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A district-level vulnerability assessment of next COVID-19 variant (Omicron BA.2) in Uttarakhand using quantitative SWOT analysis

COVID-19 has had an impact on the entire humankind and has been proved to spread in deadly waves. As a result, preparedness and planning are required to better deal with the epidemic’s upcoming waves. Effective planning, on the other hand, necessitates detailed vulnerability assessments at all level...

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Autores principales: Khan, Zainab, Ali, Sk Ajim, Mohsin, Mohd, Parvin, Farhana, Shamim, Syed Kausar, Ahmad, Ateeque
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Netherlands 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9630075/
https://www.ncbi.nlm.nih.gov/pubmed/36345298
http://dx.doi.org/10.1007/s10668-022-02727-3
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author Khan, Zainab
Ali, Sk Ajim
Mohsin, Mohd
Parvin, Farhana
Shamim, Syed Kausar
Ahmad, Ateeque
author_facet Khan, Zainab
Ali, Sk Ajim
Mohsin, Mohd
Parvin, Farhana
Shamim, Syed Kausar
Ahmad, Ateeque
author_sort Khan, Zainab
collection PubMed
description COVID-19 has had an impact on the entire humankind and has been proved to spread in deadly waves. As a result, preparedness and planning are required to better deal with the epidemic’s upcoming waves. Effective planning, on the other hand, necessitates detailed vulnerability assessments at all levels, from the national to the state or regional. There are several issues at the regional level, and each region has its own features. As a result, each region needs its own COVID-19 vulnerability assessment. In terms of climate, terrain and demographics, the state of Uttarakhand differs significantly from the rest of India. As a result, a vulnerability assessment of the next COVID-19 variation (Omicron BA.2) is required for district-level planning to meet regional concerns. A total of 17 variables were chosen for this study, including demographic, socio-economic, infrastructure, epidemiological and tourism-related factors. AHP was used to compute their weights. After applying min–max normalisation to the data, a district-level quantitative SWOT is created to compare the performance of 13 Uttarakhand districts. A COVID-19 vulnerability index (normalised R(i)) ranging between 0 and 1 was produced, and district-level vulnerabilities were mapped. Quantitative SWOT results depict that Dehradun is a best performing district followed by Haridwar, while Bageshwar, Rudra Prayag, Champawat and Pithoragarh are on the weaker side and the normalised Ri proves Dehradun, Nainital, Champawat, Bageshwar and Chamoli to be least vulnerable to COVID-19 (normalised R(i) ≤ 0.25) and Pithoragarh to be the most vulnerable district (normalised R(i) > 0.90). Pauri Garwal and Uttarkashi are moderately vulnerable (normalised R(i) 0.50 to 0.75). ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s10668-022-02727-3) contains supplementary material, which is available to authorised users.
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spelling pubmed-96300752022-11-03 A district-level vulnerability assessment of next COVID-19 variant (Omicron BA.2) in Uttarakhand using quantitative SWOT analysis Khan, Zainab Ali, Sk Ajim Mohsin, Mohd Parvin, Farhana Shamim, Syed Kausar Ahmad, Ateeque Environ Dev Sustain Article COVID-19 has had an impact on the entire humankind and has been proved to spread in deadly waves. As a result, preparedness and planning are required to better deal with the epidemic’s upcoming waves. Effective planning, on the other hand, necessitates detailed vulnerability assessments at all levels, from the national to the state or regional. There are several issues at the regional level, and each region has its own features. As a result, each region needs its own COVID-19 vulnerability assessment. In terms of climate, terrain and demographics, the state of Uttarakhand differs significantly from the rest of India. As a result, a vulnerability assessment of the next COVID-19 variation (Omicron BA.2) is required for district-level planning to meet regional concerns. A total of 17 variables were chosen for this study, including demographic, socio-economic, infrastructure, epidemiological and tourism-related factors. AHP was used to compute their weights. After applying min–max normalisation to the data, a district-level quantitative SWOT is created to compare the performance of 13 Uttarakhand districts. A COVID-19 vulnerability index (normalised R(i)) ranging between 0 and 1 was produced, and district-level vulnerabilities were mapped. Quantitative SWOT results depict that Dehradun is a best performing district followed by Haridwar, while Bageshwar, Rudra Prayag, Champawat and Pithoragarh are on the weaker side and the normalised Ri proves Dehradun, Nainital, Champawat, Bageshwar and Chamoli to be least vulnerable to COVID-19 (normalised R(i) ≤ 0.25) and Pithoragarh to be the most vulnerable district (normalised R(i) > 0.90). Pauri Garwal and Uttarkashi are moderately vulnerable (normalised R(i) 0.50 to 0.75). ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s10668-022-02727-3) contains supplementary material, which is available to authorised users. Springer Netherlands 2022-11-03 /pmc/articles/PMC9630075/ /pubmed/36345298 http://dx.doi.org/10.1007/s10668-022-02727-3 Text en © The Author(s), under exclusive licence to Springer Nature B.V. 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. 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 Article
Khan, Zainab
Ali, Sk Ajim
Mohsin, Mohd
Parvin, Farhana
Shamim, Syed Kausar
Ahmad, Ateeque
A district-level vulnerability assessment of next COVID-19 variant (Omicron BA.2) in Uttarakhand using quantitative SWOT analysis
title A district-level vulnerability assessment of next COVID-19 variant (Omicron BA.2) in Uttarakhand using quantitative SWOT analysis
title_full A district-level vulnerability assessment of next COVID-19 variant (Omicron BA.2) in Uttarakhand using quantitative SWOT analysis
title_fullStr A district-level vulnerability assessment of next COVID-19 variant (Omicron BA.2) in Uttarakhand using quantitative SWOT analysis
title_full_unstemmed A district-level vulnerability assessment of next COVID-19 variant (Omicron BA.2) in Uttarakhand using quantitative SWOT analysis
title_short A district-level vulnerability assessment of next COVID-19 variant (Omicron BA.2) in Uttarakhand using quantitative SWOT analysis
title_sort district-level vulnerability assessment of next covid-19 variant (omicron ba.2) in uttarakhand using quantitative swot analysis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9630075/
https://www.ncbi.nlm.nih.gov/pubmed/36345298
http://dx.doi.org/10.1007/s10668-022-02727-3
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