Optimizing Vaccine Distribution Networks in Heterogeneous Populations Using Geospatial Data and Demographic Risk Models

Abstract

Effective and equitable vaccine distribution is a complex logistical challenge, especially during pandemics. Traditional, one-size-fits-all strategies often fail to address the heterogeneity within populations, leading to inadequate service for high-risk and vulnerable subgroups. This study introduces a novel, integrated approach to optimize vaccine distribution networks by leveraging multi-source geospatial data and advanced demographic risk models. Methods: The proposed framework utilizes Geographic Information Systems (GIS), spatial statistics, and operational research to overcome the limitations of standard models. It dynamically integrates key metrics—including population density, vulnerability, healthcare access, and logistical constraints—to effectively prioritize resource allocation. This integration allows for a nuanced, data-driven approach that moves beyond simplistic population-based strategies. Results: The optimized distribution strategy allocates resources based not only on density but also on vulnerability and access metrics, ensuring that the most at-risk communities are prioritized. This refined approach enables the effective deployment of mobile vaccination centers and strategic placement of fixed sites. By considering epidemiological risk and socio-economic determinants, this framework significantly enhances accessibility and equity, preventing the exacerbation of existing health disparities. Conclusion: The integration of geospatial and demographic risk modeling provides a more nuanced and equitable framework for vaccine resource allocation, thereby enhancing public health outcomes during mass vaccination efforts. Further refinement through microplanning is essential, particularly in resource-limited settings, to precisely identify and reach all target populations.