Machine learning applications for COVID-19: A state-of-the-art review

作     者：Kamalov, Firuz Cherukuri, Aswani Sulieman, Hana Thabtah, Fadi Hossain, Akbar 

作者机构：Department of Electrical Engineering Canadian University Dubai Dubai United Arab Emirates School of IT and Engineering Vellore Institute of Technology Vellore India Department of Mathematics and Statistics American University of Sharjah Sharjah United Arab Emirates School of Digital Technologies Manukau Institute of Technology Auckland New Zealand School of Engineering Auckland University of Technology Auckland New Zealand 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2021年

核心收录：

主　　题：Viruses 

摘      要：The COVID-19 pandemic has galvanized the machine learning community to create new solutions that can help in the fight against the virus. The body of literature related to applications of machine learning and artificial intelligence to COVID-19 is constantly growing. The goal of this article is to present the latest advances in machine learning research applied to COVID-19. We cover four major areas of research: forecasting, medical diagnostics, drug development, and contact tracing. We review and analyze the most successful state of the art studies. In contrast to other existing surveys on the subject, our article presents a high level overview of the current research that is sufficiently detailed to provide an informed insight. Copyright © 2021, The Authors. All rights reserved.