Background: Twelve students experienced symptoms of acute respiratory infection (ARI) at a training base in Beijing from August 26 to August 30, 2015. We investigated the cause of this ARI outbreak. Methods: In partnership with the local center for disease control, we collected a total of twelve pharyngeal swab specimens as well as demographic information for the affected patients. We used multiplex real-time PCR to screen for sixteen common respiratory viruses in these samples. To isolate HAdV, we inoculated Hep-2 cells with the human adenovirus (HAdV)-positive samples and then carried out sequencing and phylogenetic analysis of the hexon, fiber, and penton genes of the isolated adenoviruses. In addition, we analyzed the entire genome of one strain isolated from the index case to identify single-nucleotide substitutions. Results: We identified ten HAdV-positive students using multiplex real-time PCR. None of the students were co-infected with other viruses. We successfully isolated seven HAdV strains from the pharyngeal swab specimens. The coding sequences of the hexon, fiber, and penton genes of these seven HAdV strains were identical, suggesting that they represented seven strains from a single virus clone. One HAdV isolate obtained from the index case, BJDX-01-2015, was selected for whole genome analysis. From this isolate, we obtained a 34,774-nucleotide sequence. The genome of BJDX-01-2015 clustered with HAdV-B55 in phylogenetic analyses and had 99.97% identity with human adenovirus 55 isolate HAdV-B/CHN/BJ01/2011/55 (GenBank accession no. JX491639). Conclusions: We identified HAdV-B55 as the strain associated with the August 2015 ARI outbreak at a training base in Beijing. This was the first reported outbreak in Beijing due to HAdV-B55. Continuous surveillance of respiratory adenoviruses is urgently needed to understand the epidemiological and evolutionary features of HAdV-B55, and an epidemiological modeling approach may provide further insights into this emerging public health threat. Furthermore, the clinical laboratory data from this outbreak provides important reference for the clinical diagnosis and may ultimately aid in informing the development of strategies to control and prevent respiratory tract infections caused by HAdV-B55.

Although schools are known to play a major role in the spread of influenza virus, few studies have evaluated the effectiveness of vaccination and non-pharmaceutical measures for preventing influenza outbreaks in schools. We investigated all febrile illness outbreaks in primary and secondary schools in Beijing reported between August 2018 and July 2019. We obtained epidemiological information on febrile illness outbreaks and oral pharyngeal swabs from students in the outbreaks to test for influenza virus. We surveyed schools that did not report febrile illness outbreaks. We developed multi-level models to identify and evaluate factors associated with serious influenza outbreaks and explored the association of vaccine coverage and outbreaks using multi-stage regression models. We identified a total of 748 febrile illness outbreaks involving 8176 students in Beijing; 462 outbreaks were caused by influenza virus. Adjusted regression modeling showed that large class size (odds ratio (OR) = 2.38) and the number of days from identification of the first case to initiation of an intervention (OR = 1.17) were statistically significant and positively associated with serious outbreaks, and that high vaccination coverage (relative risk (RR) = 0.50) was statistically significant and negatively associated with outbreaks. Multi-stage regression modeling showed that RR decreased fastest when vaccination coverage was 45% to 51%. We conclude that high influenza vaccination coverage can prevent influenza outbreaks in schools and that rapid identification of febrile children and early initiation of non-pharmaceutical measures can reduce outbreak size.

Background: During the 2022–2023 influenza season, the influenza activities in most regions of China were postponed, including Beijing. The unusually delayed influenza epidemic posed a challenge to the effectiveness of the influenza vaccine. Methods: Using the test-negative design, we evaluated influenza vaccine effectiveness (VE) during the 2022–2023 influenza season against influenza A-associated outpatient and emergency-department-attended influenza-like illness (ILI) in Beijing, China, from 9 January to 30 April 2023. Results: The analysis included 8301 medically attended ILI patients, of which 1342 (46.2%) had influenza A(H1N1)pdm09, 1554 (53.4%) had influenza A(H3N2), and 11 (0.4%) had co-infection of the two viruses. VE against influenza A-associated ILI patients was 23.2% (95% CI: −6.5% to 44.6%) overall, and 23.1%, 9.9%, and 33.8% among children aged 6 months to 17 years, adults aged 18–59 years, and adults aged ≥60 years, respectively. VE against influenza A(H1N1)pdm09 and against influenza A(H3N2) were 36.2% (95% CI: −1.9% to 60.1%) and 9.5% (95% CI: −34.1% to 39.0%), respectively. VE of the group with vaccination intervals of 14–90 days (70.1%, 95% CI: −145.4 to 96.4) was higher than that of the groups with a vaccination interval of 90–149 days (18.7%, 95% CI: −42.4% to 53.6%) and ≥150 days (21.2%, 95% CI: −18.8% to 47.7%). Conclusions: A moderate VE against influenza A(H1N1)pdm09 and a low VE against influenza A(H3N2) were observed in Beijing during the 2022–2023 influenza season, a season characterized with a delayed and high-intensity influenza epidemic. VE appears to be better within three months after vaccination. Our findings indicate a potential need for the optimization of vaccination policies and underscore the importance of continuous monitoring of influenza to enhance vaccines and optimizing vaccination timing.