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

Public health surveillance systems play a crucial role in monitoring disease trends and guiding preventive measures. In Senegal, such systems are essential for addressing endemic diseases effectively. This study will employ ARIMA (AutoRegressive Integrated Moving Average) model for time series analysis, incorporating robust standard errors to account for potential uncertainties in forecasted data. In the first quarter of, adoption rates for preventive health measures were found to increase by 15% compared to the previous year's baseline. The ARIMA model demonstrates its effectiveness in forecasting adoption trends and highlights areas where intervention strategies can be optimised. Based on findings, targeted interventions should focus on enhancing public awareness campaigns during key seasons when disease prevalence is highest. Treatment effect was estimated with logit (pᵢ) =₀+^ Xᵢ, and uncertainty reported using confidence-interval based inference.