Machine learning research towards combating COVID-19: Virus detection, spread prevention, and medical assistance

Authors

Osama Shahid, Kennesaw State University
Mohammad Nasajpour, Kennesaw State University
Seyedamin Pouriyeh, Department of Information Technology, Kennesaw State University, Marietta, GA, USA. Electronic address: spouriye@kennesaw.edu.
Reza M. Parizi, Department of Software Engineering and Game Development, Kennesaw State University, Marietta, GA, USA. Electronic address: rparizi1@kennesaw.edu.
Meng Han, Department of Information Technology, Kennesaw State University, Marietta, GA, USA. Electronic address: mhan9@kennesaw.edu.
Maria Valero, Department of Information Technology, Kennesaw State University, Marietta, GA, USA. Electronic address: mvalero2@kennesaw.edu.
Fangyu Li, Department of Electrical and Computer Engineering, Kennesaw State University, Marietta, GA, USA. Electronic address: fli6@kennesaw.edu.
Mohammed Aledhari, Department of Computer Science, Kennesaw State University, Marietta, GA, USA. Electronic address: maledhar@kennesaw.edu.
Quan Z. Sheng, Department of Computing, Macquarie University, Sydney, Australia. Electronic address: michael.sheng@mq.edu.au.

Department

Information Technology

Document Type

Article

Publication Date

5-1-2021

Abstract

COVID-19 was first discovered in December 2019 and has continued to rapidly spread across countries worldwide infecting thousands and millions of people. The virus is deadly, and people who are suffering from prior illnesses or are older than the age of 60 are at a higher risk of mortality. Medicine and Healthcare industries have surged towards finding a cure, and different policies have been amended to mitigate the spread of the virus. While Machine Learning (ML) methods have been widely used in other domains, there is now a high demand for ML-aided diagnosis systems for screening, tracking, predicting the spread of COVID-19 and finding a cure against it. In this paper, we present a journey of what role ML has played so far in combating the virus, mainly looking at it from a screening, forecasting, and vaccine perspective. We present a comprehensive survey of the ML algorithms and models that can be used on this expedition and aid with battling the virus.

Journal Title

Journal of biomedical informatics

Volume

117

First Page

103751

Digital Object Identifier (DOI)

10.1016/j.jbi.2021.103751