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. The ensemble forecasts consistently outperformed both individual models and baseline forecasts, demonstrating superior accuracy at national and sub-national levels across various metrics. Despite a decline in absolute performance over longer horizons, the ensemble model outperformed the baseline in all considered time frames. These findings underscore the importance of multimodel forecasting hubs in producing consistent short-term influenza-like illnesses forecasts that can inform public health preparedness and mitigation strategies.

The authors have declared no competing interest.

S.F. acknowledges support from EU grant 101045989 VERDI. E.V. acknowledges support from EU Horizon Europe grant SIESTA (HORIZON-INFRA-2023-EOSC-01-101131957). E.V. and F.B. acknowledge the CINECA award under the ISCRA initiative, for the availability of high-performance computing resources and support.

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