Public health surveillance systems in Kenya play a critical role in monitoring and responding to infectious diseases. However, their effectiveness in measuring clinical outcomes is often under-researched. The study will apply a seasonal autoregressive integrated moving average (SARIMA) model to forecast ILI trends. Uncertainty in forecasts will be quantified using robust standard errors, ensuring reliable predictions for public health interventions. Forecast accuracy varied by season, with an average error rate of ±10% for influenza-like symptoms, highlighting the need for continuous system improvement and validation. The SARIMA model demonstrated promise in forecasting ILI rates but requires further validation across different disease types and regions to enhance its utility for public health decision-making. Public health authorities should integrate feedback loops into surveillance systems, regularly validating forecast accuracy with real-time data inputs. This will ensure the system remains effective and responsive to emerging infectious threats. SARIMA model, time-series forecasting, ILI rates, public health surveillance, Kenya Treatment effect was estimated with logit (pᵢ) =₀+^ Xᵢ, and uncertainty reported using confidence-interval based inference.
Kinyanjui et al. (Wed,) studied this question.
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