Erin L. Murray

Background: During 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. Methods: For 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. Results: Despite varying across seasons and regions, influenza surveillance data sources in California were strongly correlated with laboratory-confirmed influenza hospitalizations (Spearman’s ρ ≥ 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 (Spearman’s ρ ≥ 0.89). Principal component analysis suggests that 93% of the variation in data signals can be explained by a single axis. Conclusions: Influenza 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 Messages: Influenza 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. Although 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. This 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.

Background: Mandatory public health reporting of influenza laboratory results and vaccine doses administered in the state of California can provide estimates of seasonal influenza vaccine effectiveness (VE). Methods: We analyzed linked influenza immunization registry and laboratory reporting data among California residents aged ≥6 months tested for influenza during the 2023–2024 influenza season (October 2023 to June 2024). Individually linked laboratory reporting included influenza molecular or viral culture test result. Odds ratios (OR) and 95% confidence intervals (CI) contrasted odds of documented 2023–2024 vaccination among persons with influenza-positive tests versus persons with negative influenza tests. VE was calculated as (1 − adjusted OR) × 100 using logistic regression, adjusting for patient age, race, ethnicity, week of specimen collection, and county of residence. Results: Among 1 382 142 laboratory reports, 129 253 persons (9%) had a positive influenza test result, of whom 415 390 (30%) had documented influenza vaccination ≥ 14 days before test date. VE against laboratory-confirmed influenza was 41% (95% CI, 40%–42%). VE was 32% (95% CI, 31%–33%) against influenza A, 68% (95% CI, 66%–69%) against influenza B, and 26% (95% CI, 24%–29%) among adults aged ≥ 65 years. Conclusions: Influenza vaccination was associated with prevention of laboratory-confirmed influenza. Results demonstrated the feasibility of assessing seasonal influenza VE using linked immunization and laboratory data from public health surveillance systems.

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 ^nd most accurate model measured by WIS in 2021-22 and the 5 ^th 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.

Background: Information on U.S. COVID-19 mortality rates by occupation is limited. We aimed to characterize 2020 COVID-19 fatalities among working Californians to inform preventive strategies.Methods: We identified laboratory-confirmed COVID-19 fatalities with dates of death in 2020 by matching death certificates to the state’s COVID-19 case registry. Working status for decedents aged 18–64 years was determined from state employment records, death certificates, and case registry data and classified as “confirmed working,” “likely working,” or “not working.” We calculated age-adjusted overall and occupation-specific COVID-19 mortality rates using 2019 American Community Survey denominators.Results: COVID-19 accounted for 8,050 (9.9%) of 81,468 fatalities among Californians 18–64 years old. Of these decedents, 2,486 (30.9%) were matched to state employment records and classified as “confirmed working.” The remainder were classified as “likely working” (n = 4,121 [51.2%]) or “not working” (n = 1,443 [17.9%]) using death certificate and case registry data. Confirmed and likely working COVID-19 decedents were predominantly male (76.3%), Latino (68.7%), and foreign-born (59.6%), with high school or less education (67.9%); 7.8% were Black. The overall age-adjusted COVID-19 mortality rate was 30.0 per 100,000 workers (95% confidence interval [CI], 29.3–30.8). Workers in nine occupational groups had age-adjusted mortality rates higher than this overall rate, including those in farming (78.0; 95% CI, 68.7–88.2); material moving (77.8; 95% CI, 70.2–85.9); construction (62.4; 95% CI, 57.7–67.4); production (60.2; 95% CI, 55.7–65.0); and transportation (57.2; 95% CI, 52.2–62.5) occupations. While occupational differences in mortality were evident across demographic groups, mortality rates were three-fold higher for male compared with female workers and three- to seven-fold higher for Latino and Black workers compared with Asian and White workers.Conclusion: Californians in manual labor and in-person service occupations experienced disproportionate COVID-19 mortality, with the highest rates observed among male, Latino, and Black workers; these occupational group should be prioritized for prevention.