Spatio-Temporal Modeling for Multi-County Opioid Overdose Surveillance: A Unified Graph Convolutional Framework

This study introduces a unified spatio-temporal predictive framework for estimating opioid-involved mortality across five U.S. regions. A spatio-temporal graph convolutional network was used to generate monthly grid level predictions while incorporating local spatial adjacency and temporal progression derived from observed mortality trends. The standardized representation enables comparison of spatial clustering, temporal variability, and prediction behavior across jurisdictions that differ in geographic layout, population distribution, and mortality burden. The framework provides a basis for examining how regional characteristics relate to predictive patterns and offers a way to assess whether structures learned in one region also appear in others with distinct environments. This approach may support analysis of regional variation in mortality dynamics and help identify consistent features of opioid involvement that emerge across heterogeneous public health settings.