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Nexus between population density and novel coronavirus (COVID-19) pandemic in the south Indian states: A geo-statistical approach

Author

Listed:
  • Mohammad Arif

    (Visva-Bharati (A Central University))

  • Soumita Sengupta

    (Birla Institute of Technology)

Abstract

The unprecedented growth of the novel coronavirus (SARS-CoV-2) as a severe acute respiratory syndrome escalated to the coronavirus disease 2019 (COVID-19) pandemic. It has created an unanticipated global public health crisis that is spreading rapidly in India as well, posing a serious threat to 1350 million persons. Among the factors, population density is foremost in posing a challenge in controlling the COVID-19 contagion. In such extraordinary times, evidence-based knowledge is the prime requisite for pacifying the effect. In this piece, we have studied the district wise transmissions of the novel coronavirus in five south Indian states until 20th July 2020 and its relationship with their respective population density. The five states are purposefully selected for their records in better healthcare infrastructure vis-à-vis other states in India. The study uses Pearson’s correlation coefficient to account for the direct impact of population density on COVID-19 transmission rate. Response surface methodology approach is used to validate the correlation between density and transmission rate and spatiotemporal dynamics is highlighted using Thiessen polygon method. The analysis has found that COVID-19 transmission in four states (Kerala, Tamil Nadu, Karnataka and Telangana) strongly hinges upon the spatial distribution of population density. In addition, the results indicate that the long-term impacts of the COVID-19 crisis are likely to differ with demographic density. In conclusion, those at the helm of affairs must take cognizance of the vulnerability clusters together across districts.

Suggested Citation

  • Mohammad Arif & Soumita Sengupta, 2021. "Nexus between population density and novel coronavirus (COVID-19) pandemic in the south Indian states: A geo-statistical approach," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(7), pages 10246-10274, July.
    • Handle: RePEc:spr:endesu:v:23:y:2021:i:7:d:10.1007_s10668-020-01055-8
    DOI: 10.1007/s10668-020-01055-8
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    References listed on IDEAS

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    1. Charles J. Kowalski, 1972. "On the Effects of Non‐Normality on the Distribution of the Sample Product‐Moment Correlation Coefficient," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 21(1), pages 1-12, March.
    2. Sneha Gautam & Luc Hens, 2020. "SARS-CoV-2 pandemic in India: what might we expect?," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 22(5), pages 3867-3869, June.
    3. Shima Hamidi & Sadegh Sabouri & Reid Ewing, 2020. "Does Density Aggravate the COVID-19 Pandemic?," Journal of the American Planning Association, Taylor & Francis Journals, vol. 86(4), pages 495-509, October.
    4. Mishra, Swasti Vardhan & Gayen, Amiya & Haque, Sk. Mafizul, 2020. "COVID-19 and urban vulnerability in India," SocArXiv 523r8, Center for Open Science.
    5. Christina E. Mills & James M. Robins & Marc Lipsitch, 2004. "Transmissibility of 1918 pandemic influenza," Nature, Nature, vol. 432(7019), pages 904-906, December.
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    Cited by:

    1. Singh, Pratik Kumar & Mishra, Alok Kumar, 2024. "Deciphering the COVID-19 density puzzle: A meta-analysis approach," Social Science & Medicine, Elsevier, vol. 363(C).
    2. Nushrat Nazia & Zahid Ahmad Butt & Melanie Lyn Bedard & Wang-Choi Tang & Hibah Sehar & Jane Law, 2022. "Methods Used in the Spatial and Spatiotemporal Analysis of COVID-19 Epidemiology: A Systematic Review," IJERPH, MDPI, vol. 19(14), pages 1-28, July.
    3. Haozhe Zhang & Jinyi Li, 2024. "Mapping the urban and rural planning response paths to pandemics of infectious diseases," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-12, December.

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