Addressing multicollinearity in spatial modelling: A district level spatial analysis of pandemic COVID-19 in India.

Abstract

This study focuses on conducting spatial analysis of COVID-19 at the district level in India. Leveraging data from www.covidindia.org for confirmed cases and deaths, and integrating population characteristics from the National Family Health Survey 5 (2019-2021) and supplementary sources. The objective is to identify risk factors using spatial modelling techniques while addressing multicollinearity through principal component analysis (PCA). This study utilizes spatial analysis to identify COVID-19 hotspots and coldspots at the district level in India. It highlights highly affected districts such as Mumbai, Pune, Chennai, Kolkata, and Bengaluru, as well as low affected districts in central and north-eastern regions. The study utilized the spatial lag model (SLM), spatial error model (SEM), geographical weighted regression (GWR), and multiscale geographical weighted regression (MGWR) models to analyse the impact of demographic, socioeconomic, climatic, and comorbidity factors on COVID-19, accounting for spatial proximity. Among these models, MGWR exhibited superior performance. Key risk factors associated with the COVID-19 phenomenon identified, providing insights into the impact of household conditions, educational level of women, tobacco and alcohol consumption rates, number of health centres, and climatic factors. Moreover, the local coefficients estimated by MGWR model furnish detailed information regarding the strength and direction of the relationships between predictors and COVID-19 cases and deaths within each spatial unit. The findings emphasize the significance of addressing multicollinearity in spatial modelling. It is beneficial for accurate parameter estimation, proper interpretation of coefficients, improved spatial analysis, and providing reliable insights to support decision-making in spatial contexts.