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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

M. Mehran Bin Azam

Fahad Anwaar

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

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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/)





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