A hybrid deep learning method with attention for COVID-19 spread forecasting

Abdelkader Dairi, Fouzi Harrou, Ying Sun, Sofiane Khadraoui

Research output: Chapter in Book/Report/Conference proceedingChapter


This chapter introduces a hybrid deep learning model for COVID-19 spread forecasting. Specifically, the proposed approach combines the desirable characteristics of bidirectional long short-term memory (BiLSTM), convolutional neural networks (CNN), and an attention mechanism. Importantly, this combination, called BiLSTM-A-CNN, is intended to amalgamate the ability of LSTMs to model time dependencies, the capability of the attention mechanism to highlight relevant features, and the noted ability of CNNs to extract features from complex data. The use of the BiLSTM-A-CNN model is expected to improve the forecasting accuracy of future COVID-19 trends.
Original languageEnglish (US)
Title of host publicationArtificial Intelligence Strategies for Analyzing COVID-19 Pneumonia Lung Imaging, Volume 1
PublisherIOP Publishing
StatePublished - Apr 2022

Bibliographical note

KAUST Repository Item: Exported on 2022-04-20
Acknowledged KAUST grant number(s): OSR-2019-CRG7–3800
Acknowledgements: This chapter is based on work supported by the King Abdullah University of Science and Technology (KAUST), Office of Sponsored Research (OSR) under Award No: OSR-2019-CRG7–3800.


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