A multiscale spatial diagnostic framework for built environment determinants of epidemic risk: Evidence from Wuhan

Understanding how the built environment shapes epidemic risk at different spatial scales is critical for place-based health research and policy. However, existing studies often assume uniform effects and overlook that explanatory variables may operate at distinct and sometimes nonlinear scales within cities. In this study, we adopt a progressive diagnostic framework to analyze sub-district level built-environment, socioeconomic, and natural ecological factors associated with COVID-19 infection rates in Wuhan, China. We find hospital accessibility has highly localized effects, while metro proximity, POI density, and housing price show influence at broader meso and macro scales. Clustering of Multiscale Geographically Weighted Regression (MGWR) coefficients identifies two epidemic response zones: eastern districts where constrained healthcare access and socioeconomic vulnerability dominate, and central–western districts where socioeconomic and transit factors are stronger. These findings underscore uneven spatial vulnerability and the need to avoid city-wide “one-size-fits-all” policies. Our framework offers transferable methodological and policy insights for designing interventions tuned to place and scale across urban health contexts.