HIGH-DIMENSIONAL DATA INTEGRATION IN MULTI-SOURCE POPULATION HEALTH SURVEILLANCE SYSTEMS

We examine how integrated population health data systems influence the operational effectiveness of national surveillance infrastructures and introduce the Integrated Population Health Intelligence Model as a structured analytical framework linking digital health data integration with surveillance performance. The empirical analysis uses the Multi Source Global Health Surveillance Dataset covering 2020 to 2025 and combines institutional records from national health information platforms, laboratory reporting systems, global surveillance repositories, and governance indicators across Ghana. The results show that higher levels of system interoperability, stronger cross institutional data exchange, and faster real time analytical processing significantly improve disease detection speed, outbreak response coordination, epidemiological forecasting accuracy, and the quality of evidence based health policy decisions. The evidence also reveals that institutional governance capacity strengthens the relationship between integrated data infrastructures and surveillance outcomes by enabling coordinated analysis and policy implementation. These findings extend digital epidemiology and health governance theory by demonstrating that effective surveillance intelligence emerges from the interaction of technological integration and institutional capacity. The model offers practical guidance for governments and health agencies seeking to strengthen national epidemic intelligence systems through integrated health data architectures and coordinated governance structures.