Collaborative forecasting of influenza-like illness in Italy: The Influcast experience - Stefania Fiandrino; Andrea Bizzotto; Giorgio Guzzetta; Stefano Merler; Federico Baldo; Eugenio Valdano; Alberto Mateo Urdiales; Antonino Bella; Francesco Celino; Lorenzo Zino; Alessandro Rizzo; Yuhan Li; et al. (Epidemics)
Collaborative hubs that integrate multiple teams to generate ensemble projections and forecasts for shared targets are now regarded as state-of-the-art in epidemic predictive modeling. In this paper, we introduce Influcast, Italy’s first epidemic forecasting hub for influenza-like illness. During the 2023/2024 winter season, Influcast provided 20 rounds of forecasts, involving five teams and eight models to predict influenza-like illness incidence up to four weeks in advance at the national and regional administrative level. The individual forecasts were synthesized into an ensemble and benchmarked against a baseline model. Across all models, the ensemble most frequently ranks among the top performers at the national level considering different metrics and forecasting rounds. Additionally, the ensemble outperforms the baseline and most individual models across all regions. Despite a decline in absolute performance over longer horizons, the ensemble model outperformed the baseline in all considered horizons. These findings show the importance of multimodel forecasting hubs in producing reliable short-term influenza-like illnesses forecasts that can inform public health preparedness and mitigation strategies.