M. Mehran Bin Azam
A hybrid contextual framework to predict severity of infectious disease: COVID-19 case study
Azam, M. Mehran Bin; Anwaar, Fahad; Khan, Adil Mehmood; Anwar, Muhammad; Ghani, Hadhrami Bin Ab; Eisa, Taiseer Abdalla Elfadil; Abdelmaboud, Abdelzahir
Authors
Fahad Anwaar
Professor Adil Khan A.M.Khan@hull.ac.uk
Professor
Muhammad Anwar
Hadhrami Bin Ab Ghani
Taiseer Abdalla Elfadil Eisa
Abdelzahir Abdelmaboud
Abstract
Infectious disease is a particular type of disorder triggered by organisms and transmitted directly or indirectly from an infected one like COVID-19. The global economy and public health are immensely affected by COVID-19, a recently emerging infectious disease. Artificial Intelligence can be helpful to predict the severity rating of COVID-19 which assists authorities to take appropriate measures to mitigate its spread in different regions, hence it results in economic reopening and reduces the degree of mortality. In this paper, a hybrid contextual framework is proposed which incorporates content embedding of Standard Operating Procedure’s (SOPs) auxiliary description along with COVID-19 temporal features of the respective region as side information. The word embedding techniques are incorporated to generate distributed representation of SOPs auxiliary description. The higher representation of auxiliary description is obtained by utilizing content embedding and then combined with temporal features to build counties profiles. These county profiles are fed into a profile learner based on an ensemble algorithm to predict the severity level of COVID-19 in different regions. The proposed contextual framework is evaluated on public datasets provided by healthdata.gov and the National Centers for Environmental Information. A comparison of the proposed contextual framework with other state-of-the-art approaches has demonstrated its ability to accurately predict the severity level of COVID-19 in different regions.
Citation
Azam, M. M. B., Anwaar, F., Khan, A. M., Anwar, M., Ghani, H. B. A., Eisa, T. A. E., & Abdelmaboud, A. (2024). A hybrid contextual framework to predict severity of infectious disease: COVID-19 case study. Egyptian Informatics Journal, 27, Article 100508. https://doi.org/10.1016/j.eij.2024.100508
Journal Article Type | Article |
---|---|
Acceptance Date | Jul 17, 2024 |
Online Publication Date | Jul 31, 2024 |
Publication Date | 2024-09 |
Deposit Date | Jul 31, 2024 |
Publicly Available Date | Aug 7, 2024 |
Journal | Egyptian Informatics Journal |
Print ISSN | 1110-8665 |
Electronic ISSN | 2090-4754 |
Publisher | Elsevier |
Peer Reviewed | Peer Reviewed |
Volume | 27 |
Article Number | 100508 |
DOI | https://doi.org/10.1016/j.eij.2024.100508 |
Keywords | Artificial intelligence, Natural language processing, Severity rating, Profile learner, COVID-19 |
Public URL | https://hull-repository.worktribe.com/output/4749703 |
Files
Published article
(2.6 Mb)
PDF
Publisher Licence URL
http://creativecommons.org/licenses/by/4.0
Copyright Statement
© 2024 The Authors. Published by Elsevier B.V. on behalf of Faculty of Computers and Artificial Intelligence, Cairo University.
This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)
You might also like
LLM Based Cross Modality Retrieval to Improve Recommendation Performance
(2024)
Presentation / Conference Contribution
Downloadable Citations
About Repository@Hull
Administrator e-mail: repository@hull.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2025
Advanced Search