Analyzing the impact of social security systems on video-based public health surveillance

Introduction The increasing reliance on automated video-based systems for public health surveillance introduces some significant challenges in environments where social security systems influence health behaviors and outcomes. Motivated by the need to integrate governance structures with health informatics, this study proposes a framework for spatio-temporal health monitoring that explicitly accounts for the interaction between policy measures and population-level behavior. Traditional approaches often struggle to capture the stochastic nature of health-related signals, overlook spatial heterogeneity across communities, and remain insufficiently responsive to evolving policy interventions. Methods To address these limitations, we develop the hierarchical epidemiological transformer (HET), a deep learning architecture designed to model complex temporal and spatial dependencies in video-derived surveillance data. HET is augmented with a policy-aware dynamic calibration mechanism (PDCM), which incorporates real-time policy signals and statistical deviations to dynamic