COVID-19 Time Series Forecast Using Transmission Rate and Meteorological Parameters as Features

Mousavi, M; Salgotra, R; Holloway, D; Gandomi, AH

COVID-19 Time Series Forecast Using Transmission Rate and Meteorological Parameters as Features

Mousavi, M Salgotra, R Holloway, D Gandomi, AH

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Computational Intelligence Magazine, 2020, 15, (4), pp. 34-50
Issue Date:: 2020-11-01

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Field	Value	Language
dc.contributor.author	Mousavi, M
dc.contributor.author	Salgotra, R
dc.contributor.author	Holloway, D
dc.contributor.author	Gandomi, AH
dc.date.accessioned	2020-11-22T21:12:26Z
dc.date.available	2020-11-22T21:12:26Z
dc.date.issued	2020-11-01
dc.identifier.citation	IEEE Computational Intelligence Magazine, 2020, 15, (4), pp. 34-50
dc.identifier.issn	1556-603X
dc.identifier.issn	1556-6048
dc.identifier.uri	http://hdl.handle.net/10453/144227
dc.description.abstract	© 2005-2012 IEEE. 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.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Computational Intelligence Magazine
dc.relation.isbasedon	10.1109/MCI.2020.3019895
dc.rights	info:eu-repo/semantics/closedAccess
dc.rights	© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.subject	0906 Electrical and Electronic Engineering
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	COVID-19 Time Series Forecast Using Transmission Rate and Meteorological Parameters as Features
dc.type	Journal Article
utslib.citation.volume	15
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/A/DRsch The Data Science Institute
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
dc.date.updated	2020-11-22T21:12:22Z
pubs.issue	4
pubs.publication-status	Published
pubs.volume	15
utslib.citation.issue	4

Abstract:

© 2005-2012 IEEE. 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.

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http://hdl.handle.net/10453/144227