Public health surveillance systems in Kenya are crucial for monitoring infectious diseases such as cholera and malaria. However, their effectiveness varies widely across different regions. A meta-analysis was conducted on existing data from multiple regions within Kenya, employing ARIMA (AutoRegressive Integrated Moving Average) model for trend analysis. Uncertainty in forecasts was quantified with 95% confidence intervals. The average forecast error across all models was found to be within ±10%, indicating a reliable predictive capability of the ARIMA model. This study provides robust evidence on the reliability and effectiveness of time-series forecasting in evaluating public health surveillance systems in Kenya, offering a standardised method for future research. Public health officials should consider adopting these models to improve the timeliness and accuracy of disease outbreak predictions. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.