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Data Analytics on the COVID-19 Outbreak in South Asia using Machine Learning Methods

Journal: International Journal of Advanced Trends in Computer Science and Engineering (IJATCSE) (Vol.10, No. 4)

Publication Date:

Authors : ;

Page : 2784-2791

Keywords : COVID-19; data analytics; K-Means clustering; polynomial regression; predictive model; SAARC.;

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The transformation of COVID-19 spread from the first case reported in China's Wuhan to a worldwide pandemic has been a tremendous topic of study among data analysts and scientists alike. In South Asia, this pandemic has brought about a disaster in the lives and livelihoods of most of its inhabitants. An exploratory analysis of the overall COVID- 19 data provided by the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE) is presented in this paper. The number of confirmed, death, recovered, and active cases as recent as July 18, 2021, have been explored with machine learning data analytics methods to draw crucial conclusions about the pandemic. The analytics and predictive modelingare performed in the context of the cases in the SAARC (South Asian Association of Regional Cooperation) countries. The calculated correlation coefficient demonstrates that the countries with higher GDP Per Capita have conducted more tests/1M population. To find and compare the severity of the pandemic, the countries are grouped based on the K-Means clustering algorithm. The confirmed and death cases are modeled with the polynomial regression technique and the future evolution of the pandemic is predicted with good accuracy. Based on the predictive model, the total cases estimate around 42.91 million confirmed and 0.58 million deaths till August 17, 2021.

Last modified: 2021-08-10 17:52:54