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Nexus between population density and novel coronavirus (COVID-19) pandemic in the south Indian states: A geo-statistical approach
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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Netherlands
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7596317/ https://www.ncbi.nlm.nih.gov/pubmed/33144832 http://dx.doi.org/10.1007/s10668-020-01055-8 |
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author | Arif, Mohammad Sengupta, Soumita |
author_facet | Arif, Mohammad Sengupta, Soumita |
author_sort | Arif, Mohammad |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7596317 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Springer Netherlands |
record_format | MEDLINE/PubMed |
spelling | pubmed-75963172020-10-30 Nexus between population density and novel coronavirus (COVID-19) pandemic in the south Indian states: A geo-statistical approach Arif, Mohammad Sengupta, Soumita Environ Dev Sustain Article 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. Springer Netherlands 2020-10-30 2021 /pmc/articles/PMC7596317/ /pubmed/33144832 http://dx.doi.org/10.1007/s10668-020-01055-8 Text en © Springer Nature B.V. 2020 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 Arif, Mohammad Sengupta, Soumita Nexus between population density and novel coronavirus (COVID-19) pandemic in the south Indian states: A geo-statistical approach |
title | Nexus between population density and novel coronavirus (COVID-19) pandemic in the south Indian states: A geo-statistical approach |
title_full | Nexus between population density and novel coronavirus (COVID-19) pandemic in the south Indian states: A geo-statistical approach |
title_fullStr | Nexus between population density and novel coronavirus (COVID-19) pandemic in the south Indian states: A geo-statistical approach |
title_full_unstemmed | Nexus between population density and novel coronavirus (COVID-19) pandemic in the south Indian states: A geo-statistical approach |
title_short | Nexus between population density and novel coronavirus (COVID-19) pandemic in the south Indian states: A geo-statistical approach |
title_sort | nexus between population density and novel coronavirus (covid-19) pandemic in the south indian states: a geo-statistical approach |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7596317/ https://www.ncbi.nlm.nih.gov/pubmed/33144832 http://dx.doi.org/10.1007/s10668-020-01055-8 |
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