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South Asian Countries are Less Fatal Concerning COVID-19: A Fact-finding Procedure Integrating Machine Learning & Multiple Criteria Decision-Making (MCDM) Technique
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) which caused an outbreak of pneumonia in December 2019 in Wuhan, China, has spread rapidly throughout the world. This ongoing pandemic has resulted over 55.6 million cases of COVID-19 leading to 1.34 million deaths in more than 188 countri...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer India
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7936601/ http://dx.doi.org/10.1007/s40031-021-00547-z |
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author | Guhathakurata, Soham Saha, Sayak Kundu, Souvik Chakraborty, Arpita Banerjee, Jyoti Sekhar |
author_facet | Guhathakurata, Soham Saha, Sayak Kundu, Souvik Chakraborty, Arpita Banerjee, Jyoti Sekhar |
author_sort | Guhathakurata, Soham |
collection | PubMed |
description | Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) which caused an outbreak of pneumonia in December 2019 in Wuhan, China, has spread rapidly throughout the world. This ongoing pandemic has resulted over 55.6 million cases of COVID-19 leading to 1.34 million deaths in more than 188 countries. However, it has been observed that the death rate is significantly in the lower side for the SAARC countries compared to the First World Nations. In this paper, the possible factors have been represented that determine this uneven distribution of COVID-19 deaths. The significance of the factors has been presented in this paper after the data analysis of the factors from 165 different countries. Based on the correlation of the factors and their critical impact towards the concerned countries death toll, the risk index of each factor has been labeled using analytical hierarchy process (AHP)-based MCDM, i.e., multiple criteria decision-making technique. The risk index of all the factors has been used to generate the susceptibility of COVID-19 for each of the countries in study, specifically the SAARC Nations. Finally, the hierarchical clustering was applied to visualize the death toll of the countries corresponding to their susceptibility index. |
format | Online Article Text |
id | pubmed-7936601 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer India |
record_format | MEDLINE/PubMed |
spelling | pubmed-79366012021-03-08 South Asian Countries are Less Fatal Concerning COVID-19: A Fact-finding Procedure Integrating Machine Learning & Multiple Criteria Decision-Making (MCDM) Technique Guhathakurata, Soham Saha, Sayak Kundu, Souvik Chakraborty, Arpita Banerjee, Jyoti Sekhar J. Inst. Eng. India Ser. B Original Contribution Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) which caused an outbreak of pneumonia in December 2019 in Wuhan, China, has spread rapidly throughout the world. This ongoing pandemic has resulted over 55.6 million cases of COVID-19 leading to 1.34 million deaths in more than 188 countries. However, it has been observed that the death rate is significantly in the lower side for the SAARC countries compared to the First World Nations. In this paper, the possible factors have been represented that determine this uneven distribution of COVID-19 deaths. The significance of the factors has been presented in this paper after the data analysis of the factors from 165 different countries. Based on the correlation of the factors and their critical impact towards the concerned countries death toll, the risk index of each factor has been labeled using analytical hierarchy process (AHP)-based MCDM, i.e., multiple criteria decision-making technique. The risk index of all the factors has been used to generate the susceptibility of COVID-19 for each of the countries in study, specifically the SAARC Nations. Finally, the hierarchical clustering was applied to visualize the death toll of the countries corresponding to their susceptibility index. Springer India 2021-03-06 2021 /pmc/articles/PMC7936601/ http://dx.doi.org/10.1007/s40031-021-00547-z Text en © The Institution of Engineers (India) 2021 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 Contribution Guhathakurata, Soham Saha, Sayak Kundu, Souvik Chakraborty, Arpita Banerjee, Jyoti Sekhar South Asian Countries are Less Fatal Concerning COVID-19: A Fact-finding Procedure Integrating Machine Learning & Multiple Criteria Decision-Making (MCDM) Technique |
title | South Asian Countries are Less Fatal Concerning COVID-19: A Fact-finding Procedure Integrating Machine Learning & Multiple Criteria Decision-Making (MCDM) Technique |
title_full | South Asian Countries are Less Fatal Concerning COVID-19: A Fact-finding Procedure Integrating Machine Learning & Multiple Criteria Decision-Making (MCDM) Technique |
title_fullStr | South Asian Countries are Less Fatal Concerning COVID-19: A Fact-finding Procedure Integrating Machine Learning & Multiple Criteria Decision-Making (MCDM) Technique |
title_full_unstemmed | South Asian Countries are Less Fatal Concerning COVID-19: A Fact-finding Procedure Integrating Machine Learning & Multiple Criteria Decision-Making (MCDM) Technique |
title_short | South Asian Countries are Less Fatal Concerning COVID-19: A Fact-finding Procedure Integrating Machine Learning & Multiple Criteria Decision-Making (MCDM) Technique |
title_sort | south asian countries are less fatal concerning covid-19: a fact-finding procedure integrating machine learning & multiple criteria decision-making (mcdm) technique |
topic | Original Contribution |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7936601/ http://dx.doi.org/10.1007/s40031-021-00547-z |
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