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Background <p>Accurate short-term influenza forecasting is important for early warning and public health preparedness, but routine sentinel surveillance may be delayed and may not fully capture behavioural and environmental signals related to influenza activity. This study aimed to develop and evaluate multi-source machine-learning models for short-term influenza forecasting in Hunan Province, China.</p> Methods <p>Weekly influenza-like illness plus (ILI+) data from 54 sentinel hospitals across 14 prefecture-level cities in Hunan Province were used to characterise influenza activity from 2015 to 2024. For forecasting during 2020–2024, ILI+ data were integrated with Baidu Search Index indicators, meteorological variables and air quality index data. ILI + was calculated as the product of the weekly ILI proportion and weekly influenza virus test positivity rate. Five models, including LSTM, GRU, stacked autoencoder, random forest and LightGBM, were compared for 1–3-week-ahead forecasting under four feature settings. Performance was evaluated using RMSE, MAE, MAPE, SMAPE and R², with SeasonalNaive52 as the baseline comparator.</p> Results <p>Influenza activity showed pronounced winter–spring seasonality with substantial interannual and city-level heterogeneity. At the provincial level, the all-features RF model achieved the lowest absolute errors among the machine-learning models (RMSE = 0.00882, MAE = 0.00389) and an out-of-sample R² of 0.723. Compared with the SeasonalNaive52 baseline, the machine-learning models showed lower RMSE, MAE and SMAPE and higher out-of-sample R². In city-level analyses, RF ranked highest in 11 of 14 cities based on the descriptive summary score, whereas GRU ranked highest in Yueyang, Zhangjiajie and Hengyang, indicating that model suitability may vary across local epidemic settings.</p> Conclusions <p>We developed a city-level multi-source framework for short-term influenza forecasting in Hunan Province by integrating surveillance, Baidu Search Index, meteorological and air-quality data. Multi-source predictors improved forecasting beyond a seasonal naive baseline, with RF providing a robust option for reducing absolute weekly errors. City-level performance differences highlight the need for local validation before operational deployment. With prospective validation, this framework could support 1–2-week-ahead early warning and targeted influenza preparedness.</p>

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Multi-source machine learning for short-term influenza forecasting in Hunan Province, China: a spatiotemporal analysis using surveillance, search, meteorological and air-quality data

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月8日

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  • Qianlai Sun,
  • Jiangshaya Bahati,
  • Hongfeng Zhao,
  • Zixin Hu,
  • Yanhua Su,
  • Xiaolei Wang,
  • Zhihong Deng,
  • Zhifei Zhan,
  • Jia Rui,
  • Zeyu Zhao,
  • Tianmu Chen,
  • Kaiwei Luo

摘要

Background

Accurate short-term influenza forecasting is important for early warning and public health preparedness, but routine sentinel surveillance may be delayed and may not fully capture behavioural and environmental signals related to influenza activity. This study aimed to develop and evaluate multi-source machine-learning models for short-term influenza forecasting in Hunan Province, China.

Methods

Weekly influenza-like illness plus (ILI+) data from 54 sentinel hospitals across 14 prefecture-level cities in Hunan Province were used to characterise influenza activity from 2015 to 2024. For forecasting during 2020–2024, ILI+ data were integrated with Baidu Search Index indicators, meteorological variables and air quality index data. ILI + was calculated as the product of the weekly ILI proportion and weekly influenza virus test positivity rate. Five models, including LSTM, GRU, stacked autoencoder, random forest and LightGBM, were compared for 1–3-week-ahead forecasting under four feature settings. Performance was evaluated using RMSE, MAE, MAPE, SMAPE and R², with SeasonalNaive52 as the baseline comparator.

Results

Influenza activity showed pronounced winter–spring seasonality with substantial interannual and city-level heterogeneity. At the provincial level, the all-features RF model achieved the lowest absolute errors among the machine-learning models (RMSE = 0.00882, MAE = 0.00389) and an out-of-sample R² of 0.723. Compared with the SeasonalNaive52 baseline, the machine-learning models showed lower RMSE, MAE and SMAPE and higher out-of-sample R². In city-level analyses, RF ranked highest in 11 of 14 cities based on the descriptive summary score, whereas GRU ranked highest in Yueyang, Zhangjiajie and Hengyang, indicating that model suitability may vary across local epidemic settings.

Conclusions

We developed a city-level multi-source framework for short-term influenza forecasting in Hunan Province by integrating surveillance, Baidu Search Index, meteorological and air-quality data. Multi-source predictors improved forecasting beyond a seasonal naive baseline, with RF providing a robust option for reducing absolute weekly errors. City-level performance differences highlight the need for local validation before operational deployment. With prospective validation, this framework could support 1–2-week-ahead early warning and targeted influenza preparedness.