A comparative analysis of covid forecasting by using various machine learning methods | [Lahore Garrison University Research Journal of Computer Science and Information Technology • 2022]

Author(s):

1. Jamaluddin Mir: Faculty of Computer Science & Information Technology (FSKTM), Universiti Tun Hussein Onn Malaysia (UTHM), 86400 Parit Raja, Batu Pahat, Johor, Malaysia

2. Ahsan Zubair: Department of Computer Science, Abbottabad University of Science & Technology (AUST), Havelian, Pakistan

3. Ayesha Aslam: Department of Computer Science, Abbottabad University of Science & Technology (AUST), Havelian, Pakistan

4. Asim Shahzad: Department of Computer Science, Abbottabad University of Science & Technology (AUST), Havelian, Pakistan

5. Muhammad Arshad: Department of Computer Science, Abbottabad University of Science & Technology (AUST), Havelian, Pakistan

6. Aamer Khan: Department of Computer Science, Abbottabad University of Science & Technology (AUST), Havelian, Pakistan

Abstract:

Covid-19 emerged as one of the most infectious diseases in the history of humankind, affecting nearly 250 million people worldwide in just a short period. The pandemic, which started in China, has now spread worldwide, taking about 5 million lives globally. This has also severely affected the countries' economies and has burdened health care systems. Due to these reasons, forecasting the spread of the disease has become critical so that concerned government authorities in countries can have the chance to mitigate the space and plan healthcare resources efficiently and adequately. This makes it more important to have a reliable forecast to plan resources ahead of time. In the present work, linear regression is used for time forecasting the spread of Covid-19 in Pakistan. Statistical parameters and metrics have been used to evaluate and validate the model. The results show that linear regression results are highly reliable, time efficient and accurate.

Page(s): 37-43

DOI: 10.54692/lgurjcsit.2022.0601278

Published: Journal: Lahore Garrison University Research Journal of Computer Science and Information Technology, Volume: 6, Issue: 1, Year: 2022

Keywords:

machine learning , COVID19 , Support Vector Machine , forecasting , Prediction , Covid19

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