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

Abstract 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...

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Autores principales:	Asif Iqbal Middya, Sarbani Roy
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/32aa5106d710434f97ba19b214a11cbc
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spelling	oai:doaj.org-article:32aa5106d710434f97ba19b214a11cbc2021-12-02T15:51:12ZGeographically varying relationships of COVID-19 mortality with different factors in India10.1038/s41598-021-86987-52045-2322https://doaj.org/article/32aa5106d710434f97ba19b214a11cbc2021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-86987-5https://doaj.org/toc/2045-2322Abstract 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 ( $$R^{2}=0.97$$ R 2 = 0.97 ) with smaller Akaike Information Criterion (AICc $$=-66.42$$ = - 66.42 ) as compared to the global method (ordinary least square). The GWR method also comes up with lower spatial autocorrelation (Moran’s $$I=-0.0395$$ I = - 0.0395 and $$p < 0.01$$ p < 0.01 ) in the residuals. It is found that more than 86% of local $$R^{2}$$ R 2 values are larger than 0.60 and almost 68% of $$R^{2}$$ R 2 values are within the range 0.80–0.97. Moreover, some interesting local variations in the relationships are also found.Asif Iqbal MiddyaSarbani RoyNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Asif Iqbal Middya Sarbani Roy Geographically varying relationships of COVID-19 mortality with different factors in India
description	Abstract 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 ( $$R^{2}=0.97$$ R 2 = 0.97 ) with smaller Akaike Information Criterion (AICc $$=-66.42$$ = - 66.42 ) as compared to the global method (ordinary least square). The GWR method also comes up with lower spatial autocorrelation (Moran’s $$I=-0.0395$$ I = - 0.0395 and $$p < 0.01$$ p < 0.01 ) in the residuals. It is found that more than 86% of local $$R^{2}$$ R 2 values are larger than 0.60 and almost 68% of $$R^{2}$$ R 2 values are within the range 0.80–0.97. Moreover, some interesting local variations in the relationships are also found.
format	article
author	Asif Iqbal Middya Sarbani Roy
author_facet	Asif Iqbal Middya Sarbani Roy
author_sort	Asif Iqbal Middya
title	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_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_sort	geographically varying relationships of covid-19 mortality with different factors in india
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/32aa5106d710434f97ba19b214a11cbc
work_keys_str_mv	AT asifiqbalmiddya geographicallyvaryingrelationshipsofcovid19mortalitywithdifferentfactorsinindia AT sarbaniroy geographicallyvaryingrelationshipsofcovid19mortalitywithdifferentfactorsinindia
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Geographically varying relationships of COVID-19 mortality with different factors in India

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