DISCOVID: discovering patterns of COVID-19 infection from recovered patients: a case study in Saudi Arabia

A respiratory syndrome COVID-19 pandemic has become a serious global concern. Still, a large number of people have been daily infected worldwide. Discovering COVID-19 infection patterns is significant for health providers towards understanding the infection factors. Current COVID-19 research works h...

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Autores principales: Alafif, Tarik, Etaiwi, Alaa, Hawsawi, Yousef, Alrefaei, Abdulmajeed, Albassam, Ayman, Althobaiti, Hassan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Nature Singapore 2022
Materias:
Original Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9251043/
https://www.ncbi.nlm.nih.gov/pubmed/35812263
http://dx.doi.org/10.1007/s41870-022-00973-2
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author Alafif, Tarik
Etaiwi, Alaa
Hawsawi, Yousef
Alrefaei, Abdulmajeed
Albassam, Ayman
Althobaiti, Hassan
author_facet Alafif, Tarik
Etaiwi, Alaa
Hawsawi, Yousef
Alrefaei, Abdulmajeed
Albassam, Ayman
Althobaiti, Hassan
author_sort Alafif, Tarik
collection PubMed
description A respiratory syndrome COVID-19 pandemic has become a serious global concern. Still, a large number of people have been daily infected worldwide. Discovering COVID-19 infection patterns is significant for health providers towards understanding the infection factors. Current COVID-19 research works have not been attempted to discover the infection patterns, yet. In this paper, we employ an Association Rules Apriori (ARA) algorithm to discover the infection patterns from COVID-19 recovered patients’ data. A non-clinical COVID-19 dataset is introduced and analyzed. A sample of recovered patients’ data is manually collected in Saudi Arabia. Our manual computation and experimental results show strong associative rules with high confidence scores among males, weight above 70 kilograms, height above 160 centimeters, and fever patterns. These patterns are the strongest infection patterns discovered from COVID-19 recovered patients’ data.
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spelling pubmed-92510432022-07-05 DISCOVID: discovering patterns of COVID-19 infection from recovered patients: a case study in Saudi Arabia Alafif, Tarik Etaiwi, Alaa Hawsawi, Yousef Alrefaei, Abdulmajeed Albassam, Ayman Althobaiti, Hassan Int J Inf Technol Original Research A respiratory syndrome COVID-19 pandemic has become a serious global concern. Still, a large number of people have been daily infected worldwide. Discovering COVID-19 infection patterns is significant for health providers towards understanding the infection factors. Current COVID-19 research works have not been attempted to discover the infection patterns, yet. In this paper, we employ an Association Rules Apriori (ARA) algorithm to discover the infection patterns from COVID-19 recovered patients’ data. A non-clinical COVID-19 dataset is introduced and analyzed. A sample of recovered patients’ data is manually collected in Saudi Arabia. Our manual computation and experimental results show strong associative rules with high confidence scores among males, weight above 70 kilograms, height above 160 centimeters, and fever patterns. These patterns are the strongest infection patterns discovered from COVID-19 recovered patients’ data. Springer Nature Singapore 2022-07-04 2022 /pmc/articles/PMC9251043/ /pubmed/35812263 http://dx.doi.org/10.1007/s41870-022-00973-2 Text en © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Original Research
Alafif, Tarik
Etaiwi, Alaa
Hawsawi, Yousef
Alrefaei, Abdulmajeed
Albassam, Ayman
Althobaiti, Hassan
DISCOVID: discovering patterns of COVID-19 infection from recovered patients: a case study in Saudi Arabia
title DISCOVID: discovering patterns of COVID-19 infection from recovered patients: a case study in Saudi Arabia
title_full DISCOVID: discovering patterns of COVID-19 infection from recovered patients: a case study in Saudi Arabia
title_fullStr DISCOVID: discovering patterns of COVID-19 infection from recovered patients: a case study in Saudi Arabia
title_full_unstemmed DISCOVID: discovering patterns of COVID-19 infection from recovered patients: a case study in Saudi Arabia
title_short DISCOVID: discovering patterns of COVID-19 infection from recovered patients: a case study in Saudi Arabia
title_sort discovid: discovering patterns of covid-19 infection from recovered patients: a case study in saudi arabia
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9251043/
https://www.ncbi.nlm.nih.gov/pubmed/35812263
http://dx.doi.org/10.1007/s41870-022-00973-2
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