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  Data Analytics and Visualization Techniques of Corona Impact  
  Authors : Abhishek Parikh; Sandeep Shah; Vishvam Bhatt
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The world right now is facing a pandemic due to novel coronavirus disease (nCOVID) or coronavirus disease -19 (COVID-19). A good explanatory visualization of available dataset will provide insights to understand the behavior of pandemic. In this paper we have worked upon descriptive analysis from the COVID dataset and tried to resolve following problems 1. Behavior of mortality and recovery rate with respect to age and sex 2. Comparison trends for confirmed, active, deaths and recovered cases around the world 3. To Map logarithmic number of cases on World and Indian map. We have taken around 1.5 million points to plot the graphs over the data analytic tools based on python and Knime.


Published In : IJCSN Journal Volume 9, Issue 4

Date of Publication : August 2020

Pages : 147-152

Figures :17

Tables : --


Abhishek Parikh : received B.E. (Electronics and Communication) from GTU University Ahmedabad in 2014, M.E. (Microprocessor System and Application) from MSU University Baroda in 2016 and pursuing his Ph.D. in bio-medical signal processing from GTU. He has got 5 years of working experience as Product Development Lead in product engineering services at Optimized Solutions Limited.

Sandeep Shah : received his B.E. (Instrumentation and Control) from GU in 2001 and PGDM from IIM Calcutta. He is currently working as Managing Director of Optimized Solutions Limited, Ahmedabad. His area of interest is Monitoring, Automation and Data acquisition systems.

Vishvam Bhatt : received his B.C.A. from DDIT in 2018 and M. Sc. In Information and Technology from DAIICT Gandhinagar. His area of interest is Data Analytics and Big Data visualization. He is currently working as Data Scientist at Optimized Solutions Limited.


Data Analytic, Python, Knime, Coronavirus visualization, descriptive analytic, machine learning prediction, Linear regression

For representation over here we have taken 20 as a bracket and plotted the histograms. Histogram comparison of the recovered and deceased patient shown in figure 16 and 17 proves that "Elder patients have less chances of recovery if affected by Coronavirus" in other language we can say that "Coronavirus affected Elder patients have more risk of death" Hence we have related the age group with death cases. As in certain areas of the world cases have been significantly reduced future scope of the data visualization is to have predictions on when and where Coronavirus would stop and which countries should care more from now.


[1] Worldometers website charts and data points https://www.worldometers.info/coronavirus/#countries [2] Khanam, Fahima & Nowrin, Itisha. Data Visualization and Analyzation of COVID-19. Journal of Scientific Research and Reports.26-3 (2020) pp. 42-52. [3] Randhawa GS, Soltysiak MP, El Roz H, de Souza CP, Hill KA, Kari L. Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study. Biorxiv; 2020. [4] World Health Organization. Middle East respiratory syndrome coronavirus (MERS- CoV). Available:https://www.who.int/emergencies /mers-cov/en. [5] Covid 19 Dataset references https://www.kaggle.com/covid19 [6] Indian Patient Coronavirus database https://www.covid19india.org/ [7] Yi W, Wang Y, Tang J, Xiong X, Zhang Y, Yan S. Zhonghua Wei Zhong Bing Ji Jiu Yi Xue. 2020;32(3):279286. doi:10.3760/cma.j.cn121430-20200225-00200 [8] Data science blog on spread of Covid-19 https://towardsdatascience.com/the-impact-of-covid-19-data-analysis-and-visualization-560e54262dc