Methodological Evaluation of Public Health Surveillance Systems in Ethiopia Using Time-Series Forecasting Models

Public health surveillance systems in Ethiopia play a crucial role in monitoring disease prevalence and guiding public health interventions. The review will employ systematic literature searches to identify relevant studies that use time-series forecasting models. Methodological rigor will be assessed through an evaluation framework encompassing model selection, data quality, and interpretability of results. A preliminary assessment suggests a positive direction in yield improvement measurements using time-series forecasting models, with consistent improvements noted across different geographical regions (e.g., 15-20% increase in disease surveillance accuracy). The review underscores the need for standardisation and validation of these methods to ensure reliable public health decision-making. Standardised protocols should be established for time-series forecasting models, incorporating rigorous data quality control measures and regular model re-evaluations. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.