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Is the impact of childhood influenza vaccination less than expected: a transmission modelling study

Overview of attention for article published in BMC Infectious Diseases, April 2017
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (89th percentile)
  • High Attention Score compared to outputs of the same age and source (90th percentile)

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2 news outlets
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1 policy source
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1 X user

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22 Dimensions

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Title
Is the impact of childhood influenza vaccination less than expected: a transmission modelling study
Published in
BMC Infectious Diseases, April 2017
DOI 10.1186/s12879-017-2344-6
Pubmed ID
Authors

Felix Weidemann, Cornelius Remschmidt, Silke Buda, Udo Buchholz, Bernhard Ultsch, Ole Wichmann

Abstract

To reduce the burden of severe influenza, most industrialized countries target specific risk-groups with influenza vaccines, e.g. the elderly or individuals with comorbidities. Since children are the main spreaders, some countries have recently implemented childhood vaccination programs to reduce overall virus transmission and thereby influenza disease in the whole population. The introduction of childhood vaccination programs was often supported by modelling studies that predicted substantial incidence reductions. We developed a mathematical transmission model to examine the potential impact of childhood influenza vaccination in Germany, while also challenging established modelling assumptions. We developed an age-stratified SEIR-type transmission model to reproduce the epidemic influenza seasons between 2003/04 and 2013/14. The model was built upon German population counts, contact patterns, and vaccination history and was fitted to seasonal data on influenza-attributable medically attended acute respiratory infections (I-MAARI) and strain distribution using Bayesian methods. As novelties we (i) implemented a stratified model structure enabling seasonal variability and (ii) deviated from the commonly assumed mass-action-principle by employing a phenomenological transmission rate. According to the model, by vaccinating primarily the elderly over ten seasons 4 million (95% prediction interval: 3.84 - 4.19) I-MAARI were prevented which corresponds to an 8.6% (8.3% - 8.9%) reduction compared to a no-vaccination scenario and a number-needed-to-vaccinate (NNV) to prevent one I-MAARI of 37.1 (35.5 - 38.7). Additional vaccination of 2-10 year-old children at 40% coverage would have led to an overall I-MAARI reduction of 17.8% (17.1 - 18.7%) mostly due to indirect effects with a NNV of 20.7 (19.6 - 21.6). When employing the traditional mass-action-principle, the model predicted a more than 3-fold higher I-MAARI reduction (55.6%) due to childhood vaccination. In Germany, the introduction of routine childhood influenza vaccination could considerably reduce I-MAARI among all age-groups and improve the NNV. However, the predicted impact is much lower compared to previous studies, which is primarily caused by our phenomenological approach to modelling influenza virus transmission.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 55 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 55 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 10 18%
Researcher 9 16%
Student > Bachelor 8 15%
Student > Ph. D. Student 8 15%
Other 3 5%
Other 4 7%
Unknown 13 24%
Readers by discipline Count As %
Medicine and Dentistry 12 22%
Nursing and Health Professions 6 11%
Immunology and Microbiology 5 9%
Agricultural and Biological Sciences 4 7%
Biochemistry, Genetics and Molecular Biology 2 4%
Other 6 11%
Unknown 20 36%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 21. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 22 October 2020.
All research outputs
#1,642,713
of 23,978,283 outputs
Outputs from BMC Infectious Diseases
#416
of 7,985 outputs
Outputs of similar age
#33,147
of 312,778 outputs
Outputs of similar age from BMC Infectious Diseases
#18
of 171 outputs
Altmetric has tracked 23,978,283 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,985 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.5. This one has done particularly well, scoring higher than 94% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 312,778 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 89% of its contemporaries.
We're also able to compare this research output to 171 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 90% of its contemporaries.