Modelling of COVID-19 spread time and mortality rate using machine learning techniques

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.