Trial reportPloS one2014
Positive network assortativity of influenza vaccination at a high school: implications for outbreak risk and herd immunity.
Trial report in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
45 citing papers in PubMed.
- Quantifying Protection Against Influenza Virus Infection Measured by Hemagglutination-inhibition Assays in Vaccine Trials.Epidemiology (Cambridge, Mass.) · 2016Trial
- Impact of Homophily in Adherence to Anti-Epidemic Measures on the Spread of Infectious Diseases in Social Networks.Entropy (Basel, Switzerland) · 2025Article
- Online Media Use and COVID-19 Vaccination in Real-World Personal Networks: Quantitative Study.Journal of medical Internet research · 2024Article
- Evaluating the impact of school-based influenza vaccination programme on absenteeism and outbreaks at schools in Hong Kong: a retrospective cohort study protocol.Journal of health, population, and nutrition · 2024Article
- Assortative mixing of opinions about COVID-19 vaccination in personal networks.Scientific reports · 2024Article
- Network segregation and the propagation of misinformation.Scientific reports · 2023Article
- Article
- Assortative mixing among vaccination groups and biased estimation of reproduction numbers.The Lancet. Infectious diseases · 2022Article
- The Mediating Effect of Religiousness in the Relationship Between Psychological Resilience and Fear of COVID-19 in Turkey.Journal of religion and health · 2022Article
- Inferring the reproduction number using the renewal equation in heterogeneous epidemics.Journal of the Royal Society, Interface · 2022Article
- Using social contact data to improve the overall effect estimate of a cluster-randomized influenza vaccination program in Senegal.Journal of the Royal Statistical Society. Series C, Applied statistics · 2022Article
- Assortativity and Bias in Epidemiologic Studies of Contagious Outcomes: A Simulated Example in the Context of Vaccination.American journal of epidemiology · 2021Article
- Model-driven mitigation measures for reopening schools during the COVID-19 pandemic.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Effect of homophily and correlation of beliefs on COVID-19 and general infectious disease outbreaks.PloS one · 2021Article
- Article
- Clustering of susceptible individuals within households can drive measles outbreaks: an individual-based model exploration.Scientific reports · 2020Article
- Once we have it, will we use it? A European survey on willingness to be vaccinated against COVID-19.The European journal of health economics : HEPAC : health economics in prevention and care · 2020Article
- Influenza vaccine effectiveness estimates against influenza A(H3N2) and A(H1N1) pdm09 among children during school-based outbreaks in the 2016-2017 season in Beijing, China.Human vaccines & immunotherapeutics · 2020Article
- Modelling anti-vaccine sentiment as a cultural pathogen.Evolutionary human sciences · 2020Article
- A Rational Explanation of Limited FMD Vaccine Uptake in Endemic Regions.Pathogens (Basel, Switzerland) · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Schools are known to play a significant role in the spread of influenza. High vaccination coverage can reduce infectious disease spread within schools and the wider community through vaccine-induced immunity in vaccinated individuals and through the indirect effects afforded by herd immunity. In general, herd immunity is greatest when vaccination coverage is highest, but clusters of unvaccinated individuals can reduce herd immunity. Here, we empirically assess the extent of such clustering by measuring whether vaccinated individuals are randomly distributed or demonstrate positive assortativity across a United States high school contact network. Using computational models based on these empirical measurements, we further assess the impact of assortativity on influenza disease dynamics. We found that the contact network was positively assortative with respect to influenza vaccination: unvaccinated individuals tended to be in contact more often with other unvaccinated individuals than with vaccinated individuals, and these effects were most pronounced when we analyzed contact data collected over multiple days. Of note, unvaccinated males contributed substantially more than unvaccinated females towards the measured positive vaccination assortativity. Influenza simulation models using a positively assortative network resulted in larger average outbreak size, and outbreaks were more likely, compared to an otherwise identical network where vaccinated individuals were not clustered. These findings highlight the importance of understanding and addressing heterogeneities in seasonal influenza vaccine uptake for prevention of large, protracted school-based outbreaks of influenza, in addition to continued efforts to increase overall vaccine coverage.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.