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.

Background: Varicella imposes a sustained public health burden in China, where decades of voluntary single-dose immunization have been associated with recurrent school outbreaks and an age shift toward adolescents. Accurate forecasting of varicella incidence is crucial for public health preparedness and resource allocation. Systematic head-to-head benchmarks of statistical, machine-learning, deep-learning, and ensemble forecasts for varicella in Chinese metropolitan settings are scarce. Methods: Monthly varicella case counts for Nanjing, China (January 2015-December 2025) were extracted from the Chinese Notifiable Infectious Disease Reporting System. Trend, seasonality, and multi-year periodicity were characterized using the Mann-Kendall test, Theil-Sen estimator, STL decomposition, Lomb-Scargle periodogram, and continuous wavelet transform. Twelve models, SARIMA, ETS, TBATS, Prophet, random forest, XGBoost, support vector regression, NNAR, LSTM, and three ensembles (simple averaging, inverse-RMSE weighting, Ridge-stacked generalization), were trained on 108 months and evaluated on a pre-specified 24-month hold-out (2024-2025) using RMSE, MAE, MAPE, sMAPE, and R Results: Incidence rose significantly (Kendall's Conclusion: 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.