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

Zainab Khan; Sk Ajim Ali; Mohd Mohsin; Farhana Parvin; Syed Kausar Shamim; Ateeque Ahmad

doi:10.1007/s10668-022-02727-3

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

Environ Dev Sustain. 2022 Nov 3:1-30. doi: 10.1007/s10668-022-02727-3. Online ahead of print.

Authors

Zainab Khan¹, Sk Ajim Ali¹, Mohd Mohsin², Farhana Parvin¹, Syed Kausar Shamim¹, Ateeque Ahmad¹

Affiliations

¹ Department of Geography, Faculty of science, Aligarh Muslim University, Aligarh, 202002 India.
² Department of Civil engineering, Faculty of Engineering and Technology, Zakir Husain College of Engineering, Aligarh Muslim University, Aligarh, 202002 India.

Abstract

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).

Keywords: COVID-19; Quantitative SWOT; Vulnerability assessment; Vulnerability index.

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