IDEAS home Printed from https://ideas.repec.org/a/spr/endesu/v26y2024i1d10.1007_s10668-022-02727-3.html
   My bibliography  Save this article

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

Author

Listed:
  • Zainab Khan

    (Aligarh Muslim University)

  • Sk Ajim Ali

    (Aligarh Muslim University)

  • Mohd Mohsin

    (Aligarh Muslim University)

  • Farhana Parvin

    (Aligarh Muslim University)

  • Syed Kausar Shamim

    (Aligarh Muslim University)

  • Ateeque Ahmad

    (Aligarh Muslim University)

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 Ri) 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 Ri ≤ 0.25) and Pithoragarh to be the most vulnerable district (normalised Ri > 0.90). Pauri Garwal and Uttarkashi are moderately vulnerable (normalised Ri 0.50 to 0.75).

Suggested Citation

  • Zainab Khan & Sk Ajim Ali & Mohd Mohsin & Farhana Parvin & Syed Kausar Shamim & Ateeque Ahmad, 2024. "A district-level vulnerability assessment of next COVID-19 variant (Omicron BA.2) in Uttarakhand using quantitative SWOT analysis," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(1), pages 657-686, January.
    • Handle: RePEc:spr:endesu:v:26:y:2024:i:1:d:10.1007_s10668-022-02727-3
    DOI: 10.1007/s10668-022-02727-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10668-022-02727-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10668-022-02727-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Nikolopoulos, Konstantinos & Punia, Sushil & Schäfers, Andreas & Tsinopoulos, Christos & Vasilakis, Chrysovalantis, 2021. "Forecasting and planning during a pandemic: COVID-19 growth rates, supply chain disruptions, and governmental decisions," European Journal of Operational Research, Elsevier, vol. 290(1), pages 99-115.
    2. Tarig Ali & Maruf Mortula & Rehan Sadiq, 2021. "GIS-based vulnerability analysis of the United States to COVID-19 occurrence," Journal of Risk Research, Taylor & Francis Journals, vol. 24(3-4), pages 416-431, April.
    3. Rakhohori Bag & Manoranjan Ghosh & Bapan Biswas & Mitrajit Chatterjee, 2020. "Understanding the spatio‐temporal pattern of COVID‐19 outbreak in India using GIS and India's response in managing the pandemic," Regional Science Policy & Practice, Wiley Blackwell, vol. 12(6), pages 1063-1103, December.
    4. Matthew Browning & Kangjae Lee, 2017. "Within What Distance Does “Greenness” Best Predict Physical Health? A Systematic Review of Articles with GIS Buffer Analyses across the Lifespan," IJERPH, MDPI, vol. 14(7), pages 1-21, June.
    5. Qiu, Richard T.R. & Park, Jinah & Li, ShiNa & Song, Haiyan, 2020. "Social costs of tourism during the COVID-19 pandemic," Annals of Tourism Research, Elsevier, vol. 84(C).
    6. Vandana Tamrakar & Ankita Srivastava & Nandita Saikia & Mukesh C Parmar & Sudheer Kumar Shukla & Shewli Shabnam & Bandita Boro & Apala Saha & Benjamin Debbarma, 2021. "District level correlates of COVID-19 pandemic in India during March-October 2020," PLOS ONE, Public Library of Science, vol. 16(9), pages 1-17, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hwang, Eunju, 2022. "Prediction intervals of the COVID-19 cases by HAR models with growth rates and vaccination rates in top eight affected countries: Bootstrap improvement," Chaos, Solitons & Fractals, Elsevier, vol. 155(C).
    2. Nabetse Baruc Blas-Miranda & Ana Lilia Lozada-Tequeanes & Juan Antonio Miranda-Zuñiga & Marcia P. Jimenez, 2022. "Green Space Exposure and Obesity in the Mexican Adult Population," IJERPH, MDPI, vol. 19(22), pages 1-13, November.
    3. Hao-Ting Chang & Chih-Da Wu & Wen-Chi Pan & Shih-Chun Candice Lung & Huey-Jen Su, 2019. "Association Between Surrounding Greenness and Schizophrenia: A Taiwanese Cohort Study," IJERPH, MDPI, vol. 16(8), pages 1-16, April.
    4. Liu, Xing (Stella) & Wan, Lisa C. & Yi, Xiao (Shannon), 2022. "Humanoid versus non-humanoid robots: How mortality salience shapes preference for robot services under the COVID-19 pandemic?," Annals of Tourism Research, Elsevier, vol. 94(C).
    5. Leon John Mach, 2021. "Surf Tourism in Uncertain Times: Resident Perspectives on the Sustainability Implications of COVID-19," Societies, MDPI, vol. 11(3), pages 1-15, July.
    6. Yamaka, Woraphon & Zhang, Xuefeng & Maneejuk, Paravee & Ramos, Vicente, 2023. "Asymmetric effects of third-country exchange rate risk: A Markov switching approach," Annals of Tourism Research, Elsevier, vol. 103(C).
    7. Benítez-Peña, Sandra & Carrizosa, Emilio & Guerrero, Vanesa & Jiménez-Gamero, M. Dolores & Martín-Barragán, Belén & Molero-Río, Cristina & Ramírez-Cobo, Pepa & Romero Morales, Dolores & Sillero-Denami, 2021. "On sparse ensemble methods: An application to short-term predictions of the evolution of COVID-19," European Journal of Operational Research, Elsevier, vol. 295(2), pages 648-663.
    8. Roberta Capello & Andrea Caragliu, 2021. "Regional growth and disparities in a post‐COVID Europe: A new normality scenario," Journal of Regional Science, Wiley Blackwell, vol. 61(4), pages 710-727, September.
    9. Ling-Long Tsai, 2022. "Factors That Influence Virtual Tourism Holistic Image: The Moderating Role of Sense of Presence," Sustainability, MDPI, vol. 14(1), pages 1-20, January.
    10. Das, Saikat & Bose, Indranil & Sarkar, Uttam Kumar, 2023. "Predicting the outbreak of epidemics using a network-based approach," European Journal of Operational Research, Elsevier, vol. 309(2), pages 819-831.
    11. Jerome Agrusa & Cathrine Linnes & Joseph Lema & Jihye (Ellie) Min & Tony Henthorne & Holly Itoga & Harold Lee, 2021. "Tourism Well-Being and Transitioning Island Destinations for Sustainable Development," JRFM, MDPI, vol. 14(1), pages 1-14, January.
    12. Konstantinos Liagkouras & Konstantinos Metaxiotis, 2023. "Sources of Uncertainty and Risk Quantification Methods in Supply Chain Management: A Literature Study," SN Operations Research Forum, Springer, vol. 4(4), pages 1-42, December.
    13. Ling-en Wang & Bing Tian & Viachaslau Filimonau & Zhizhong Ning & Xuechun Yang, 2022. "The impact of the COVID-19 pandemic on revenues of visitor attractions: An exploratory and preliminary study in China," Tourism Economics, , vol. 28(1), pages 153-174, February.
    14. Erica Mingotto & Michele Tamma, 2021. "Covid-19 and recovery strategies. Some insights from an ongoing exploratory study in the Italian hospitality industry: the case of the historic city centre of Venice," Working Papers 02, Venice School of Management - Department of Management, Università Ca' Foscari Venezia.
    15. Alessandro Bitetto & Paola Cerchiello & Charilaos Mertzanis, 2021. "A data-driven approach to measuring epidemiological susceptibility risk around the world," DEM Working Papers Series 200, University of Pavia, Department of Economics and Management.
    16. , Aisdl, 2020. "Employing Value Chain Theory To Address COVID-19 Outbreak In Tourism Management: A Resilience and Stakeholder View," OSF Preprints tmzvf, Center for Open Science.
    17. Qi, Yue & Liao, Kezhi & Liu, Tongyang & Zhang, Yu, 2022. "Originating multiple-objective portfolio selection by counter-COVID measures and analytically instigating robust optimization by mean-parameterized nondominated paths," Operations Research Perspectives, Elsevier, vol. 9(C).
    18. Xi, Mengjie & Liu, Yang & Fang, Wei & Feng, Taiwen, 2024. "Intelligent manufacturing for strengthening operational resilience during the COVID-19 pandemic: A dynamic capability theory perspective," International Journal of Production Economics, Elsevier, vol. 267(C).
    19. José F Baños-Pino & David Boto-García & Eduardo Del Valle & Inés Sustacha, 2023. "The impact of COVID-19 on tourists’ length of stay and daily expenditures," Tourism Economics, , vol. 29(2), pages 437-459, March.
    20. Ranveer Singh Rana & Dinesh Kumar & Kanika Prasad & K. Mathiyazhagan, 2024. "Mitigating the impact of demand disruption on perishable inventory in a two-warehouse system," Operations Management Research, Springer, vol. 17(2), pages 469-504, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:endesu:v:26:y:2024:i:1:d:10.1007_s10668-022-02727-3. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.