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Background <p>Seasonal influenza poses a significant public health burden, particularly in China, where regional outbreaks vary widely and timely surveillance is constrained by reporting delays. While traditional surveillance systems rely on virological data, population-level internet search behavior has emerged as a complementary signal for early epidemic detection.</p> Objective <p>This study aims to develop a hybrid deep learning model, LSTCNet, that integrates online search data from Baidu Index with influenza surveillance records for accurate and timely forecasting of influenza trends in both Southern and Northern China.</p> Methods <p>We developed a hybrid deep learning model, LSTCNet, to predict influenza trends in real time by integrating Baidu Index search data and virological surveillance records from Northern and Southern China. LSTCNet combines a Bidirectional Long Short-Term Memory (BiLSTM) network for modeling long-term dependencies and a Temporal Convolutional Network (TCN) for capturing short-term patterns, further enhanced by temporal and channel-wise attention mechanisms. Feature selection was conducted using LightGBM and SHAP to identify the top 10 region-specific Baidu search terms most strongly associated with influenza positivity rates. The model was trained on weekly data from 2011 to 2022 and tested on data from mid-2022 to 2024. Its performance was evaluated against LSTM, BiLSTM, CNN-LSTM, LSTM with Attention (LSTM-Attn.), TCN, and Transformer baselines using RMSE, MAE, MAPE, and R² metrics. Extensive ablation studies were also performed to assess the contribution of each LSTCNet component.</p> Results <p>LSTCNet consistently outperformed baseline models across multiple forecasting horizons and both geographic regions. For Northern China, LSTCNet achieved the highest accuracy for 1-week forecasts with an RMSE of 0.0655 and R² of 0.931, while maintaining robust performance for 4-week forecasts (RMSE = 0.1382, R² = 0.695). In Southern China, LSTCNet obtained an RMSE of 0.0822 and R² of 0.925 for 1-week forecasts, with a moderate decline at 4 weeks (RMSE = 0.1990, R² = 0.560). Compared to traditional models like LSTM, and Transformer, LSTCNet demonstrated superior accuracy and stability, particularly in capturing epidemic peaks and trend inflection points. Ablation studies further confirmed the importance of integrating BiLSTM, TCN, and attention mechanisms; removing any component significantly reduced performance. Region-specific feature selection using SHAP-enhanced Baidu Index terms also improved model interpretability and predictive precision.</p> Conclusion <p>LSTCNet demonstrates strong potential for timely influenza forecasting by leveraging region-specific online behavioral data and advanced hybrid architectures, offering a scalable and interpretable tool for epidemic monitoring and supporting more responsive public health interventions.</p>

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LSTCNet: timely prediction of influenza in China using Baidu index and LSTM-TCN hybrid network

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月6日

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  • Wei He,
  • Xuanfeng Li,
  • Haining He,
  • Yiping Li,
  • Ning Sun,
  • Chitin Hon

摘要

Background

Seasonal influenza poses a significant public health burden, particularly in China, where regional outbreaks vary widely and timely surveillance is constrained by reporting delays. While traditional surveillance systems rely on virological data, population-level internet search behavior has emerged as a complementary signal for early epidemic detection.

Objective

This study aims to develop a hybrid deep learning model, LSTCNet, that integrates online search data from Baidu Index with influenza surveillance records for accurate and timely forecasting of influenza trends in both Southern and Northern China.

Methods

We developed a hybrid deep learning model, LSTCNet, to predict influenza trends in real time by integrating Baidu Index search data and virological surveillance records from Northern and Southern China. LSTCNet combines a Bidirectional Long Short-Term Memory (BiLSTM) network for modeling long-term dependencies and a Temporal Convolutional Network (TCN) for capturing short-term patterns, further enhanced by temporal and channel-wise attention mechanisms. Feature selection was conducted using LightGBM and SHAP to identify the top 10 region-specific Baidu search terms most strongly associated with influenza positivity rates. The model was trained on weekly data from 2011 to 2022 and tested on data from mid-2022 to 2024. Its performance was evaluated against LSTM, BiLSTM, CNN-LSTM, LSTM with Attention (LSTM-Attn.), TCN, and Transformer baselines using RMSE, MAE, MAPE, and R² metrics. Extensive ablation studies were also performed to assess the contribution of each LSTCNet component.

Results

LSTCNet consistently outperformed baseline models across multiple forecasting horizons and both geographic regions. For Northern China, LSTCNet achieved the highest accuracy for 1-week forecasts with an RMSE of 0.0655 and R² of 0.931, while maintaining robust performance for 4-week forecasts (RMSE = 0.1382, R² = 0.695). In Southern China, LSTCNet obtained an RMSE of 0.0822 and R² of 0.925 for 1-week forecasts, with a moderate decline at 4 weeks (RMSE = 0.1990, R² = 0.560). Compared to traditional models like LSTM, and Transformer, LSTCNet demonstrated superior accuracy and stability, particularly in capturing epidemic peaks and trend inflection points. Ablation studies further confirmed the importance of integrating BiLSTM, TCN, and attention mechanisms; removing any component significantly reduced performance. Region-specific feature selection using SHAP-enhanced Baidu Index terms also improved model interpretability and predictive precision.

Conclusion

LSTCNet demonstrates strong potential for timely influenza forecasting by leveraging region-specific online behavioral data and advanced hybrid architectures, offering a scalable and interpretable tool for epidemic monitoring and supporting more responsive public health interventions.