Modelling of COVID-19 spread time and mortality rate using machine learning techniques Online publication date: Fri, 09-Jun-2023
by Ahmad Arrabi; Amjed Al-Mousa
International Journal of Intelligent Information and Database Systems (IJIIDS), Vol. 16, No. 2, 2023
Abstract: One of the main issues in dealing with the COVID-19 global pandemic is that governments cannot predict the time it spreads or the mortality rate. If known, these two factors would have helped governments take appropriate measures without being excessively cautious and negatively impacting populations' mental health and economic outcomes. This paper presents a machine learning (ML)-based model that helps assess the rate at which the virus spreads in a country as well as the mortality based on multiple health, social, economic, and political factors. The method predicts how long a country's cases take to reach 5%, 10%, 15%, and 20% of its population. The prediction was conducted by regularised linear regression models and support vector machine regression (SVR). The SVR model achieved the highest median accuracy of 97%. Meanwhile, the ridge regression model achieved the best median accuracy of 84% for predicting the mortality rate.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Intelligent Information and Database Systems (IJIIDS):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook