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Data Visualization of Covid-19 and Associated Diseases

Udigala Basavaraju Mahadevaswamy *, Chandini Manjunath **, Shivani N. ***, Swathi P. ****

*-**** Department of Electronics and Communication Engineering, Sri Jayachamarajendra College of Engineering, Mysuru, Karnataka, India.

Periodicity:March - May'2021
DOI : https://doi.org/10.26634/jcom.9.1.18406

World Health Organization : COVID-19 - Global literature on coronavirus disease

https://pesquisa.bvsalud.org/global-literature-on-novel-coronavirus-2019-ncov/resource/en/covidwho-1638562

ProQuest Central | ID: covidwho-1638562

Abstract

The novel coronavirus disease (COVID-19) has currently affected millions of people, claiming more than 4,000,000 lives all over the world. Several dashboards have been created to analyze the present situation and get a better grasp of the current status of COVID-19. As the situation unfolded, infections caused by species of fungi, Mucormycosis (commonly called black fungus), have affected patients treated for COVID-19. Therefore, to facilitate information and to create awareness, it would be better to have a dashboard that display trends and data on COVID-19 and associated related diseases. In the proposed work, a dashboard has been created to visualize how COVID-19 epidemic has an impact in the global scenario. With the present work, the spread of diseases associated with COVID-19 (fungi variants) can be visualized. The data visualization is performed using Python. The tool kits and packages used for this purpose is Dash by Plotly. The acquired data is classified and filtered with interesting criteria in the ranging process stage. Using specific tools, data representations like line chart, bubble map, heat map, choropleths, tree map and, folium map are plotted to visualize the data.

Keywords

COVID-19, Black Fungus, Dashboard, Python, Plotly Dash, Visualization.

How to Cite this Article?

Mahadevaswamy, U. B., Manjunath, C., Shivani, N., and Swathi, P. (2021). Data Visualization of Covid-19 and Associated Diseases. i-manager's Journal on Computer Science, 9(1), 1-10. https://doi.org/10.26634/jcom.9.1.18406

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