Modelling of COVID-19 spread time and mortality rate using machine learning techniques. (5th June 2023)

• Record Type: Journal Article
• Authors: Arrabi, Ahmad; Al-Mousa, Amjed
• Is Part Of: International journal of intelligent information and database systems. Volume 16:Number 2(2023)
• Page Start: 143
• Page End: 166
• Publication Date: 2023-06-05
• Subjects: COVID-19 -- machine learning -- support vector machine regression -- SVR -- kernel -- regularised linear regression -- leave one out cross-validation -- LOOCV; Database management -- Computer programs -- Periodicals; Information retrieval -- Computer programs -- Periodicals; Information storage and retrieval systems -- Computer programs -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligent agents (Computer software) -- Periodicals; 006.33
• Languages: English
• ISSNs: 1751-5858

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.