融合多源数据与时频分析，提升流感疫情预测精度
Multi-modal Adaptive Estimation for Temporal Respiratory Disease Outbreak
- 用自适应加权融合监测、搜索趋势和气象数据
- 在11年香港数据上达R²=0.956，优于现有方法
- 模块化设计适合其他地区和病原体部署
及时且稳健的流感发病率预测对公共卫生决策至关重要。本文提出MAESTRO（多模态自适应时序呼吸系统疾病暴发估计），一种统一框架，协同整合先进的时频建模与多模态数据融合，包括流行病学监测、网络搜索趋势和气象数据。通过自适应加权异构数据源并分解复杂时间序列模式，模型实现稳健精准的预测。在排除新冠时期的11年香港流感数据上评估，MAESTRO表现优异，模型拟合度达R²=0.956。大量消融实验验证了其多模态与时频组件的重要贡献。该模块化、可复现的流程已公开，便于在其他地区和病原体中部署，为流行病预测提供强大工具。
原文摘要 · Abstract (English)
Timely and robust influenza incidence forecasting is critical for public health decision-making. This paper presents MAESTRO (Multi-modal Adaptive Estimation for Temporal Respiratory Disease Outbreak), a novel, unified framework that synergistically integrates advanced spectro-temporal modeling with multi-modal data fusion, including surveillance, web search trends, and meteorological data. By adaptively weighting heterogeneous data sources and decomposing complex time series patterns, the model achieves robust and accurate forecasts. Evaluated on over 11 years of Hong Kong influenza data (excluding the COVID-19 period), MAESTRO demonstrates state-of-the-art performance, achieving a superior model fit with an R-square of 0.956. Extensive ablations confirm the significant contributions of its multi-modal and spectro-temporal components. The modular and reproducible pipeline is made publicly available to facilitate deployment and extension to other regions and pathogens, presenting a powerful tool for epidemiological forecasting.
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