Geospatial modelling of COVID-19 outcomes: Insights from composite indicators for pandemic preparedness (In revision)

Effective pandemic preparedness requires a comprehensive understanding of the spatial distribution of the outbreak and its local driving factors. We applied a Geographically Weighted Principal Component Analysis (GWPCA) framework to capture the spatial heterogeneity of COVID-19 incidence and mortality at the county level across the United States. Unlike most studies that used standalone factors, this study integrates multiple standalone factors into a single score. We used robust GWPCA to construct composite indicators integrating epidemiological, demographic, socioeconomic, and healthcare-related variables, providing a more holistic representation of regional disparities. Our findings indicate that epidemiological indicators are the strongest predictors of COVID-19 incidence and mortality, with significant regional disparities observed. Counties in the West and South exhibit higher risks, while those in the Midwest and Northeast show comparatively lower risks. Interestingly, lower vaccination vulnerability does not necessarily correlate with lower COVID-19 mortality, underscoring the complexity of pandemic risk dynamics. By incorporating spatially adaptive composite indicators, this study provides enhanced predictive insights for public health planning and intervention strategies. The integration of spatial analytics with epidemiological modelling offers a scalable framework for regionalised pandemic response, ensuring more equitable resource allocation and targeted mitigation efforts in future health crises.