Data Sheet 1_Weighted ensemble learning outperforms statistical, machine-learning, and deep-learning models for varicella forecasting: a 12-model benchmarking study using 11-year surveillance data from Nanjing, China.pdf

The optimal model was retrained on the full series to generate 2026–2027 baseline projections with bootstrap 95% prediction intervals.</p>Results<p>Incidence rose significantly (Kendall’s τ = 0.34, p < 0.001; Theil–Sen slope 2.57 cases/month) with strong annual seasonality (F_s = 0.82), a primary November–December peak, a secondary May–June surge, and a wavelet-detected biennial mode intensifying after 2019.

The inverse-RMSE weighted ensemble outperformed all 11 competitors (RMSE 133.49; MAPE 19.30%; R² = 0.74), reducing RMSE by 23.7% versus XGBoost and 44.6% versus SARIMA, and surpassing Ridge-stacked meta-learning under the moderate sample size. Forecasts projected 9,980 and 10,210 cases for 2026 and 2027, respectively.</p>Conclusion<p>A transparently weighted ensemble delivered the most accurate short-term varicella forecasts for Nanjing, aligning with international forecast-hub practice and providing a pre-registered baseline for evaluating Jiangsu’s two-dose policy.</p>