Outcome: Led spatial and longitudinal analysis of a statewide disease surveillance and management program, identifying high-risk clusters and informing where intervention intensity should be increased, maintained, or scaled back.

Decision Context

• Disease risk varied substantially across geography and time, but resources were limited
• Program leaders needed evidence to prioritize surveillance and management efforts
• Existing summaries masked localized risk and delayed response

What I Led

• Analytical leadership: Designed and executed the statistical strategy for a $250K+ externally funded program evaluation
• Spatial risk modeling: Identified non-random spatial and temporal clustering indicating changing risk profiles
• Program evaluation: Assessed how intervention timing and intensity influenced downstream risk
• Policy translation: Delivered findings directly to state partners to guide surveillance and management prioritization

Impact

• Revealed emerging high-risk regions requiring increased management focus
• Demonstrated where sustained intervention had successfully reduced risk over time
• Provided defensible evidence to support adaptive, region-specific program decisions

This project demonstrates how spatial analytics can be used to evaluate real-world programs and support resource allocation decisions under uncertainty.