AI-Driven Strategies for Precision Public Health Interventions

The transition from traditional "one-size-fits-all" public health measures to Precision Public Health (PPH) is necessitated by the "Data-Rich, Information-Poor" (DRIP) syndrome, where massive healthcare data volumes fail to translate into rapid response times. This study proposes an AI-driven framework utilizing a Heterogeneous Data Architecture (HDA) to integrate multi-modal streams, including electronic health records (EHRs), environmental IoT telemetry, and digital phenotyping. To address infectious disease dynamics, a hybrid spatiotemporal model merging SEIR compartmental logic with Long Short-Term Memory (LSTM) networks was developed, achieving a +7-day lead-time advantage and an 80% reduction in transmission velocity. For chronic disease management, an unsupervised clustering approach identified four distinct patient phenotypes, including a critical "Invisible" high-risk group whose clinical markers are borderline but whose environmental stressors predict rapid deterioration. Empirical results demonstrate that while precision interventions incur higher upfront costs per capita, they reduce the "Effective Cost per Successful Outcome" by 33% to 70% across various domains. This framework not only stabilizes the healthcare system capacity Kcap but also provides a proactive blueprint for modernizing public health infrastructure through algorithmic stratification and personalized intervention pathways.