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 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.

Spatiotemporal clustering patterns and sociodemographic determinants of COVID-19 (SARS-CoV-2) infections in Helsinki, Finland

“…Traditional OLS regression methods and spatial regression models have been used to understand the contribution of socioeconomic, demographic and environmental determinants to explain spatial variability of COVID-19 incidences and mortality in the epidemic ( Mansour et al, 2021 ; Maiti et al, 2021 ; Mena et al, 2021 ; Middya and Roy 2021 ; Mollalo et al, 2020 ; Zhang and Schwartz, 2020 ; Snyder and Parks, 2020 ). However, in the majority of these studies, only one regression-based method was employed.…”

Impact of urbanisation and environmental factors on spatial distribution of COVID-19 cases during the early phase of epidemic in Singapore

“…metropolitan, highly urbanised built environment, hyper-dense globalised population) such as London and Wuhan. However, this study also explores a different climatic facet given Singapore’s tropical climate, compared to those cities with temperate climates 28 , 47 .…”

“…In that direction, in relation to environmental and epidemiological characters, the study primarily focused on revealing spatial patterns, relations, geographical spread, dependency and intensity of COVID-19 in the respective sub-zones of Singapore using geospatial modelling and analysis techniques. Specifically, the Geographical Weighted Regression (GWR), which has been reported to be efficient in identifying the spatial variations 26 – 28 , was employed in this study. The spatial relationships among the sub-zones assessed in this study are (i) between environmental parameters and COVID-19 cases, (ii) between the level of urbanisation and population density and the COVID-19 cases, and (iii) among the three different type of epidemiological cases (linked, unlinked and imported cases).…”

A spatial-temporal analysis at the early stages of the COVID-19 pandemic and its determinants: The case of Recife neighborhoods, Brazil

“…Cumulative count data has been applied in studies of COVID-19, especially at a small-scale level such as neighborhood or county-level [ 57 ] and grid [ 58 ]. The study of [ 59 ] predicted cumulative confirmed and cured cases of COVID-19 at a province-level, while [ 60 ] considered the number of deaths. Furthermore, the use of count data as a dependent variable instead of some sort of rates at the neighborhood-level is advocated by [ 61 ], and it is widely applied in the context of studies of crime [ 62 ].…”