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

• URL: http://arxiv.org/abs/2101.07824v1
• Date: Tue, 19 Jan 2021 19:12:45 GMT
• Title: Machine learning applications for COVID-19: A state-of-the-art review
• Authors: Firuz Kamalov, Aswani Cherukuri, Hana Sulieman, Fadi Thabtah, Akbar Hossain
• Abstract summary: The COVID-19 pandemic has galvanized the machine learning community to create new solutions. This article presents the latest advances in machine learning research applied to COVID-19.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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