A REVIEW ON EXTENSIVELY USED MACHINE LEARNING TECHNIQUES FOR THE PREDICTION OF COVID-19

Abstract

Forecasting with a precise evaluation of new cases and the rate of occurrence is essential for the effective implementation of governmental initiatives and early prevention of any infectious illness. Despite the extensive research done on the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus since the outbreak, not enough knowledge could be gained about the virus in terms of immune function, virus-host interactions, pathogenesis, propagation, and mutations. In this paper, various statistical models, namely Supervised Machine Learning techniques (ML), are being discussed for previous diseases and for the recent COVID-19 pandemic. Namely, the use of the Support Vector Machine (SVM) model and a variety of time series regression models is demonstrated for several infectious diseases, including COVID-19. As infectious diseases evolve throughout time, they provide data on a single variable, that is, “the figure of contaminations that occurred over time”; thus, researchers tend to use time series models to fit the data and make predictions using different evaluation metrics to find the best-fitting model. This review developed ideas about how to enhance the current modeling techniques. Furthermore, findings of current Machine Learning Techniques are being evaluated, which attempts to estimate the COVID-19 spread. Researchers looking for approaches to advance SARS-CoV-2 research as well as individuals curious about the field’s current condition will find this review to be helpful.