EpiGIS pro: an AI-powered geospatial intelligence platform for integrated disease surveillance and predictive analytics

BACKGROUND: Global infectious disease surveillance requires timely integration of heterogeneous data sources. Existing platforms typically address only one dimension, leaving cross-domain correlations unexplored. This study presents EpiGIS Pro, an AI-powered geospatial platform that consolidates automated disease event extraction, environmental monitoring, mobility analysis, and predictive analytics within a single web-based framework, providing a shared data infrastructure for future cross-domain modeling. METHODS: EpiGIS Pro employs a modular multi-application architecture built on Django REST Framework with PostGIS spatial extensions. Disease intelligence is automated from six source categories using Claude AI (Anthropic) for structured extraction with standardized epidemiological metadata. Environmental data, traffic congestion indicators, and international flight route data are ingested via scheduled Celery Beat tasks. Machine learning modules include Prophet-based time-series forecasting with multiplicative seasonality for WHO FluNet data, seasonal Z-score anomaly detection, and effective reproduction number (R_t) estimation. Semantic search is enabled through Voyage AI embeddings stored in pgvector-indexed PostgreSQL. A systematic evaluation framework covers six analytical dimensions across 9 countries spanning 6 years of data. RESULTS: The platform integrates 327,000 + records across six data domains. Claude AI extraction processed 353 disease event articles from 56 countries with 92.4% success and 100% completeness for disease type, severity, and priority fields. Using 2,868 WHO FluNet records across 9 countries (2019-2026), seasonality analysis correctly identified hemisphere-concordant peak timing in all 7 evaluated countries. Prophet multiplicative forecasting reduced RMSE by 21-29% compared to the seasonal naive baseline for the United States, and consistently outperformed log-transformed Prophet across all countries and horizons. R_t estimation demonstrated epidemiologically consistent patterns, with onset-period R_t exceeding trough-period R_t in Australia (1.27 vs. 1.09) and Japan (1.80 vs. 1.08). Cross-border risk assessment computed 4,828 flight corridor risk scores by linking disease activity with 633 international aviation routes. CONCLUSIONS: EpiGIS Pro demonstrates that consolidating AI-driven event extraction, multi-source environmental and mobility data, and geospatial analytics within a unified platform enhances situational awareness for global disease surveillance. The modular architecture and reproducible evaluation framework position EpiGIS Pro as both a practical surveillance tool and a research testbed for computational epidemiology.