Correlation study of New Cases, Deaths, Recoveries and Temperature with Machine Learning during COVID-19 spread in Saudi Arabia

Zafar Iqbal Khan¹ , Yasir Javed² , Khurram Naim Shmasi³

Section:Research Paper, Product Type: Journal-Paper
Vol.8 , Issue.3 , pp.1-5, Jun-2020

Online published on Jun 30, 2020

Copyright © Zafar Iqbal Khan, Yasir Javed, Khurram Naim Shmasi . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
Millions of people have been infected and killed by the recent outbreak of novel coronavirus throughout the world. It has affected 210 countries around the globe and two International conveyances [1]. It has evolved from epidemic to pandemic crossing all physical, socio economic and geographic barriers. The untraceable virus mutations can quickly effect hundreds of people before the antibodies are developed by the immune system. Since its first inception in Wuhan, the virus has rapidly influenced all nooks and corners of the tightly connected world. The imperative lethality of the virus varies from hot temperature to cold climates. There have been different impacts of COVID-19 on different races, geographical conditions and socio-cultural environments. It is evident that temperature is a critical factor for incubation period of pathogens. This research paper tries to find out the correlation among various factors such as infections, deaths, recoveries of the patients infected with COVID-19 with respect to the Temperature with the help of k-means clustering

Key-Words / Index Term :
Coronavirus, COVID-19, k-means, clustering, Machine Learning, correlation

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