BEYOND SINGLE-STREAM SURVEILLANCE: A COMPARATIVE EVALUATION OF FEDERATED MULTIMODAL AI FRAMEWORKS FOR PANDEMIC EARLY WARNING ACROSS CLINICAL, GENOMIC, MOBILITY, ENVIRONMENTAL, AND BEHAVIORAL DATA STREAMS

Abstract

The global epidemic intelligence landscape comprises a diverse set of digital surveillance systems developed over three decades, each addressing specific gaps in conventional outbreak detection. This paper presents a systematic comparative evaluation of seven pandemic surveillance frameworks:  ProMED-mail, HealthMap, BlueDot, EPIWATCH, Epitweetr, CDC BioSense, and HealthVigil across eight standardized dimensions: clinical data integration, genomic surveillance, mobility signal incorporation, environmental and wastewater data, federated privacy-preserving architecture, cross-border deployment, explainable AI, and quantified detection lead time. The evaluation draws on the primary published literature for each system and applies consistent assessment criteria across all frameworks. The findings reveal a three-generation structural progression in the field from human-moderated single-stream reporting through AI-assisted single-platform monitoring toward multimodal federated integration, with each successive generation addressing limitations of its predecessor. Among the systems evaluated, the analysis identifies federated multimodal integration as the dimension cluster most strongly associated with detection lead time, with the only system combining all five stream types and a federated privacy-preserving architecture achieving a 43-day detection lead time and a 37 percent false alarm reduction. The evaluation also identifies the absence of standardized performance reporting as a persistent methodological gap across the field, with most systems lacking published quantitative benchmarks that would enable rigorous cross-system comparison.