Time Series Analysis with Systematic Survey on Covid-19 Based Predictive Studies During Pandemic Period using Enhanced Machine Learning Techniques

Abstract

Coronavirus 2 virus is responsible for the spread of the infectious disease COVID-19 (also known as Coronavirus disease). People around the globe who got infected with the virus experienced a respiratory illness that could become as serious as leading someone to lose their life. However, the upside of the pandemic is that it has led to numerous types of research and explorations, majorly in the medical science field. Since a systematic survey of previous research activities and bibliometric analysis gives a brief idea about such contributions and acts as a reference to future research, this study aims to cover the research related to COVID-19 in the computer technology domain. It is limited to the works accepted and accessible with the keywords - Covid-19, prediction, and pandemic, in the Scopus search engine to justify the scope of this survey. Further, the paper highlights a few prior works used for predictive analysis and presents a quantitative angle on their algorithms. Earlier works showcase Time Series Analysis using ARIMA/SARIMA models for predicting the vaccination rates, and Extreme Gradient Boosting (XGBoost), Xtremely Boosted Network (XBNet) Regression, and Recurrent Neural Network (RNN) for Confirmed, Cured, and Death cases. Amongst the algorithms used in the latter use case, XBNet regression performed better than XGBoost regressor.