Cargando…

Geographically varying relationships of COVID-19 mortality with different factors in India

COVID-19 is a global crisis where India is going to be one of the most heavily affected countries. The variability in the distribution of COVID-19-related health outcomes might be related to many underlying variables, including demographic, socioeconomic, or environmental pollution related factors....

Descripción completa

Detalles Bibliográficos
Autores principales: Middya, Asif Iqbal, Roy, Sarbani
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8041785/
https://www.ncbi.nlm.nih.gov/pubmed/33846443
http://dx.doi.org/10.1038/s41598-021-86987-5
_version_ 1783678007608606720
author Middya, Asif Iqbal
Roy, Sarbani
author_facet Middya, Asif Iqbal
Roy, Sarbani
author_sort Middya, Asif Iqbal
collection PubMed
description COVID-19 is a global crisis where India is going to be one of the most heavily affected countries. The variability in the distribution of COVID-19-related health outcomes might be related to many underlying variables, including demographic, socioeconomic, or environmental pollution related factors. The global and local models can be utilized to explore such relations. In this study, ordinary least square (global) and geographically weighted regression (local) methods are employed to explore the geographical relationships between COVID-19 deaths and different driving factors. It is also investigated whether geographical heterogeneity exists in the relationships. More specifically, in this paper, the geographical pattern of COVID-19 deaths and its relationships with different potential driving factors in India are investigated and analysed. Here, better knowledge and insights into geographical targeting of intervention against the COVID-19 pandemic can be generated by investigating the heterogeneity of spatial relationships. The results show that the local method (geographically weighted regression) generates better performance ([Formula: see text] ) with smaller Akaike Information Criterion (AICc [Formula: see text] ) as compared to the global method (ordinary least square). The GWR method also comes up with lower spatial autocorrelation (Moran’s [Formula: see text] and [Formula: see text] ) in the residuals. It is found that more than 86% of local [Formula: see text] values are larger than 0.60 and almost 68% of [Formula: see text] values are within the range 0.80–0.97. Moreover, some interesting local variations in the relationships are also found.
format Online
Article
Text
id pubmed-8041785
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Nature Publishing Group UK
record_format MEDLINE/PubMed
spelling pubmed-80417852021-04-13 Geographically varying relationships of COVID-19 mortality with different factors in India Middya, Asif Iqbal Roy, Sarbani Sci Rep Article COVID-19 is a global crisis where India is going to be one of the most heavily affected countries. The variability in the distribution of COVID-19-related health outcomes might be related to many underlying variables, including demographic, socioeconomic, or environmental pollution related factors. The global and local models can be utilized to explore such relations. In this study, ordinary least square (global) and geographically weighted regression (local) methods are employed to explore the geographical relationships between COVID-19 deaths and different driving factors. It is also investigated whether geographical heterogeneity exists in the relationships. More specifically, in this paper, the geographical pattern of COVID-19 deaths and its relationships with different potential driving factors in India are investigated and analysed. Here, better knowledge and insights into geographical targeting of intervention against the COVID-19 pandemic can be generated by investigating the heterogeneity of spatial relationships. The results show that the local method (geographically weighted regression) generates better performance ([Formula: see text] ) with smaller Akaike Information Criterion (AICc [Formula: see text] ) as compared to the global method (ordinary least square). The GWR method also comes up with lower spatial autocorrelation (Moran’s [Formula: see text] and [Formula: see text] ) in the residuals. It is found that more than 86% of local [Formula: see text] values are larger than 0.60 and almost 68% of [Formula: see text] values are within the range 0.80–0.97. Moreover, some interesting local variations in the relationships are also found. Nature Publishing Group UK 2021-04-12 /pmc/articles/PMC8041785/ /pubmed/33846443 http://dx.doi.org/10.1038/s41598-021-86987-5 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Middya, Asif Iqbal
Roy, Sarbani
Geographically varying relationships of COVID-19 mortality with different factors in India
title Geographically varying relationships of COVID-19 mortality with different factors in India
title_full Geographically varying relationships of COVID-19 mortality with different factors in India
title_fullStr Geographically varying relationships of COVID-19 mortality with different factors in India
title_full_unstemmed Geographically varying relationships of COVID-19 mortality with different factors in India
title_short Geographically varying relationships of COVID-19 mortality with different factors in India
title_sort geographically varying relationships of covid-19 mortality with different factors in india
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8041785/
https://www.ncbi.nlm.nih.gov/pubmed/33846443
http://dx.doi.org/10.1038/s41598-021-86987-5
work_keys_str_mv AT middyaasifiqbal geographicallyvaryingrelationshipsofcovid19mortalitywithdifferentfactorsinindia
AT roysarbani geographicallyvaryingrelationshipsofcovid19mortalitywithdifferentfactorsinindia