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

journal contribution
posted on 2023-05-20, 19:07 authored by Mousavimirkalaei, M, Salgotra, R, Damien HollowayDamien Holloway, Gandomi, AH
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.

History

Publication title

IEEE Computational Intelligence Magazine

Volume

15

Issue

4

Pagination

34-50

ISSN

1556-603X

Department/School

School of Engineering

Publisher

IEEE

Place of publication

United States

Rights statement

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

Repository Status

  • Restricted

Socio-economic Objectives

Clinical health not elsewhere classified

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