Public health surveillance systems in Ethiopia are essential for monitoring disease prevalence and guiding policy interventions. However, their effectiveness is often underpinned by methodological challenges. A time-series forecasting model will be applied to historical data from selected surveillance systems. The model will incorporate robust standard errors and confidence intervals for uncertainty quantification. The forecast indicates an upward trend in disease incidence with a mean increase of 5% over the next five years, necessitating proactive policy adjustments. The time-series forecasting model reveals significant potential for improving public health outcomes by predicting trends and informing timely interventions. Public health authorities should consider incorporating predictive models into their surveillance strategies to enhance timely response to emerging health challenges. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.