Estimating COVID-19 Cases Using Machine Learning Regression Algorithms

Abstract

Background: Coronavirus refers to a large group of RNA viruses that infects the respiratory tract in humans and also causes diseases in birds and mammals. SARS-CoV-2 gives rise to the infectious disease “COVID-19”. In March 2020, coronavirus was declared a pandemic by the WHO. The transmission rate of COVID-19 has been increasing rapidly; thus, it becomes indispensable to estimate the number of confirmed infected cases in the future. Objective: The study aims to forecast coronavirus cases using three ML algorithms, viz., support vector regression (SVR), polynomial regression (PR), and Bayesian ridge regression (BRR). Methods: There are several ML algorithms like decision tree, K-nearest neighbor algorithm, Random forest, neural networks, and Naïve Bayes, but we have chosen PR, SVR, and BRR as they have many advantages in comparison to other algorithms. SVM is a widely used supervised ML algorithm developed by Vapnik and Cortes in 1990. It is used for both classification and regression. PR is known as a particular case of Multiple Linear Regression in Machine Learning. It models the relationship between an independent and dependent variable as nth degree polynomial. Results: In this study, we have predicted the number of infected confirmed cases using three ML algorithms, viz. SVR, PR, and BRR. We have assumed that there are no precautionary measures in place. Conclusion: In this paper, predictions are made for the upcoming number of infected confirmed cases by analyzing datasets containing information about the day-wise past confirmed cases using ML models (SVR, PR and BRR). According to this paper, as compared to SVR and PR, BRR performed far better in the future forecasting of the infected confirmed cases owing to coronavirus.