Analytical and Predictive Model for the impact of social distancing on COVID-19 pandemic

ABSTRACT

The coronavirus (COVID-19) as in the study of which had a starting point in China in 2019, has spread rapidly in every single country and has spread in millions of cases. The pandemic attracts lots of attentions due to major impacts not only on human health but on many other aspects including, social and political ones. This paper presents a robust data-driven machine learning analysis of COVID19 starting from data collection to the final step of knowledge extraction based on the selected research topics. The proposed approach evaluates the impact of social distancing on COVID19. Several machine learning and ensemble models have been used and compared to obtain the best accuracy. Experiments have been demonstrated on large public datasets. The motivation of this study is to propose an analytical machine learning based model to explore the social distancing aspects of COVID-19 pandemic. The proposed analytical model includes classic classifiers, distinctive ensemble methods such as bagging, feature based ensemble, voting and stacking. Also, it uses different Python libraries, Rattle, RStudio, Anaconda, and Jupyter Notebook. This study shows superior prediction performance comparing with the related approaches and the classical machine learning approaches. © 2022 IEEE.