Time-Series Analysis and Age-Stratified Forecasting of Diarrheal Disease in Rwanda Using SARIMA Models

Diarrheal disease remains a major and persistent cause of morbidity and mortality in Rwanda, with substantial seasonal surges that strain routine services; however, transparent and operationally interpretable national forecasting has been underused for age-stratified burden.

We analyzed the Rwanda Health Management Information System (HMIS) monthly diarrhea case counts (January 2015-December 2025), stratified by age group (under-five and five-and-above), and developed validated Seasonal Autoregressive Integrated Moving Average (SARIMA) forecasts for January 2026-December 2027. Stationarity was assessed using the Augmented Dickey-Fuller test and addressed through differencing. Candidate models were selected via rolling 5-fold cross-validation: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and Mean Absolute Percentage Error (MAPE) and confirmed via Ljung-Box residual diagnostics, and benchmarked against seasonal naïve, Exponential Smoothing State-Space (ETS), and Seasonal-Trend decomposition using Loess (STL) + drift reference models.

Rwanda recorded 6,309,098 diarrhea cases during 2015-2025, with 49.2% among under-fives; while absolute counts were higher in those aged ≥5 years, risk remained consistently higher in under-fives (91.7-229.5 per 1000) than in those ≥5 years (17.9-34.3 per 1000). Both series showed strong annual seasonality with recurrent peaks in August-November, and forecasts suggest this pattern will persist through 2026-2027.

These findings suggest a provisional seasonal (pre-peak, peak, and post-peak) preparedness framework and age-differentiated planning signals, underscoring that burden and risk are not inter changeable across age groups.