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

Public health surveillance systems in Nigeria are essential for monitoring diseases and managing outbreaks efficiently. However, their effectiveness varies significantly across different regions. This meta-analysis employs systematic review techniques to aggregate data from multiple studies conducted in Nigeria. Time-series forecasting models are used to analyse trends and forecast future performance, incorporating uncertainty through robust standard errors. The analysis reveals that the implementation of time-series forecasting significantly improved the yield metrics by an average of 15% across monitored health indicators. Our findings underscore the potential of adopting advanced statistical models for enhancing surveillance system efficiency in Nigeria, particularly in improving disease management outcomes. Public health authorities should prioritise the integration and validation of time-series forecasting into their current surveillance protocols to maximise yield improvement. public health surveillance, Nigeria, time-series forecasting, yield improvement, robust standard errors Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.