REAL-TIME BAYESIAN UPDATING ALGORITHMS FOR ADAPTIVE GLOBAL HEALTH RISK FORECASTING

We develop an adaptive forecasting framework that explains how real time health intelligence strengthens global health risk prediction and policy readiness. The analysis uses the Adaptive Bayesian Global Risk Forecast Model and the Global Real Time Health Risk Intelligence Dataset covering the period 2020 to 2025 with empirical validation from forty one public health experts working in epidemiology, surveillance, and health data analytics in Ghana. We examine how data integration capacity, real time epidemiological monitoring, and predictive risk analytics influence adaptive global health risk forecasting while institutional response capacity moderates these relationships. Results show that stronger digital data integration and continuous monitoring significantly improve early risk detection and forecasting accuracy, while predictive analytics produces the strongest contribution to forecasting performance. Institutional response capacity amplifies these effects by accelerating policy reaction time and improving resource allocation efficiency. The findings demonstrate that integrated intelligence infrastructure and predictive analytics form the operational foundation of adaptive health forecasting systems. The framework offers practical guidance for governments and global health institutions seeking to strengthen epidemic preparedness through integrated surveillance architecture and predictive decision support systems.