Public health surveillance systems are crucial for monitoring diseases in Ghana. However, their effectiveness can be improved through methodological evaluation and cost-effectiveness analysis. The study employed a time-series forecasting model (e.g., ARIMA) to analyse data from Ghana’s surveillance system. Uncertainty was quantified with robust standard errors and confidence intervals around forecasted values. A significant proportion (p < 0.05) of the variance in disease incidence could be explained by time-series forecasting models, indicating their predictive accuracy. The findings suggest that integrating advanced statistical models into public health surveillance systems can enhance their cost-effectiveness and reliability. Public health officials should consider implementing these methodologies to improve disease monitoring and resource allocation. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.