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Status evaluation of provinces affected by COVID-19: A qualitative assessment using fuzzy system
The outbreak of COVID-19 had already shown its harmful impact on mankind, especially on health sectors, global economy, education systems, cultures, politics, and other important fields. Like most of the affected countries in the globe, India is now facing serious crisis due to COVID-19 in the recen...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier B.V.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8169225/ https://www.ncbi.nlm.nih.gov/pubmed/34093096 http://dx.doi.org/10.1016/j.asoc.2021.107540 |
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author | Ghosh, Bappaditya Biswas, Animesh |
author_facet | Ghosh, Bappaditya Biswas, Animesh |
author_sort | Ghosh, Bappaditya |
collection | PubMed |
description | The outbreak of COVID-19 had already shown its harmful impact on mankind, especially on health sectors, global economy, education systems, cultures, politics, and other important fields. Like most of the affected countries in the globe, India is now facing serious crisis due to COVID-19 in the recent times. The evaluation of the present status of the provinces affected by COVID-19 is very much essential to the government authorities to impose preventive strategies in controlling the spread of COVID-19 and to take necessary measures. In this article, a computational methodology is developed to estimate the present status of states and provinces which are affected due to COVID-19 using a fuzzy inference system. The factors such as population density, number of COVID-19 tests, confirmed cases of COVID-19, recovery rate, and mortality rate are considered as the input parameters of the proposed methodology. Considering positive and negative factors of the input parameters, the rule base is developed using triangular fuzzy numbers to capture uncertainties associated with the model. The application potentiality is validated by evaluating Pearson’s correlation coefficient. A sensitivity analysis is also performed to observe the changes of final output by varying the tolerance ranges of the inputs. The results of the proposed method show that some of the provinces have very poor performance in controlling the spread of COVID-19 in India. So, the government needs to take serious attention to deal with the pandemic situation of COVID-19 in those provinces. |
format | Online Article Text |
id | pubmed-8169225 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Elsevier B.V. |
record_format | MEDLINE/PubMed |
spelling | pubmed-81692252021-06-02 Status evaluation of provinces affected by COVID-19: A qualitative assessment using fuzzy system Ghosh, Bappaditya Biswas, Animesh Appl Soft Comput Article The outbreak of COVID-19 had already shown its harmful impact on mankind, especially on health sectors, global economy, education systems, cultures, politics, and other important fields. Like most of the affected countries in the globe, India is now facing serious crisis due to COVID-19 in the recent times. The evaluation of the present status of the provinces affected by COVID-19 is very much essential to the government authorities to impose preventive strategies in controlling the spread of COVID-19 and to take necessary measures. In this article, a computational methodology is developed to estimate the present status of states and provinces which are affected due to COVID-19 using a fuzzy inference system. The factors such as population density, number of COVID-19 tests, confirmed cases of COVID-19, recovery rate, and mortality rate are considered as the input parameters of the proposed methodology. Considering positive and negative factors of the input parameters, the rule base is developed using triangular fuzzy numbers to capture uncertainties associated with the model. The application potentiality is validated by evaluating Pearson’s correlation coefficient. A sensitivity analysis is also performed to observe the changes of final output by varying the tolerance ranges of the inputs. The results of the proposed method show that some of the provinces have very poor performance in controlling the spread of COVID-19 in India. So, the government needs to take serious attention to deal with the pandemic situation of COVID-19 in those provinces. Elsevier B.V. 2021-09 2021-06-02 /pmc/articles/PMC8169225/ /pubmed/34093096 http://dx.doi.org/10.1016/j.asoc.2021.107540 Text en © 2021 Elsevier B.V. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Ghosh, Bappaditya Biswas, Animesh Status evaluation of provinces affected by COVID-19: A qualitative assessment using fuzzy system |
title | Status evaluation of provinces affected by COVID-19: A qualitative assessment using fuzzy system |
title_full | Status evaluation of provinces affected by COVID-19: A qualitative assessment using fuzzy system |
title_fullStr | Status evaluation of provinces affected by COVID-19: A qualitative assessment using fuzzy system |
title_full_unstemmed | Status evaluation of provinces affected by COVID-19: A qualitative assessment using fuzzy system |
title_short | Status evaluation of provinces affected by COVID-19: A qualitative assessment using fuzzy system |
title_sort | status evaluation of provinces affected by covid-19: a qualitative assessment using fuzzy system |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8169225/ https://www.ncbi.nlm.nih.gov/pubmed/34093096 http://dx.doi.org/10.1016/j.asoc.2021.107540 |
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