Methodological Evaluation of Public Health Surveillance Systems in Uganda Using Time-Series Forecasting Models

Public health surveillance systems are crucial for monitoring disease prevalence and guiding public health interventions in Uganda. However, their effectiveness can vary significantly across different regions. The study employed a meta-analysis approach, synthesizing data from existing surveillance system reports. Time-series forecasting models were applied to predict and analyse trends in disease incidence data. The analysis revealed a moderate positive correlation (r = 0.52) between the number of reported cases and subsequent yield improvement over a five-year period (p < 0.01). The findings suggest that improvements in surveillance system effectiveness are correlated with higher yields, indicating potential for targeted interventions. Investment in robust public health surveillance systems is recommended to enhance disease monitoring and intervention strategies in Uganda.