualizations which erase discrepancies in visualizations due to inaccurate and irregular streams of data. The authors suggest alternative data visualization techniques to represent data which has the potential of being visualized better than it currently is. Various data visualization libraries such as Plotly, Matplotlib, Seaborn, Ggplot2, GeoPlotlib, etc. have been used to create a variety of visualizations such as scatterplots, bubble charts, histograms, boxplots, distplots, heatmaps, choropleth maps, etc.

Keywords – Coronavirus, COVID-19, Data Science, Data Visualization, Modeling, Pandemic, Simulation, Statistics

PDF | DOI: 10.17148/IJARCCE.2021.105120

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