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.

This study aimed to estimate the end-of-season influenza vaccine effectiveness (VE) for the 2024/25 season in Beijing, China. Methods: We used a test-negative design (TND) to assess influenza VE among outpatients with influenza-like illness (ILI) enrolled through the influenza virological surveillance in sentinel hospitals in Beijing from week 44, 2024 to week 14, 2025. Cases were ILI patients who tested positive for influenza; controls were those who tested negative. Results: Among 18,405 ILI patients tested, 3690 (20.0%) were positive for influenza, with A(H1N1)pdm09 as the predominant strain (98.9%). The overall influenza vaccination coverage was 12.4%. Adjusted VE was 48.3% (95%CI: 40.4%–55.3%) against any influenza and 48.2% (95%CI: 40.3%–55.1%) against A(H1N1)pdm09, with the highest VE observed in adults aged 18–59 years (79.0%). The adjusted VE was similar for those vaccinated in 2023/24 only (53.1%) or both 2023/24 and 2024/25 seasons (50.8%), but lower for those vaccinated only in the 2024/25 season (48.5%). The adjusted VE was higher during the epidemic period (52.5%) than in the pre-epidemic (48.1%) and post-epidemic (35.3%) periods. Conclusions: Our findings indicate moderate VE against laboratory-confirmed influenza, especially A(H1N1)pdm09, during the end of the 2024/25 season in Beijing, China. Influenza vaccination provided protective effects across different epidemic periods. These timely estimates support ongoing public health communication and immunization strategies.

The present study aimed to evaluate the vaccine effectiveness (VE) of different doses of an inactivated coronavirus disease 2019 (COVID-19) vaccine against Omicron BA.2.2 infection in Beijing, China, 2022. Based on data from a previous cohabitation retrospective cohort of COVID-19 outbreak in Beijing, China, 2022, the cohabitating contacts of individuals with BA.2.2 infection were followed up. Using a log-binomial regression model in which the unvaccinated group as the control group, the risk ratios of different doses of inactivated vaccine in terms of preventing SARS-CoV-2 infection, symptoms of COVID-19, and pneumonia were calculated, and the protective effect of the vaccine was estimated. The Kruskal–Wallis rank-sum test was used to compare the effect of vaccination on the viral load of infected patients. From April to June 2022, a total of 2259 cohabiting close contacts of 1308 patients with SARS-CoV-2 infection aged ≥3 years were included. Of the included close contacts, 737 (32.63%) were positive for SARS-CoV-2 during the isolation period: 140 (19.00%) were infected but asymptomatic, 525 (71.23%) had mild infection, and 72 (9.77%) had pneumonia. There were no cases of severe or critical infection or death. The VE of the primary in preventing BA.2.2 infection, symptoms, and pneumonia was 37.35% (95% CI: 24.00–48.35), 42.36% (95% CI: 28.41–53.60), and 48.35% (95% CI: −5.34–74.67), respectively. The VE of the booster shot in preventing SARS-CoV-2 infection, symptoms, and pneumonia was 37.08% (95% CI: 24.29–47.70), 44.38% (95% CI: 31.45–54.87), and 61.46% (95% CI: 29.79–78.85), respectively. Six months after the booster vaccination, the VE of the booster in terms of preventing SARS-CoV-2 remained above 46%, and its VE in terms of the prevention of pneumonia remained above 72%. In the unvaccinated group, the Ct values of the N gene and ORFlab gene (represented by the median value and Q1 and Q3 in parentheses) were 26.45 (21.09, 31.61) and 28.06 (22.21, 32.06), respectively. There was no significant difference in the median value of either gene between the unvaccinated group, the partial group [25.81 (19.91, 31.78) and 26.98 (21.63, 31.17)], the primary group [28.79 (22.08, 32.34) and 29.30 (23.81, 33.86)], and the booster group [26.23 (21.66, 31.46) and 27.73 (23.38, 32.52)] (p > 0.05). Inactivated COVID-19 vaccines provided a certain level protection from infection and symptoms, very good protection against pneumonia, and it still has a modest protective effect at 6 months after vaccination. Booster doses are necessary to provide strongest protection. However, irrespective of their vaccination status, individuals with COVID-19 have a similar viral load.

Background/Objectives: Understanding the factors influencing breakthrough infections following COVID-19 vaccination is critical for disease prevention, especially in households where transmission risks are high. Factors such as age, symptoms, living conditions, and viral load contribute to household transmission dynamics. Methods: To elucidate this complex interplay of these factors, we analyzed a detailed household transmission study of COVID-19 involving 839 households and 1598 vaccinated individuals during the Omicron variant outbreak in Beijing, China, from April to June 2022. Using multivariate logistic regression models, we analyzed the impact of demographic, environmental, clinical, and virological factors on the risk of breakthrough infections. Results: In multivariate analysis. we estimated that index cases aged 45–59 and 60+ years were associated with 80% (95% confidence interval [CI]: 35%, 140%) and 288% (95% CI: 160%, 481%) higher infectivity compared with index cases aged 18–44 years. We estimated that index cases with fever, headache and cough were associated with 43% (95% CI: 11%, 84%), 78% (95% CI: 18%, 168%) and 67% (25%, 123%) higher infectivity compared with those without. Index cases with higher viral loads were associated with higher infectivity in univariate analysis, but this was no longer significant in multivariate analysis. Smaller living space and two-member households were associated with higher odds of breakthrough infections. Conclusions: Age, symptoms, and living conditions were significant risk factors for breakthrough infections during the Omicron outbreak. Suburban settings, smaller spaces, and two-member households enhance transmission risks. These findings inform targeted interventions to reduce household transmission.