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Abstract

The number of confirmed cases of COVID-19 has been ever increasing worldwide since its outbreak in Wuhan, China. As such, many researchers have sought to predict the dynamics of the virus spread in different parts of the globe. In this paper, a novel systematic platform for prediction of the future number of confirmed cases of COVID-19 is proposed, based on several factors such as transmission rate, temperature, and humidity. The proposed strategy derives systematically a set of appropriate features for training Recurrent Neural Networks (RNN). To that end, the number of confirmed cases (CC) of COVID-19 in three states of India (Maharashtra, Tamil Nadu and Gujarat) is taken as a case study. It has been noted that stationary and nonstationary parts of the features improved the prediction of the stationary and non-stationary trends of the number of confirmed cases, respectively. The new platform has general application and can be used for pandemic time series forecasting.

Item Type:	Article
Authors/Creators:	Mousavi, M and Salgotra, R and Holloway, D and Gandomi, AH
Keywords:	COVID, forecasting, transmission rate, RNN, recurrent neural networks
Journal or Publication Title:	IEEE Computational Intelligence Magazine
Publisher:	IEEE
ISSN:	1556-603X
DOI / ID Number:	10.1109/MCI.2020.3019895
Copyright Information:	Copyright 2020 IEEE. This article is free to access and download, along with rights for full text and data mining, re-use and analysis
Related URLs:	Google Scholar: Holloway, D UTAS Profile: Holloway, D
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