SPATIAL ANALYSIS OF COVID-19 SPREAD IN INDIA: INTEGRATING GIS AND AHP TO EVALUATE SOCIO-ENVIRONMENTAL RISK FACTORS

The COVID-19 pandemic brought about unprecedented challenges to global health systems, urban planning, and policy-making. It demonstrated the critical importance of understanding spatial patterns of disease transmission and emphasized the need for intelligent decision-support systems capable of integrating complex socio-environmental factors in real time. This research presents a statistical modeling, geospatial visualization, and multi-criteria decision analysis (MCDA) to evaluate the factors influencing the spread of COVID-19. This study focused on the entire Indian subcontinent, encompassing 28 states and 8 union territories, and employed five key determinants presumed to influence the spread and severity of COVID-19: Air Quality Index (AQI), the proportion of the population aged over 65 (AWP), Population Density (PD), Literacy Rate (LTR), and Availability of Doctors (ABD). Linear regression was used to assess the individual contribution of each factor to COVID-19 spread, while multiple regression offered insights into combined effects. The results of the multiple regression analysis were used to construct a pairwise comparison matrix under the Analytic Hierarchy Process (AHP) framework to derive normalized global weights for each factor. Additionally, AHP was applied at the local (area-specific) level to compute location-specific factor weights, capturing spatial heterogeneity across regions. Interpolation techniques were then applied to generate continuous surface maps for each factor as well as for the composite impact of all factors. This approach enables end-users to analyze and visualize spatial disease patterns dynamically. Overall, the study illustrates how geospatial intelligence, coupled with data science and MCDA, can inform effective pandemic response, strategic health interventions, and targeted policy actions. It also provides a generalized framework that can be extended to various infectious diseases, offering long-term value for health planning and epidemiological surveillance.