Integrating Nowcasts into an Ensemble of Data-Driven Forecasting Models for SARI Hospitalizations in Germany

Predictive epidemic modeling can enhance situational awareness during emerging and seasonal outbreaks and has received increasing interest in recent years. A common distinction is between nowcasting, which corrects recent incidence data for reporting delays, and forecasting, which predicts future trends. This paper presents an integrated system for nowcasting and multi-model short-term forecasting of hospitalizations from severe acute respiratory infections (SARI) in Germany (November 2023-September 2024). We propose a modular approach combining a statistical nowcasting model with various data-driven forecasting methods, including a time series model, a gradient boosting approach, and a neural network. These are combined into an ensemble approach, which achieves the best average performance. The resulting forecasts are overall well-calibrated up to four weeks ahead, but struggled to capture the unusual double peak which occurred during the test season. While the presented analysis is retrospective, it serves as a blueprint for a collaborative real-time forecasting platform for respiratory diseases in Germany (the RESPINOW Hub). We conclude with an outlook on this system, which was launched in the fall of 2024 and covers a broader range of data sources and modeling approaches.