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Dose-response models for selected respiratory infectious agents: Bordetella pertussis, group a Streptococcus, rhinovirus and respiratory syncytial virus

Overview of attention for article published in BMC Infectious Diseases, February 2015
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (91st percentile)

Mentioned by

news
1 news outlet
policy
1 policy source
twitter
12 tweeters

Citations

dimensions_citation
15 Dimensions

Readers on

mendeley
45 Mendeley
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Title
Dose-response models for selected respiratory infectious agents: Bordetella pertussis, group a Streptococcus, rhinovirus and respiratory syncytial virus
Published in
BMC Infectious Diseases, February 2015
DOI 10.1186/s12879-015-0832-0
Pubmed ID
Authors

Rachael M Jones, Yu-Min Su

Abstract

Dose-response assessment is one step in quantitative microbial risk assessment (QMRA). Four infectious microbes capable of causing respiratory diseases important to public health, and for which dose-response functions have not been available are: Bordetella pertussis (whooping cough), group A Streptococcus (pharyngitis), rhinovirus (common cold) and respiratory syncytial virus (common cold). The objective of this study was to fit dose-response functions for these microbes to published experimental data. Experimental infectivity data in human subjects and/or animal models were identified from the peer-reviewed literature. The exponential and beta-Poisson dose-response functions were fitted using the method of maximum likelihood, and models compared by Akaike's Information Criterion. Dose-response functions were identified for each appropriate data set for the four infectious microbes. Statistical and graphical measures of fit are presented. With the fitted dose-response functions it will be possible to perform QMRA for these microbes. The dose-response functions, however, have a number of limitations associated with the route of exposure, use of animal hosts, and quality of fit. As a result, thoughtfulness must be used in selecting one dose-response function for a QMRA, and the function should be recognized as a significant source of uncertainty. Nonetheless, QMRA offers a transparent, systematic framework within which to understand the mechanisms of disease transmission, and evaluate interventions.

Twitter Demographics

The data shown below were collected from the profiles of 12 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 45 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Australia 1 2%
Unknown 44 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 16%
Researcher 5 11%
Student > Master 5 11%
Other 3 7%
Student > Bachelor 3 7%
Other 9 20%
Unknown 13 29%
Readers by discipline Count As %
Engineering 7 16%
Medicine and Dentistry 6 13%
Agricultural and Biological Sciences 4 9%
Nursing and Health Professions 1 2%
Mathematics 1 2%
Other 6 13%
Unknown 20 44%

Attention Score in Context

This research output has an Altmetric Attention Score of 20. 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 June 2020.
All research outputs
#1,343,959
of 19,463,333 outputs
Outputs from BMC Infectious Diseases
#306
of 6,764 outputs
Outputs of similar age
#19,303
of 226,574 outputs
Outputs of similar age from BMC Infectious Diseases
#1
of 1 outputs
Altmetric has tracked 19,463,333 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 6,764 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.7. This one has done particularly well, scoring higher than 95% 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 226,574 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 91% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them