Multi-modal Adaptive Estimation for Temporal Respiratory Disease Outbreak

• URL: http://arxiv.org/abs/2509.08578v3
• Date: Fri, 19 Sep 2025 17:05:44 GMT
• Title: Multi-modal Adaptive Estimation for Temporal Respiratory Disease Outbreak
• Authors: Hong Liu, Kerui Cen, Yanxing Chen, Zige Liu, Dong Chen, Zifeng Yang, Chitin Hon,
• Abstract summary: MAESTRO is a novel, unified framework that integrates spectro-temporal modeling with multi-modal data fusion.<n> Evaluated on over 11 years of Hong Kong data, MAESTRO achieves a superior model fit with an R-square of 0.956.<n>The modular and reproducible pipeline is made publicly available to facilitate deployment and extension to other regions and pathogens.
• Score: 8.861941883057098
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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