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Geospatial modelling of COVID-19 vulnerability using an integrated fuzzy MCDM approach: a case study of West Bengal, India
COVID-19 is a worldwide transmitted pandemic that has brought a threatening challenge to Indian society and the economy. The disease has become a public health disaster, which has no effective medication. However, proper management and planning, which includes understanding the transmitting pattern,...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Springer International Publishing
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8475317/ https://www.ncbi.nlm.nih.gov/pubmed/34604502 http://dx.doi.org/10.1007/s40808-021-01287-1 |
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author | Malakar, Sukanta |
author_facet | Malakar, Sukanta |
author_sort | Malakar, Sukanta |
collection | PubMed |
description | COVID-19 is a worldwide transmitted pandemic that has brought a threatening challenge to Indian society and the economy. The disease has become a public health disaster, which has no effective medication. However, proper management and planning, which includes understanding the transmitting pattern, number of containment zones, vulnerable factors, and level of risk, may break the chain of transmission and reduce the number of cases. Hence, this study has attempted to model the COVID-19 vulnerability using an integrated fuzzy multi-criteria decision-making (MCDM) approach, namely fuzzy-analytical hierarchy process (AHP) and fuzzy-technique for order preference by similarity to ideal solution (TOPSIS) for West Bengal, India, through geographic information system (GIS). A total of 15 parameters were utilised to model the COVID-19 vulnerability, which was further categorised into three criteria: social vulnerability, epidemiological vulnerability, and physical vulnerability. The final vulnerability mapping has been done using these three criteria through the GIS platform. This study reveals that COVID-19 infection highly threatens about 20% of the total area of West Bengal, 23.42% moderately vulnerable, and 57.03% of the area comes under low vulnerability. The highly vulnerable region includes the Kolkata, South 24 Paraganas, and North 24 Paraganas, which are considered highly populated districts of West Bengal. Therefore government agencies should be more focused and plan accordingly to safeguard the community, especially the region with very high COVID-19 vulnerability, from further spreading the infection. |
format | Online Article Text |
id | pubmed-8475317 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-84753172021-09-28 Geospatial modelling of COVID-19 vulnerability using an integrated fuzzy MCDM approach: a case study of West Bengal, India Malakar, Sukanta Model Earth Syst Environ Original Article COVID-19 is a worldwide transmitted pandemic that has brought a threatening challenge to Indian society and the economy. The disease has become a public health disaster, which has no effective medication. However, proper management and planning, which includes understanding the transmitting pattern, number of containment zones, vulnerable factors, and level of risk, may break the chain of transmission and reduce the number of cases. Hence, this study has attempted to model the COVID-19 vulnerability using an integrated fuzzy multi-criteria decision-making (MCDM) approach, namely fuzzy-analytical hierarchy process (AHP) and fuzzy-technique for order preference by similarity to ideal solution (TOPSIS) for West Bengal, India, through geographic information system (GIS). A total of 15 parameters were utilised to model the COVID-19 vulnerability, which was further categorised into three criteria: social vulnerability, epidemiological vulnerability, and physical vulnerability. The final vulnerability mapping has been done using these three criteria through the GIS platform. This study reveals that COVID-19 infection highly threatens about 20% of the total area of West Bengal, 23.42% moderately vulnerable, and 57.03% of the area comes under low vulnerability. The highly vulnerable region includes the Kolkata, South 24 Paraganas, and North 24 Paraganas, which are considered highly populated districts of West Bengal. Therefore government agencies should be more focused and plan accordingly to safeguard the community, especially the region with very high COVID-19 vulnerability, from further spreading the infection. Springer International Publishing 2021-09-27 2022 /pmc/articles/PMC8475317/ /pubmed/34604502 http://dx.doi.org/10.1007/s40808-021-01287-1 Text en © The Author(s), under exclusive licence to Springer Nature Switzerland AG 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 Article Malakar, Sukanta Geospatial modelling of COVID-19 vulnerability using an integrated fuzzy MCDM approach: a case study of West Bengal, India |
title | Geospatial modelling of COVID-19 vulnerability using an integrated fuzzy MCDM approach: a case study of West Bengal, India |
title_full | Geospatial modelling of COVID-19 vulnerability using an integrated fuzzy MCDM approach: a case study of West Bengal, India |
title_fullStr | Geospatial modelling of COVID-19 vulnerability using an integrated fuzzy MCDM approach: a case study of West Bengal, India |
title_full_unstemmed | Geospatial modelling of COVID-19 vulnerability using an integrated fuzzy MCDM approach: a case study of West Bengal, India |
title_short | Geospatial modelling of COVID-19 vulnerability using an integrated fuzzy MCDM approach: a case study of West Bengal, India |
title_sort | geospatial modelling of covid-19 vulnerability using an integrated fuzzy mcdm approach: a case study of west bengal, india |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8475317/ https://www.ncbi.nlm.nih.gov/pubmed/34604502 http://dx.doi.org/10.1007/s40808-021-01287-1 |
work_keys_str_mv | AT malakarsukanta geospatialmodellingofcovid19vulnerabilityusinganintegratedfuzzymcdmapproachacasestudyofwestbengalindia |