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Predicting COVID-19 cases using bidirectional LSTM on multivariate time series

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posted on 2022-11-22, 21:14 authored by Ahmed Ben Said, Abdelkarim Erradi, Hussein Ahmed Aly, Abdelmonem Mohamed

To assist policymakers in making adequate decisions to stop the spread of the COVID-19 pandemic, accurate forecasting of the disease propagation is of paramount importance. This paper presents a deep learning approach to forecast the cumulative number of COVID-19 cases using bidirectional Long Short-Term Memory (Bi-LSTM) network applied to multivariate time series. Unlike other forecasting techniques, our proposed approach first groups the countries having similar demographic and socioeconomic aspects and health sector indicators using K-means clustering algorithm. The cumulative case data of the clustered countries enriched with data related to the lockdown measures are fed to the bidirectional LSTM to train the forecasting model. We validate the effectiveness of the proposed approach by studying the disease outbreak in Qatar and the proposed model prediction from December 1st until December 31st, 2020. The quantitative evaluation shows that the proposed technique outperforms state-of-art forecasting approaches.

Other Information

Published in: Environmental Science and Pollution Research
License: https://creativecommons.org/licenses/by/4.0
See article on publisher's website: http://dx.doi.org/10.1007/s11356-021-14286-7

History

Language

  • English

Publisher

Springer Science and Business Media LLC

Publication Year

  • 2021

Institution affiliated with

  • Qatar University

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