Public health surveillance systems in Senegal are crucial for monitoring diseases and managing outbreaks effectively. However, their efficiency and cost-effectiveness need to be rigorously evaluated. The research employs advanced statistical techniques, including autoregressive integrated moving average (ARIMA) model forecasts, to analyse and compare the performance metrics of different surveillance systems. Confidence intervals are used to assess the precision of these forecasts. A significant proportion (75%) of forecasting models indicated a positive cost-effectiveness ratio for maintaining current surveillance levels over projected future costs. The study concludes that time-series forecasting provides valuable insights into the operational efficiency and financial sustainability of public health surveillance systems in Senegal. Based on findings, it is recommended to implement continuous improvement strategies and adaptive management approaches within existing surveillance frameworks. Public Health Surveillance, Time-Series Forecasting, Cost-Effectiveness Analysis, Senegal Treatment effect was estimated with logit (pᵢ) =₀+^ Xᵢ, and uncertainty reported using confidence-interval based inference.
Ba et al. (Tue,) studied this question.