Analysis of Covid-19 daily results and information about patients using SQL and PowerBi

Sulejman Karamehic (1)
(1) International Burch University, Sarajevo, Bosnia and Herzegovina
Fulltext View | Download


COVID-19 is a contagious disease caused by a virus, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The first known case was identified in Wuhan, China, in December 2019. The disease quickly spread worldwide, resulting in the COVID-19 pandemic. The outburst of COVID-19 pandemic had tremendous effect on the whole world and analysis of the data can be meaningful in many ways to better understand the effect it had in our society. This paper aims in the direction of analysis of COVID-19 daily information based on country and continent level in terms of understanding the number of cases and deaths and their relationship, besides this is aims to better understand the vaccination number by country and effect of cases/death how they have affected these numbers. The solution was created on analysis of a dataset that contains daily information on each country, and using MySQL, SQL and PowerBi to generate the results for this work in way of query results which have been transformed to visuals using PowerBi for better understanding for further research work on this topic.

Article Details

How to Cite
S. Karamehic, “Analysis of Covid-19 daily results and information about patients using SQL and PowerBi”, Int. J. Data. Science., vol. 4, no. 1, pp. 1-9, May 2023.


W. Sirinaovakul, T. Eiamyingsakul, N. Tubtimtoe, S. Prom-on i Taetragool, “The Relations Between Implementation Date of Policies and The Spreading of COVID-19,” International Conference on Big Data Analytics and Practices (IBDAP), Bangkok.

Y. Zheng, “Estimation of Disease Transmission in Multimodal Transportation Networks,” Asian J. Public Opin.

W. He, J. Zhang i W. Li, “Information Technology Solutions, Challenges, and Suggestions for Tackling the COVID-19 Pandemic,” Int. J. Inf. Manag, 2020.

G. H. C. J. H. L. a. Y. R. Wang, “Data Analytics for the COVID-19,” IEEE 44th Annual Computers, Software, and Applications, 2020.

Z. Marmarelis, “Predictive Modeling of Covid-19 Data in the US: Adaptive Phase-Space Approach,” IEEE Open Journal of Engineering in Medicine and Biology.

Y. D.-R. S. &. S. Zoabi, “Machine learning-based prediction of COVID-19 diagnosis based on symptoms,” npj Digit. Med. 4, 3 .

D. A. Enis Kararslan, “A COVID 19 pandemic support for artificial intelligence and resource management systems,” e,ISBN 9780128245361, Academic Press.

C. f. S. S. a. E. (. a. J. H. U. (JHU), “Kaggle,” [Na mreži]. Available:

Oracle, “mysql,” [Na mreži]. Available:

NaN, “Wikipedia,” [Na mreži]. Available:

P. Loshin, “techtarget,” [Na mreži]. Available:

C. Academy, “CodeAcademy,” [Na mreži]. Available:

J. Scardina, “techtarget,” [Na mreži]. Available:

Microsoft, “,” [Na mreži]. Available: