An experimental platform for interpreting open-source health data though integration with dynamic disease models and geoplots

We report our work in progress to create an experimental decision support architecture for evidence-based health policy planning employing open data records. It combines a population-level disease control and prevention simulation with de-identified data for patients hospitalized with diabetes in Albany County, New York, USA over a period of years. The geo-coding associated with this combined dataset is visualized through dynamic plots of neighborhood hot-spots over multiple years. This multi-model approach provides a novel tool for reflecting on historical data, policy choices and possible long-term outcomes in numeric and geo-space.