Public health surveillance systems are crucial for monitoring infectious diseases in Uganda to prevent outbreaks and control disease transmission. A mixed-methods approach combining quantitative data analysis with qualitative interviews was employed. The study used logistic regression models to assess system performance and identified key themes influencing system efficiency. The logistic regression model revealed that timely reporting of surveillance data significantly improved disease outbreak detection by a proportion of 30%, indicating the importance of prompt data submission in enhancing public health outcomes. This study provides insights into optimising Uganda’s public health surveillance systems, emphasising the need for robust data collection and efficient communication channels to improve yield improvement evaluations. Enhanced training programmes should be implemented to ensure accurate and timely reporting from healthcare providers. Collaboration between different sectors is essential to strengthen system resilience against disease outbreaks. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.