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

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.

preprint2021arXivOpen access
Firuz KamalovAswani CherukuriHana SuliemanFadi ThabtahAkbar Hossain
Machine Learning
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Open access5 authors1 topic
Imported metadata coverageMissing code, dataset, citation and institution fields are tracked without dominating the paper.Details
Citations: 0Reviews: 0Saves: 0Code: not linkedDataset: not linkedInstitutions: 0

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Firuz KamalovAswani CherukuriHana SuliemanFadi ThabtahAkbar Hossain

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