A Comparative Analysis of COVID Forecasting by Using Various Machine Learning Methods

  • Jamaluddin Mir Faculty of Computer Science & Information Technology (FSKTM), Universiti Tun Hussein Onn Malaysia (UTHM), 86400 Parit Raja, Batu Pahat, Johor, Malaysia
Keywords: Covid-19, forecasting, machine learning, Support Vector Machine, prediction


Covid-19 emerged as one of the most infectious diseases in the history of mankind, affecting nearly 250 million people all over the world in just a short period. The pandemic which started in China, has now spread all over the world, taking about 5 million lives globally. This has also severely affected the economies of countries and has proved to be a burden on 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 spread and plan health care resources efficiently and properly. This makes it more important to have a reliable forecast so that resources can be planned 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.  

How to Cite
Jamaluddin Mir. (2022). A Comparative Analysis of COVID Forecasting by Using Various Machine Learning Methods. Lahore Garrison University Research Journal of Computer Science and Information Technology, 6(1), 69-80. https://doi.org/10.54692/lgurjcsit.2022.0601278