J. T. Davis, G. Kaur, A. Hines, M. Ben-Nun, S. Venkatramanan, L. Brooks, S. Mathis, M. Ajelli, M. Litvinova, A. G. Kummer, P. C. Ventura, S. Mhade, D. Weber, D. Shemetov, N. DeFries, D. J. McDonald, T. Yamana, R. Zepeda-Tello, J. Shaman, R. Yaari, S. Pei, A. Webber, L. Shandross, E. Ray, S. Wadsworth, J. Niemi, W. T. Redman, L. Mullany, R. Posner, A. Mallela, Y. T. Lin, W. S. Hlavacek, A. Smart, A. A. Gill, A. Drennan, B. J. Fiebiger, E. F. Miller, J. Lee, J. R. Mihaljevic, K. A. Geist, M. Baltz, O. Bernik, Y.-M. B. Truong, Y. Chen, C. J. Grosvenor
Forecasting influenza hospitalizations informs public health preparedness, yet questions remain about which types of forecasts best guide action. We evaluate categorical trend forecasts, which communicate probabilities of upcoming increases or decreases in epidemic trajectories, submitted to CDC's FluSight Forecasting Challenge between Fall-2024 and Spring-2026. Teams submitted probability distributions over five categories describing direction and magnitude of week-over-week changes in laboratory-confirmed influenza hospital admissions. We assessed performance using Ranked Probability Skill Score, Brier Skill Score, and measures of forecast-observation agreement. Most models outperformed an equal-probability baseline; the FluSight ensemble ranked among the top three in the 2024-25 and 2025-26 seasons. Forecasts were most accurate during stable periods and least during periods of rapid change, with most models underestimating observed trends. Conclusions were robust to choice of scoring metric and reference model. These results support categorical trend ensembles as an approach to communicating infectious disease forecasts that may inform public health decision-making.