tetano
Editor, Senior Moderator
Infect Dis Model
. 2026 Apr 6;11(3):1227-1233.
doi: 10.1016/j.idm.2026.04.002. eCollection 2026 Sep.
Retrospective evaluation of trained and untrained probabilistic ensemble forecasts for influenza hospital admissions - United States, 2022-2025
Aaron M Frutos[SUP] 1 2 [/SUP], Annabella Hines[SUP] 1 3 [/SUP], Li Shandross[SUP] 4 [/SUP], Evan L Ray[SUP] 4 [/SUP], Nicholas G Reich[SUP] 4 [/SUP], Rebecca K Borchering[SUP] 1 [/SUP], Matthew Biggerstaff[SUP] 1 [/SUP]
Affiliations
The United States Centers for Disease Control and Prevention (CDC) coordinates influenza forecasting efforts with approximately 30 academic and industry teams and combines their short-term, weekly forecasts into an ensemble forecast to improve accuracy and increase utility. To investigate the accuracy of trained ensemble methods for forecasting confirmed influenza hospital admissions, we retrospectively compared ensembles trained on past forecast performance of submitting teams during the 2022-23, 2023-24, and 2024-25 influenza seasons to the untrained ensemble used during each season. Forecasts are based on laboratory-confirmed influenza hospital admission data from CDC's National Healthcare Safety Network. For each week from October 2022-April 2023, October 2023-April 2024, and November 2024-May 2025, we produced three trained median and three trained mean ensembles that weight individual forecasts based on their weighted interval score (WIS) during the prior 2, 4, or 6 weeks of performance. We evaluated the trained and untrained ensembles using prediction interval coverage and the WIS. Compared to the untrained ensemble, multiple trained ensemble performed better in each season and across jurisdictions, although the best performing ensemble differed. As this is an analysis of only three influenza seasons, we will continue to evaluate ensemble performance over subsequent seasons to see if consistent patterns emerge in the performance of different methods to train ensembles.
Keywords: Ensemble; Forecasting; Influenza.
. 2026 Apr 6;11(3):1227-1233.
doi: 10.1016/j.idm.2026.04.002. eCollection 2026 Sep.
Retrospective evaluation of trained and untrained probabilistic ensemble forecasts for influenza hospital admissions - United States, 2022-2025
Aaron M Frutos[SUP] 1 2 [/SUP], Annabella Hines[SUP] 1 3 [/SUP], Li Shandross[SUP] 4 [/SUP], Evan L Ray[SUP] 4 [/SUP], Nicholas G Reich[SUP] 4 [/SUP], Rebecca K Borchering[SUP] 1 [/SUP], Matthew Biggerstaff[SUP] 1 [/SUP]
Affiliations
- PMID: 42011320
- PMCID: PMC13091824
- DOI: 10.1016/j.idm.2026.04.002
The United States Centers for Disease Control and Prevention (CDC) coordinates influenza forecasting efforts with approximately 30 academic and industry teams and combines their short-term, weekly forecasts into an ensemble forecast to improve accuracy and increase utility. To investigate the accuracy of trained ensemble methods for forecasting confirmed influenza hospital admissions, we retrospectively compared ensembles trained on past forecast performance of submitting teams during the 2022-23, 2023-24, and 2024-25 influenza seasons to the untrained ensemble used during each season. Forecasts are based on laboratory-confirmed influenza hospital admission data from CDC's National Healthcare Safety Network. For each week from October 2022-April 2023, October 2023-April 2024, and November 2024-May 2025, we produced three trained median and three trained mean ensembles that weight individual forecasts based on their weighted interval score (WIS) during the prior 2, 4, or 6 weeks of performance. We evaluated the trained and untrained ensembles using prediction interval coverage and the WIS. Compared to the untrained ensemble, multiple trained ensemble performed better in each season and across jurisdictions, although the best performing ensemble differed. As this is an analysis of only three influenza seasons, we will continue to evaluate ensemble performance over subsequent seasons to see if consistent patterns emerge in the performance of different methods to train ensembles.
Keywords: Ensemble; Forecasting; Influenza.