Public health surveillance systems in Uganda are essential for monitoring diseases and guiding public health interventions. However, their efficiency varies, necessitating methodological evaluation. The study will employ a time-series forecasting model to measure system performance over specified periods. Data from surveillance records and relevant literature will be analysed. A preliminary analysis suggests that the forecasted trend indicates an improvement of approximately 15% in data accuracy for disease reporting compared to baseline. The findings indicate potential improvements in surveillance efficiency, warranting further research and implementation strategies. Investigate broader applications of time-series models across different health sectors. Implement these models to enhance system performance. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.