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

Public health surveillance systems in Senegal are critical for monitoring and responding to infectious diseases. However, their effectiveness can vary over time. The study employed ARIMA (p, d, q) model for forecasting future trends in disease incidence data. Uncertainty was quantified by reporting robust standard errors and 95% confidence intervals. The ARIMA (1, 1, 0) model provided a direction of decline (reduction) in reported cases with a proportion reduction of 42% over the study period. Time-series forecasting models offer valuable insights for monitoring and improving public health surveillance systems. Continuous evaluation and adaptation of surveillance strategies are recommended based on model predictions and real-time data feedback loops. Public Health Surveillance, ARIMA Model, Time Series Forecasting, Risk Reduction