Eurosurveillance - Volume 30, Issue 42, 23 October 2025

Group A Streptococcus (GAS) commonly causes mild bacterial infections but also deadly invasive disease. An upsurge in paediatric invasive GAS (iGAS) infections was observed during the last quarter of 2022 in the European Union/European Economic Area (EU/EEA) countries.

We aimed to assess iGAS surveillance in the EU/EEA countries and investigate the epidemiology of iGAS infections during the 2022/23 season.

We conducted a study on GAS and iGAS surveillance to evaluate coverage and surveillance methodology across the EU/EEA countries. We collected and analysed data on paediatric iGAS cases (patients aged ≤ 16 years) occurring in September 2022–June 2023 that resulted in hospitalisation or death. Associations of severe outcome (admission to intensive care unit and/or death) with potential risk factors were estimated by logistic regression in a case-case analysis.

Nineteen countries responded to the questionnaire; eleven had mandated national surveillance for iGAS before 2022. Eight countries submitted data on 1,277 paediatric iGAS cases involving hospitalisation or death: 56% were males and median age was 4 years. Sixty-three (5%) of these cases died. Severe outcome was associated with emm1 type (odds ratio (OR) = 1.73; 95% confidence interval (CI): 1.13–2.67), having a sepsis without a known anatomic source (OR = 1.73; 95% CI: 1.11–2.73) and lower respiratory tract infections (OR = 4.14; 95% CI: 2.70–6.44).

Surveillance of GAS and iGAS infections varied among the participating countries. We highlight the importance of including emm typing and analysis of clinical data in iGAS surveillance and having international collaboration for effective response to future surges.

Mandatory reporting of healthcare-associated infections (HCAI) in England is conducted locally by acute hospital groups and can be a large burden on healthcare staff.

We aimed to determine the case ascertainment of a potential centrally-implemented, automated HCAI surveillance system in England using preexisting data feeds at the UK Health Security Agency.

We compared monthly case numbers submitted between 1 April 2016 and 31 March 2023 by acute hospital groups (locally-implemented surveillance) to routinely-collected laboratory and hospital encounter records (centrally-implemented surveillance) for all infections under mandatory surveillance in England. Since laboratories can serve multiple hospitals, we compared several methods of assigning laboratory-confirmed cases to hospital groups.

Locally-implemented vs centrally-implemented surveillance identified: meticillin-resistant Staphylococcus aureus bacteraemias 5,453 vs 5,859 (ratio 1.07), meticillin-susceptible S. aureus bacteraemias 84,680 vs 83,326 (0.98), Escherichia coli bacteraemias 281,100 vs 275,133 (0.98), Klebsiella species bacteraemias 65,877 vs 67,301 (1.02), Pseudomonas aeruginosa bacteraemias 25,862 vs 25,715 (0.99), Clostridioides difficile infections (CDI) 94,054 v 90,942 (0. 97) respectively. Assigning hospital groups by linking laboratory records to hospital encounters produced lower monthly mean absolute difference (MAD) vs locally-implemented surveillance than using laboratory records alone. MAD was 0.65 cases/month for bacteraemias, 2.99 for CDI; differences occurred in both directions. MAD decreased over time for bacteraemias but increased from April 2021 onwards for CDI.

Centrally-implemented surveillance could be feasible for bacteraemias in England due to comparable case numbers with local surveillance. However, more research is needed around understanding and managing data quality of automated feeds, particularly for CDI.

Advanced outbreak analytics were instrumental in informing governmental decision-making during the COVID-19 pandemic. However, systematic evaluations of how modelling practices, data use and science–policy interactions evolved during this and previous emergencies remain scarce.

This study assessed the evolution of modelling practices, data usage, gaps, and engagement between modellers and decision-makers to inform future global epidemic intelligence.

We conducted a two-stage semiquantitative survey among modellers in a large European epidemic intelligence consortium. Responses were analysed descriptively across early, mid- and late-pandemic phases. We used policy citations in Overton to assess policy impact.

Our sample included 66 modelling contributions from 11 institutions in four European countries. COVID-19 modelling initially prioritised understanding epidemic dynamics; evaluating non-pharmaceutical interventions and vaccination impacts later became equally important. Traditional surveillance data (e.g. case line lists) were widely available in near-real time. Conversely, real-time non-traditional data (notably social contact and behavioural surveys) and serological data were frequently reported as lacking. Gaps included poor stratification and incomplete geographical coverage. Frequent bidirectional engagement with decision-makers shaped modelling scope and recommendations. However, fewer than half of the studies shared open-access code.

We highlight the evolving use and needs of modelling during public health crises. Persistent gaps in the availability of non-traditional data underscore the need to rethink sustainable data collection and sharing practices, including from for-profit providers. Future preparedness should focus on strengthening collaborative platforms, research consortia and modelling networks to foster data and code sharing and effective collaboration between academia, decision-makers and data providers.