整合多源数据的广义加性模型用于中国深圳短期流感预测（2023-2025）
Generalized additive model integrating multi-source data for short-term influenza forecasting in Shenzhen, China (2023-2025).
摘要(中文译文)
COVID-19大流行重塑了全球呼吸道传染病的流行病学格局，为流感预测带来了新的挑战。现有研究往往受限于依赖单一数据来源、可解释性差或未能考虑变量间的非线性关系，从而限制了其在预测准确性与公共卫生决策实际应用之间取得平衡的能力。本研究旨在开发并验证一种多源数据整合的广义相加模型（GAM），用于预测中国深圳市的流感活动。利用2023年至2025年的监测及辅助数据，我们构建了纳入深圳本地及香港流感监测数据、跨境流动指标、气象因素和百度搜索指数数据的GAM模型。将GAM的预测性能与带外生变量的季节性自回归积分滑动平均模型（SARIMAX）进行比较。采用均方根误差（RMSE）、平均绝对百分比误差（MAPE）和R对模型准确性进行评估。多源数据驱动的GAM在短期预测时间范围内表现出较高的预测准确性。对于提前1周预测，模型R值为0.85（95% CI：0.74-0.92）。值得注意的是，提前2周和3周预测的性能依然稳健，R值分别为0.80（95% CI：0.69-0.87）和0.74（95% CI：0.62-0.83）。与SARIMAX相比，GAM表现出更优的总体性能。多源数据整合的GAM可为深圳市提供提前长达3周的稳健且稳定的流感预测。该方法为支持香港与深圳之间的跨境公共卫生合作提供了有价值的工具，并可能为未来研究中制定更广泛的区域公共卫生策略提供参考。
Abstract
The COVID-19 pandemic has reshaped the global epidemiology of respiratory infectious diseases, posing new challenges for influenza forecasting. Existing studies are often limited by reliance on single data sources, poor interpretability, or failure to account for nonlinear relationships among variables, which restricts their ability to balance prediction accuracy and practical utility for public health decision-making. This study aimed to develop and validate a multisource data-integrated generalized additive model (GAM) to forecast influenza activity in Shenzhen, China. Using surveillance and auxiliary data from 2023 to 2025, we developed GAM models incorporating local and Hong Kong influenza surveillance, cross-boundary mobility metric, meteorological factors, and Baidu Search Index data. The predictive performance of the GAM was compared with Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX) model. Model accuracy was evaluated using root mean square error (RMSE), mean absolute percentage error (MAPE), and R. The multisource data-driven GAM exhibited high predictive accuracy across short-term forecasting horizons. For 1-week ahead forecasts, the model achieved an R of 0.85 (95% CI: 0.74-0.92). Notably, performance remained robust for 2- and 3-week forecasts, with R values of 0.80 (95% CI: 0.69-0.87) and 0.74 (95% CI: 0.62-0.83), respectively. The GAM demonstrated superior overall performance compared with SARIMAX. The multisource data-integrated GAM provides robust and stable influenza forecasts for Shenzhen up to 3 weeks in advance. This approach provides a valuable tool to support cross-boundary public health collaboration between Hong Kong and Shenzhen, and might serve as a reference for the development of broader regional public health strategies in future research.
如何引用
AMA
Xing Li, Qiuying Lv, Jianpeng Xiao, Zhigao Chen, Aiping Deng, Zuhua Rong, et al. Generalized additive model integrating multi-source data for short-term influenza forecasting in Shenzhen, China (2023-2025).. Frontiers in public health. 2026; doi:10.3389/fpubh.2026.1811040.
APA
Xing Li, Qiuying Lv, Jianpeng Xiao, Zhigao Chen, Aiping Deng, Zuhua Rong, et al (2026). Generalized additive model integrating multi-source data for short-term influenza forecasting in Shenzhen, China (2023-2025).. Frontiers in public health. https://doi.org/10.3389/fpubh.2026.1811040
GB/T 7714
Xing Li, Qiuying Lv, Jianpeng Xiao, Zhigao Chen, Aiping Deng, Zuhua Rong, et al. Generalized additive model integrating multi-source data for short-term influenza forecasting in Shenzhen, China (2023-2025).[J]. Frontiers in public health, 2026 doi:10.3389/fpubh.2026.1811040.
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