Between October 2022 and January 2023, influenza A(H1N1)pdm09, A(H3N2) and B/Victoria viruses circulated in Europe with different influenza (sub)types dominating in different areas.

To provide interim 2022/23 influenza vaccine effectiveness (VE) estimates from six European studies, covering 16 countries in primary care, emergency care and hospital inpatient settings.

All studies used the test-negative design, but with differences in other study characteristics, such as data sources, patient selection, case definitions and included age groups. Overall and influenza (sub)type-specific VE was estimated for each study using logistic regression adjusted for potential confounders.

There were 20,477 influenza cases recruited across the six studies, of which 16,589 (81%) were influenza A. Among all ages and settings, VE against influenza A ranged from 27 to 44%. Against A(H1N1)pdm09 (all ages and settings), VE point estimates ranged from 28% to 46%, higher among children (< 18 years) at 49–77%. Against A(H3N2), overall VE ranged from 2% to 44%, also higher among children (62–70%). Against influenza B/Victoria, overall and age-specific VE were ≥ 50% (87–95% among children < 18 years).

Interim results from six European studies during the 2022/23 influenza season indicate a ≥ 27% and ≥ 50% reduction in disease occurrence among all-age influenza vaccine recipients for influenza A and B, respectively, with higher reductions among children. Genetic virus characterisation results and end-of-season VE estimates will contribute to greater understanding of differences in influenza (sub)type-specific results across studies.

Since 1996, epidemiological surveillance of acute respiratory infections (ARI) in Spain has been limited to seasonal influenza, respiratory syncytial virus (RSV) and potential pandemic viruses. The COVID-19 pandemic provides opportunities to adapt existing systems for extended surveillance to capture a broader range of ARI.

To describe how the Influenza Sentinel Surveillance System of Castilla y León, Spain was rapidly adapted in 2020 to comprehensive sentinel surveillance for ARI, including influenza and COVID-19.

Using principles and methods of the health sentinel network, we integrated electronic medical record data from 68 basic surveillance units, covering 2.6% of the regional population between January 2020 to May 2022. We tested sentinel and non-sentinel samples sent weekly to the laboratory network for SARS-CoV-2, influenza viruses and other respiratory pathogens. The moving epidemic method (MEM) was used to calculate epidemic thresholds.

ARI incidence was estimated at 18,942 cases per 100,000 in 2020/21 and 45,223 in 2021/22, with similar seasonal fold increases by type of respiratory disease. Incidence of influenza-like illness was negligible in 2020/21 but a 5-week epidemic was detected by MEM in 2021/22. Epidemic thresholds for ARI and COVID-19 were estimated at 459.4 and 191.3 cases per 100,000 population, respectively. More than 5,000 samples were tested against a panel of respiratory viruses in 2021/22.

Extracting data from electronic medical records reported by trained professionals, combined with a standardised microbiological information system, is a feasible and useful method to adapt influenza sentinel reports to comprehensive ARI surveillance in the post-COVID-19 era.

Serological surveys have been the gold standard to estimate numbers of SARS-CoV-2 infections, the dynamics of the epidemic, and disease severity. Serological assays have decaying sensitivity with time that can bias their results, but there is a lack of guidelines to account for this phenomenon for SARS-CoV-2.

Our goal was to assess the sensitivity decay of seroassays for detecting SARS-CoV-2 infections, the dependence of this decay on assay characteristics, and to provide a simple method to correct for this phenomenon.

We performed a systematic review and meta-analysis of SARS-CoV-2 serology studies. We included studies testing previously diagnosed, unvaccinated individuals, and excluded studies of cohorts highly unrepresentative of the general population (e.g. hospitalised patients).

Of the 488 screened studies, 76 studies reporting on 50 different seroassays were included in the analysis. Sensitivity decay depended strongly on the antigen and the analytic technique used by the assay, with average sensitivities ranging between 26% and 98% at 6 months after infection, depending on assay characteristics. We found that a third of the included assays departed considerably from manufacturer specifications after 6 months.

Seroassay sensitivity decay depends on assay characteristics, and for some types of assays, it can make manufacturer specifications highly unreliable. We provide a tool to correct for this phenomenon and to assess the risk of decay for a given assay. Our analysis can guide the design and interpretation of serosurveys for SARS-CoV-2 and other pathogens and quantify systematic biases in the existing serology literature.