Methodological Evaluation of Public Health Surveillance Systems in Senegal Using Time-Series Forecasting Models for Yield Improvement Assessment

Public health surveillance systems in Senegal are critical for monitoring infectious diseases. However, their effectiveness can be enhanced through advanced analytical tools. The study utilised a time-series forecasting model incorporating ARIMA (p, d, q) where p, d, q are parameters for the autoregressive, differencing, and moving average components respectively. The model was applied to historical data on disease incidence rates with robust standard errors estimated at ±5%. The analysis indicated a significant upward trend in disease surveillance accuracy over five consecutive years, suggesting an improvement of about 20% in reporting efficiency. The time-series forecasting model provided actionable insights for enhancing public health surveillance systems, leading to more accurate and timely disease outbreak notifications. Implementing the proposed model could lead to a substantial reduction in false negatives and improve overall health outcomes in Senegal. Public Health Surveillance, Time-Series Forecasting, ARIMA Model, Disease Incidence Rates