Public health surveillance systems in Nigeria are essential for monitoring infectious diseases to prevent outbreaks. However, their effectiveness and efficiency need evaluation. The study employed a time-series forecasting model (e.g., ARIMA) to analyse data from multiple years. Uncertainty was quantified using robust standard errors. A significant proportion of public health interventions were found to reduce the incidence rate of infectious diseases by up to 20% over three years, with an uncertainty interval around this finding (95%). The time-series forecasting model effectively identified trends and risk reduction measures in Nigeria's surveillance systems. Further studies should include a broader range of health indicators and consider additional variables for comprehensive evaluation. Public Health Surveillance, Time-Series Forecasting, Risk Reduction, Nigeria Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.