Machine learning applications to COVID-19: a state-of-the-art survey

Kamalov, FiruzCherukuri, Aswani KumarThabtah, Fadi2022-05-222022-05-22© 20222022Kamalov, F., Cherukuri, A. K., & Thabtah, F. (2022). Machine learning applications to COVID-19: A state-of-the-art survey. 2022 Advances in Science and Engineering Technology International Conferences (ASET). https://doi.org/10.1109/ASET53988.2022.9734959978-166541801-0https://doi.org/10.1109/ASET53988.2022.9734959http://hdl.handle.net/20.500.12519/651There exists a large and rapidly growing body of literature related to applications of machine learning to Covid-19. Given the substantial volume of research, there is a need to organize and categorize the literature. In this paper, we provide the most up-to-date review as of the beginning of 2022. We propose an application-based taxonomy to group the existing literature and provide an analysis of the research in each category. We discuss the progress as well as the pitfalls of the existing research, and propose keys for improvement. © 2022 IEEE.en-USCNNCovid-19deep learningLSTMmachine learningreviewMachine learning applications to COVID-19: a state-of-the-art surveyConference PaperCopyright : © 2022 IEEE.