Data‐driven analysis and predictive modeling on COVID‐19. (11th November 2022)

• Record Type: Journal Article
• Authors: Sharma, Sonam; Alsmadi, Izzat; Alkhawaldeh, Rami S.; Al‐Ahmad, Bilal
• Is Part Of: Concurrency and computation. Volume 34:Number 28(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-11-11
• Subjects: COVID‐19 -- gender of patients -- global growth rate -- predictive modeling -- social distancing; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.7390
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: The coronavirus (COVID‐19) started in China in 2019, has spread rapidly in every single country and has spread in millions of cases worldwide. This paper presents a proposed approach that involves identifying the relative impact of COVID‐19 on a specific gender, the mortality rate in specific age, investigating different safety measures adopted by each country and their impact on the virus growth rate. Our study proposes data‐driven analysis and prediction modeling by investigating three aspects of the pandemic (gender of patients, global growth rate, and social distancing). Several machine learning and ensemble models have been used and compared to obtain the best accuracy. Experiments have been demonstrated on three large public datasets. The motivation of this study is to propose an analytical machine learning based model to explore three significant aspects of COVID‐19 pandemic as gender, global growth rate, and social distancing. The proposed analytical model includes classic classifiers, distinctive ensemble methods such as bagging, feature based ensemble, voting and stacking. The results show a superior prediction performance comparing with the related approaches.