» Articles » PMID: 38168429

Evaluation of FluSight Influenza Forecasting in the 2021-22 and 2022-23 Seasons with a New Target Laboratory-confirmed Influenza Hospitalizations

Overview
Journal medRxiv
Date 2024 Jan 3
PMID 38168429
Authors
Affiliations
Soon will be listed here.
Abstract

Accurate forecasts can enable more effective public health responses during seasonal influenza epidemics. Forecasting teams were asked to provide national and jurisdiction-specific probabilistic predictions of weekly confirmed influenza hospital admissions for one through four weeks ahead for the 2021-22 and 2022-23 influenza seasons. Across both seasons, 26 teams submitted forecasts, with the submitting teams varying between seasons. Forecast skill was evaluated using the Weighted Interval Score (WIS), relative WIS, and coverage. Six out of 23 models outperformed the baseline model across forecast weeks and locations in 2021-22 and 12 out of 18 models in 2022-23. Averaging across all forecast targets, the FluSight ensemble was the 2 most accurate model measured by WIS in 2021-22 and the 5 most accurate in the 2022-23 season. Forecast skill and 95% coverage for the FluSight ensemble and most component models degraded over longer forecast horizons and during periods of rapid change. Current influenza forecasting efforts help inform situational awareness, but research is needed to address limitations, including decreased performance during periods of changing epidemic dynamics.

References
1.
Merced-Morales A, Daly P, Abd Elal A, Ajayi N, Annan E, Budd A . Influenza Activity and Composition of the 2022-23 Influenza Vaccine - United States, 2021-22 Season. MMWR Morb Mortal Wkly Rep. 2022; 71(29):913-919. PMC: 9310632. DOI: 10.15585/mmwr.mm7129a1. View

2.
Lutz C, Huynh M, Schroeder M, Anyatonwu S, Dahlgren F, Danyluk G . Applying infectious disease forecasting to public health: a path forward using influenza forecasting examples. BMC Public Health. 2019; 19(1):1659. PMC: 6902553. DOI: 10.1186/s12889-019-7966-8. View

3.
McGowan C, Biggerstaff M, Johansson M, Apfeldorf K, Ben-Nun M, Brooks L . Collaborative efforts to forecast seasonal influenza in the United States, 2015-2016. Sci Rep. 2019; 9(1):683. PMC: 6346105. DOI: 10.1038/s41598-018-36361-9. View

4.
Reich N, McGowan C, Yamana T, Tushar A, Ray E, Osthus D . Accuracy of real-time multi-model ensemble forecasts for seasonal influenza in the U.S. PLoS Comput Biol. 2019; 15(11):e1007486. PMC: 6897420. DOI: 10.1371/journal.pcbi.1007486. View

5.
Reich N, Brooks L, Fox S, Kandula S, McGowan C, Moore E . A collaborative multiyear, multimodel assessment of seasonal influenza forecasting in the United States. Proc Natl Acad Sci U S A. 2019; 116(8):3146-3154. PMC: 6386665. DOI: 10.1073/pnas.1812594116. View