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COVID-19 time series forecast using transmission rate and meteorological parameters as features


Mousavi, M and Salgotra, R and Holloway, D and Gandomi, AH, COVID-19 time series forecast using transmission rate and meteorological parameters as features, IEEE Computational Intelligence Magazine, 15, (4) pp. 34-50. ISSN 1556-603X (2020) [Refereed Article]

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Copyright 2020 IEEE. This article is free to access and download, along with rights for full text and data mining, re-use and analysis

DOI: doi:10.1109/MCI.2020.3019895


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 Details

Item Type:Refereed Article
Keywords:COVID, forecasting, transmission rate, RNN, recurrent neural networks
Research Division:Mathematical Sciences
Research Group:Applied mathematics
Research Field:Applied mathematics not elsewhere classified
Objective Division:Health
Objective Group:Clinical health
Objective Field:Clinical health not elsewhere classified
UTAS Author:Mousavi, M (Mr Mohsen Mousavi)
UTAS Author:Holloway, D (Associate Professor Damien Holloway)
ID Code:141662
Year Published:2020
Web of Science® Times Cited:1
Deposited By:Engineering
Deposited On:2020-11-08
Last Modified:2021-02-18

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