These factors play a very important role in determining the patterns of COVID-19 mortality.īoth global and local models can be utilized to explore the above-mentioned associations. Also, there are research works that examine the association of COVID-19-related health outcomes with different socio-economic, environmental, and region-specific factors 5– 7. In this context, recent studies are focusing on exploring person-specific risk factors for COVID-19-related health outcomes 2– 4. There is unprecedented urgency to investigate the major factors that are related to COVID-19 death. Because of its unpredictable nature and lack of appropriate medications, COVID-19 is now a global health concern. The novel coronavirus disease (COVID-19) has spread rapidly to all parts of the world, causing almost 2.5 million deaths as of mid-February 2021 1. Moreover, some interesting local variations in the relationships are also found. It is found that more than 86% of local R 2 values are larger than 0.60 and almost 68% of R 2 values are within the range 0.80–0.97. The GWR method also comes up with lower spatial autocorrelation (Moran’s I = - 0.0395 and p < 0.01) in the residuals. The results show that the local method (geographically weighted regression) generates better performance ( R 2 = 0.97) with smaller Akaike Information Criterion (AICc = - 66.42) as compared to the global method (ordinary least square). 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. 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. It is also investigated whether geographical heterogeneity exists in the relationships. 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. The global and local models can be utilized to explore such relations. 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. COVID-19 is a global crisis where India is going to be one of the most heavily affected countries.
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