Title: Multi-modal Adaptive Estimation for Temporal Respiratory Disease Outbreak Show Chinese title

Title: 多模态自适应估计用于时间呼吸系统疾病爆发

Abstract: 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. Show Chinese abstract

Abstract: 及时且稳健的流感发病率预测对于公共卫生决策至关重要。 本文介绍了MAESTRO（多模态自适应时间呼吸系统疾病爆发估计），这是一种新颖的统一框架，通过将先进的时频建模与多模态数据融合相结合，包括监测数据、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的时间序列模式，该模型实现了稳健且准确的预测。 在超过11年的香港流感数据（不包括新冠疫情时期）上进行评估，MAESTRO表现出最先进的性能，达到了R平方值为0.956的优越模型拟合。 广泛的消融实验确认了其多模态和时频组件的重要贡献。 模块化和可重复的流程已公开提供，以促进部署和扩展到其他地区和病原体，为流行病学预测提供了一个强大的工具。