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A new surveillance landscape for seasonal influenza?: Comparing lab-confirmed influenza hospitalizations with other syndromic and surveillance data sources for the state of California

White, L. A.; Sun, M.; Burnor, E.; Murray, E. L.; Hoover, C.; Penton, C.; Li, N. J.; Chi, F.; Schember, C. O.; Leon, T. M.

2025-10-30 epidemiology
10.1101/2025.10.28.25338991 medRxiv
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BackgroundDuring the COVID-19 pandemic, U.S. federal hospitalization surveillance systems provided a new data source for lab-confirmed influenza hospital admissions; these data were important for monitoring hospital capacity for combined respiratory virus pathogen burden and served as a new forecasting target for seasonal influenza forecasting efforts. MethodsFor influenza surveillance in the state of California, from February 2, 2022 through May 31, 2025, we explored: (1) how lab-confirmed hospital admissions correlated with other syndromic and sentinel surveillance data; (2) which signals could serve as a potential leading input for predicting hospital admissions; and (3) how these relationships varied across respiratory virus seasons and geography. ResultsDespite varying across seasons and regions, influenza surveillance data sources in California were strongly correlated with laboratory-confirmed influenza hospitalizations (Spearmans {rho} [≥] 0.8). These correlations generally strengthened through time from the 2021-2022 season to the most recent 2024-2025 season, especially for death, influenza-like illness, wastewater, and clinical lab data. Most of these data sources neither consistently led nor lagged hospital admissions across all four seasons, but electronic laboratory reporting provided consistent leads of one to two weeks relative to the admissions signal across all four seasons (Spearmans {rho} [≥] 0.89). Principal component analysis suggests that 93% of the variation in data signals can be explained by a single axis. ConclusionsInfluenza surveillance data sources have inherent trade-offs in geographic coverage, temporal resolution, and reporting frequency. Understanding the relationship between different data sources will inform future predictions of influenza burden, including forecasting and scenario modeling. Key MessagesO_LIInfluenza surveillance data sources in California were strongly correlated with new laboratory- confirmed influenza hospitalizations data from the National Hospital Safety Network (NHSN), and these correlations strengthened through time from the 2021-2022 to the most recent 2024- 2025 season. C_LIO_LIAlthough most data sources did not provide a clear leading or lagging signal, electronic laboratory reporting results provided consistent leads of one to two weeks relative to laboratory-confirmed influenza hospital admissions. C_LIO_LIThis analysis demonstrates that existing influenza surveillance and sentinel data sources correspond well with the new NHSN data, which will be important for ongoing surveillance and public health action during future respiratory virus seasons. C_LI

Published in AJE Advances: Research in Epidemiology · not in our set (fewer than 10 published preprints to learn from) · training set

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