ISSN: 0973-7510

E-ISSN: 2581-690X

Research Article | Open Access
Mohammad Khubeb Siddiqui1 , Ruben Morales-Menendez1, Pradeep Kumar Gupta2, Hafiz M.N. Iqbal1, Fida Hussain1, Khudeja Khatoon3 and Sultan Ahmad4
1School of Engineering and Sciences, Tecnologico de Monterrey, Av E. Garza Sada # 2501, Monterrey, NL, 64849, Mexico.
2Department of Computer Science and Engineering, Jaypee University of Information Technology, India.
3Department of Pharmacology, Hayat Unani Medical College & Research Center, India.
4College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Saudi Arabia.
J Pure Appl Microbiol. 2020;14(suppl 1):1017-1024 | Article Number: 6201
Received: 30/03/2020 | Accepted: 04/04/2020 | Published: 16/04/2020

Currently, the whole world is struggling with the biggest health problem COVID-19 name coined by the World Health Organization (WHO). This was raised from China in December 2019. This pandemic is going to change the world. Due to its communicable nature, it is contagious to both medically and economically. Though different contributing factors are not known yet. Herein, an effort has been made to find the correlation between temperature and different cases situation (suspected, confirmed, and death cases). For a said purpose, k-means clustering-based machine learning method has been employed on the data set from different regions of China, which has been obtained from the WHO. The novelty of this work is that we have included the temperature field in the original WHO data set and further explore the trends. The trends show the effect of temperature on each region in three different perspectives of COVID-19 – suspected, confirmed and death.


Coronavirus, COVID-19, Machine Learning, k-means Clustering

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