A Mechanism-Disentangled Two-Stage Forecasting Framework with Multi-Source Signal Fusion for Respiratory Hospitalizations

Accurate forecasting of respiratory virus-associated hospitalization rates per 100,000 population is essential for healthcare capacity planning, yet remains challenging during the COVID-19 era due to abrupt distribution shifts and symptom overlap among influenza-like illnesses caused by multiple pathogens. We propose a two-stage deep learning framework that disentangles stable pre-pandemic seasonal dynamics from COVID-19-induced excess hospitalizations. A lightweight GRU is first trained on pre-pandemic surveillance data to model baseline influenza/RSV-driven seasonality, after which an excess model learns from the residual series and integrates multiple online search trends (flu, COVID-19, and fever) using a standard multi-head self-attention mechanism. While we use COVID-19-era data as a case study, the proposed baseline–excess decomposition is not disease-specific and is intended to generalize to future large-scale respiratory outbreaks or pandemics that induce abrupt regime shifts. Experiments on U.S. weekly respiratory hospitalization rate data curated from CDC surveillance networks (AME) show that the proposed approach achieves strong accuracy on a chronological COVID-era split (2020–2025), reaching R2=0.907 with MAPE = 19.22%. Beyond point forecasts, we further evaluate an expanding-window rolling-origin protocol and report calibrated prediction intervals via split conformal prediction, supporting deployment-oriented uncertainty quantification. By decoupling baseline and excess components and fusing behavioral trend signals in a disciplined manner, this framework improves predictive performance under regime shift while providing interpretable excess estimates for timely situational awareness and healthcare resource planning.