Deep Learning Based Forecasting of COVID-19 Hospitalisation in England: A Comparative Analysis

Michael Ajao-olarinoye; Vasile Palade; Seyed Mousavi; Fei He; Petra A Wark

doi:10.1109/ICMLA58977.2023.00203

Deep Learning Based Forecasting of COVID-19 Hospitalisation in England: A Comparative Analysis

Michael Ajao-olarinoye, Vasile Palade, Seyed Mousavi, Fei He, Petra A Wark

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding › peer-review

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Abstract

In the midst of the COVID-19 pandemic, it was essential to accurately forecast the demand for hospitalisation resources to achieve an effective allocation of healthcare resources. This paper explores the potential of various Deep Learning (DL) models, namely basic Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs), Gated Recurrent Units (GRU), Bidirectional RNNs, and Sequence-to-Sequence architectures with the inclusion of attention mechanisms, to forecast the demand for hospitalisation resources (mechanical ventilators) in England during the COVID-19 pandemic. The implementation of simulated annealing (SA) as a hyperparameter tuning method produced certain model structures and good results in terms of prediction accuracy. Our findings show that the LSTM-based models (LSTM_SA), achieved the lowest mean average error (MAE), outperforming other architectures used in this study. The results of this study show the potential of DL models to forecast the demand for resources and could help inform the distribution of hospitalisation resources in England during the COVID-19 pandemic.

Original language	English
Title of host publication	2023 International Conference on Machine Learning and Applications (ICMLA)
Editors	M. Arif Wani, Mihai Boicu, Moamar Sayed-Mouchaweh, Pedro Henriques Abreu, Joao Gama
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1344-1349
Number of pages	6
ISBN (Electronic)	9798350345346
ISBN (Print)	9798350318913
DOIs	https://doi.org/10.1109/ICMLA58977.2023.00203
Publication status	E-pub ahead of print - 19 Mar 2024
Event	22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023 - Jacksonville, United States Duration: 15 Dec 2023 → 17 Dec 2023 Conference number: ICMLA https://www.icmla-conference.org/icmla23/

Publication series

Name	2023 International Conference on Machine Learning and Applications (ICMLA)
Publisher	IEEE

Conference

Conference	22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023
Country/Territory	United States
City	Jacksonville
Period	15/12/23 → 17/12/23
Internet address	https://www.icmla-conference.org/icmla23/

Bibliographical note

© 2024 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.

Copyright © and Moral Rights are retained by the author(s) and/ or other copyright owners. A copy can be downloaded for personal non-commercial research or study, without prior permission or charge. This item cannot be reproduced or quoted extensively from without first obtaining permission in writing from the copyright holder(s). The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the copyright holders.

This document is the author’s post-print version, incorporating any revisions agreed during the peer-review process. Some differences between the published version and this version may remain and you are advised to consult the published version if you wish to cite from it.

Keywords

Attention mechanism
COVID-19
Deep learning
GRU
Hospitalisation forecasting
LSTM
RNN

ASJC Scopus subject areas

Artificial Intelligence
Computer Science Applications
Computer Vision and Pattern Recognition

Access to Document

10.1109/ICMLA58977.2023.00203

Olarinoye2024AAM Accepted author manuscript, 853 KB

Cite this

Ajao-olarinoye, M., Palade, V., Mousavi, S., He, F., & Wark, P. A. (2024). Deep Learning Based Forecasting of COVID-19 Hospitalisation in England: A Comparative Analysis. In M. Arif Wani, M. Boicu, M. Sayed-Mouchaweh, P. H. Abreu, & J. Gama (Eds.), 2023 International Conference on Machine Learning and Applications (ICMLA) (pp. 1344-1349). (2023 International Conference on Machine Learning and Applications (ICMLA)). Institute of Electrical and Electronics Engineers Inc.. Advance online publication. https://doi.org/10.1109/ICMLA58977.2023.00203

Deep Learning Based Forecasting of COVID-19 Hospitalisation in England: A Comparative Analysis. / Ajao-olarinoye, Michael ; Palade, Vasile; Mousavi, Seyed et al.
2023 International Conference on Machine Learning and Applications (ICMLA). ed. / M. Arif Wani; Mihai Boicu; Moamar Sayed-Mouchaweh; Pedro Henriques Abreu; Joao Gama. Institute of Electrical and Electronics Engineers Inc., 2024. p. 1344-1349 (2023 International Conference on Machine Learning and Applications (ICMLA)).

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding › peer-review

Ajao-olarinoye, M , Palade, V, Mousavi, S, He, F & Wark, PA 2024, Deep Learning Based Forecasting of COVID-19 Hospitalisation in England: A Comparative Analysis. in M Arif Wani, M Boicu, M Sayed-Mouchaweh, PH Abreu & J Gama (eds), 2023 International Conference on Machine Learning and Applications (ICMLA). 2023 International Conference on Machine Learning and Applications (ICMLA), Institute of Electrical and Electronics Engineers Inc., pp. 1344-1349, 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023, Jacksonville, Florida, United States, 15/12/23. https://doi.org/10.1109/ICMLA58977.2023.00203

Ajao-olarinoye M , Palade V, Mousavi S, He F , Wark PA. Deep Learning Based Forecasting of COVID-19 Hospitalisation in England: A Comparative Analysis. In Arif Wani M, Boicu M, Sayed-Mouchaweh M, Abreu PH, Gama J, editors, 2023 International Conference on Machine Learning and Applications (ICMLA). Institute of Electrical and Electronics Engineers Inc. 2024. p. 1344-1349. (2023 International Conference on Machine Learning and Applications (ICMLA)). Epub 2024 Mar 19. doi: 10.1109/ICMLA58977.2023.00203

Ajao-olarinoye, Michael ; Palade, Vasile ; Mousavi, Seyed et al. / Deep Learning Based Forecasting of COVID-19 Hospitalisation in England : A Comparative Analysis. 2023 International Conference on Machine Learning and Applications (ICMLA). editor / M. Arif Wani ; Mihai Boicu ; Moamar Sayed-Mouchaweh ; Pedro Henriques Abreu ; Joao Gama. Institute of Electrical and Electronics Engineers Inc., 2024. pp. 1344-1349 (2023 International Conference on Machine Learning and Applications (ICMLA)).

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Deep Learning Based Forecasting of COVID-19 Hospitalisation in England: A Comparative Analysis

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this