Multi-modal Adaptive Estimation for Temporal Respiratory Disease Outbreak
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.
Cite
@article{arxiv.2509.08578,
title = {Multi-modal Adaptive Estimation for Temporal Respiratory Disease Outbreak},
author = {Hong Liu and Kerui Cen and Yanxing Chen and Zige Liu and Dong Chen and Zifeng Yang and Chitin Hon},
journal= {arXiv preprint arXiv:2509.08578},
year = {2025}
}