Stage-aware deep learning with adversarial data synthesis enables prospective season-scale A/H1N1 influenza forecasting in China.

Authors

Lirong Zhang, Haocheng Zhou, Yuxin Zhu, Panpan Ma, Jiaxin Yuan, Yunxiao Qiu, Huayu Qiao, Jiaxiong Zheng, Suyang Liu, Liping Li, Zicheng Cao

Published in

Infectious Disease Modelling. Volume 12. Issue 1. Pages 94-103. Epub Jul 28, 2026.

Abstract

Accurate influenza forecasting is essential for public health preparedness, yet many models require future covariates, provide limited interpretability, and degrade under post-pandemic regime shifts. We propose a Stage-Aware Multimodal Neural Network (SAMNN) that integrates multimodal temporal features using configurations adapted to heterogeneous transmission regimes and supports season-ahead scenario forecasting using adversarially synthesized covariates. Using 13 years of national A/H1N1 surveillance data from mainland China (2011∼2024), SAMNN was evaluated across three epidemiologically distinct seasons spanning ∼20-fold differences in peak intensity. SAMNN achieved R ² values of 0.82-0.95, 0.82-0.97, and 0.55-0.97 for 1-week-, 2-week-, and 4-week-ahead forecasting, respectively, and generally maintained competitive performance relative to five baseline models across forecast horizons. SHAP attribution showed that epidemiological signals dominated predictions, with context-dependent contributions from climatic and social-context features. To support prospective scenario forecasting, we used an adversarial synthetic covariate-generation pipeline to produce season-ahead forecasts for 2024/25 using information available at the prespecified forecast origin.

PMID:
42571518
Bibliographic data and abstract were imported from PubMed on 09 Aug 2026.

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