Temporal trends and forecasting of respiratory mortality in Bangladesh: A SARIMA model for seasonal mortality risk and public health action

Background: Respiratory-related mortality in Bangladesh exhibits pronounced seasonal trends, yet systematic forecasting to guide public health interventions remains limited. This study analyzes temporal patterns and projects future mortality using nationally certified cause-of-death data. Methods: We applied a Seasonal Autoregressive Integrated Moving Average (SARIMA) model to monthly respiratory mortality records (January 2018–December 2024, 84 data points). Model selection was guided by AIC/BIC criteria, with performance evaluated via fivefold time-series cross-validation (training/test splits: Fold 1: 24/12, Fold 2: 36/12, Fold 3: 48/12, Fold 4: 60/12, Fold 5: 72/12). Seasonality was quantified through decomposition, and 18-month forecasts were generated with 95% prediction intervals. Results: Mortality demonstrated strong seasonal peaks in July, August (79.5% of death counts in July), and January (66.2% of death counts in July), aligning with monsoon and winter periods. The optimal SARIMA(1,2,3)(0,1,1)[12] model achieved robust accuracy: mean absolute scaled error (MASE) = 0.41, MAE = 259.123, and RMSE = 337.238 (cross-validation mean: MASE = 0.58, MAE = 520.13). Forecasts projected stable monthly averages of 1724 (95% CI: 782–2667) deaths in 2025 and 1711 (593–2830) in 2026, with highest occurrence in July, August then January. Residual diagnostics confirmed model adequacy (Ljung-Box p = 0.971), though non-normality (Shapiro-Wilk p < 0.001) was present, it was negligible. Conclusion: Respiratory mortality in Bangladesh follows predictable seasonal patterns, with monsoon and winter peaks demanding targeted interventions. The SARIMA model provides actionable forecasts for preemptive public health measures, such as pre-monsoon vaccination campaigns and pollution control. Future research should explore environmental covariates to enhance precision.