Abstract
Despite strict containment measures in India, COVID-19 cases significantly increased between 2020 and 2022, partly due to mass containment strategies based on administrative boundaries, disrupting infected and uninfected populations. This study addresses the gap in research on socio-spatial factors influencing COVID-19 infection by using micro-level data from urban wards of an Indian City, aiming to identify key socio-economic and spatial determinants of disease spread. Based on the data collected from 1194 individuals across 75 wards of Kozhikode City, the study employed a binary logistic regression model to examine how these variables affect COVID-19 test outcomes and a hotspot analysis to analyze the spatial dynamics of infection. The presence of comorbidities (Odds Ratio = 8.61) and the stringency index (Odds Ratio = 1.63) were the most significant factors associated with increased COVID-19 infection risk, highlighting the vulnerability of individuals with chronic health conditions and the complex relationship between government restrictions and case numbers. Residential density, essential job status, proximity to public amenities, and frequency of trips made were also strongly linked to higher infection rates, underscoring the role of socio-spatial factors in virus transmission. Hotspot analysis revealed spatial clustering of infections in urban cores, reinforcing the spatial nature of disease spread and the need for localized, data-driven interventions. The model achieved 86.5 % accuracy, demonstrating its effectiveness in explaining COVID-19 infection using socio-spatial parameters. The findings form a foundation for targeted public health interventions and data-driven strategies to manage infection spread, emphasizing the role of people, activities, and spaces in transmission dynamics.