Cargando…

Structural Zero Data of COVID-19 Discovers Exodus Probabilities

BACKGROUND: Challenges to manage, mitigate, or prevent the COVID-19’s pandemics are felt by medical, healthcare professionals and governing agencies. Health researchers conduct survey among the citizens to capture their opinion on COVID-19. In such surveys like in Hanafiah and Wan (2020), structural...

Descripción completa

Detalles Bibliográficos
Autores principales: Shanmugam, Ramalingam, Singh, Karan P
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8214563/
https://www.ncbi.nlm.nih.gov/pubmed/34163172
http://dx.doi.org/10.2147/JMDH.S304419
_version_ 1783710090444931072
author Shanmugam, Ramalingam
Singh, Karan P
author_facet Shanmugam, Ramalingam
Singh, Karan P
author_sort Shanmugam, Ramalingam
collection PubMed
description BACKGROUND: Challenges to manage, mitigate, or prevent the COVID-19’s pandemics are felt by medical, healthcare professionals and governing agencies. Health researchers conduct survey among the citizens to capture their opinion on COVID-19. In such surveys like in Hanafiah and Wan (2020), structural-zero (different from sampling zero) category occurs as they question about perception, knowledge, and communication regarding COVID-19. MATERIALS: The data were collected in a survey conducted among Malaysians by Hanafiah and Wan regarding COVID-19. The survey focused on people’s response about the public communication, knowledge, and perception. METHODS: One of the four question categories in the survey is mutually exclusive with the other three questions. Consequently, there will be no entry in that category. Such group is called structurally zero category in the literature. The literature never probed the migrative split to other categories of the unknown proportion belonging to the structural zero category. In this article, the probability-based new and innovative method configures what proportion in that mutually exclusive category and it is the essence of our method. RESULTS: The mutually exclusive nature of subquestions manufactured structural zero in their data. A careful analysis of the data has created so far unknown probability concepts in the literature, which we named as “Exodus probabilities” in this article. Its discovery and utility are illustrated and elaborated with application in COVID-19. This methodology is also useful in applications in engineering, epidemiology, marketing, communication networking, etc. CONCLUSION: What is quite novel about the discovery of the exodus probability in this article is the evolution of the concepts from the structural-zero category. In such situation, when a category is eliminated, the proportions of the sample might have uncommunicatively transited to other viable categories and our research question is all about configuring their proportions. This is an innovative approach.
format Online
Article
Text
id pubmed-8214563
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Dove
record_format MEDLINE/PubMed
spelling pubmed-82145632021-06-22 Structural Zero Data of COVID-19 Discovers Exodus Probabilities Shanmugam, Ramalingam Singh, Karan P J Multidiscip Healthc Methodology BACKGROUND: Challenges to manage, mitigate, or prevent the COVID-19’s pandemics are felt by medical, healthcare professionals and governing agencies. Health researchers conduct survey among the citizens to capture their opinion on COVID-19. In such surveys like in Hanafiah and Wan (2020), structural-zero (different from sampling zero) category occurs as they question about perception, knowledge, and communication regarding COVID-19. MATERIALS: The data were collected in a survey conducted among Malaysians by Hanafiah and Wan regarding COVID-19. The survey focused on people’s response about the public communication, knowledge, and perception. METHODS: One of the four question categories in the survey is mutually exclusive with the other three questions. Consequently, there will be no entry in that category. Such group is called structurally zero category in the literature. The literature never probed the migrative split to other categories of the unknown proportion belonging to the structural zero category. In this article, the probability-based new and innovative method configures what proportion in that mutually exclusive category and it is the essence of our method. RESULTS: The mutually exclusive nature of subquestions manufactured structural zero in their data. A careful analysis of the data has created so far unknown probability concepts in the literature, which we named as “Exodus probabilities” in this article. Its discovery and utility are illustrated and elaborated with application in COVID-19. This methodology is also useful in applications in engineering, epidemiology, marketing, communication networking, etc. CONCLUSION: What is quite novel about the discovery of the exodus probability in this article is the evolution of the concepts from the structural-zero category. In such situation, when a category is eliminated, the proportions of the sample might have uncommunicatively transited to other viable categories and our research question is all about configuring their proportions. This is an innovative approach. Dove 2021-06-15 /pmc/articles/PMC8214563/ /pubmed/34163172 http://dx.doi.org/10.2147/JMDH.S304419 Text en © 2021 Shanmugam and Singh. https://creativecommons.org/licenses/by-nc/3.0/This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/ (https://creativecommons.org/licenses/by-nc/3.0/) ). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php).
spellingShingle Methodology
Shanmugam, Ramalingam
Singh, Karan P
Structural Zero Data of COVID-19 Discovers Exodus Probabilities
title Structural Zero Data of COVID-19 Discovers Exodus Probabilities
title_full Structural Zero Data of COVID-19 Discovers Exodus Probabilities
title_fullStr Structural Zero Data of COVID-19 Discovers Exodus Probabilities
title_full_unstemmed Structural Zero Data of COVID-19 Discovers Exodus Probabilities
title_short Structural Zero Data of COVID-19 Discovers Exodus Probabilities
title_sort structural zero data of covid-19 discovers exodus probabilities
topic Methodology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8214563/
https://www.ncbi.nlm.nih.gov/pubmed/34163172
http://dx.doi.org/10.2147/JMDH.S304419
work_keys_str_mv AT shanmugamramalingam structuralzerodataofcovid19discoversexodusprobabilities
AT singhkaranp structuralzerodataofcovid19discoversexodusprobabilities