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Improved correlation of human Q fever incidence to modelled C. burnetii concentrations by means of an atmospheric dispersion model

Overview of attention for article published in International Journal of Health Geographics, April 2015
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (67th percentile)

Mentioned by

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1 policy source
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1 Facebook page

Citations

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25 Dimensions

Readers on

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41 Mendeley
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Title
Improved correlation of human Q fever incidence to modelled C. burnetii concentrations by means of an atmospheric dispersion model
Published in
International Journal of Health Geographics, April 2015
DOI 10.1186/s12942-015-0003-y
Pubmed ID
Authors

Jeroen PG van Leuken, Jan van de Kassteele, Ferd J Sauter, Wim van der Hoek, Dick Heederik, Arie H Havelaar, Arno N Swart

Abstract

Atmospheric dispersion models (ADMs) may help to assess human exposure to airborne pathogens. However, there is as yet limited quantified evidence that modelled concentrations are indeed associated to observed human incidence. We correlated human Q fever (caused by the bacterium Coxiella burnetii) incidence data in the Netherlands to modelled concentrations from three spatial exposure models: 1) a NULL model with a uniform concentration distribution, 2) a DISTANCE model with concentrations proportional to the distance between the source and residential addresses of patients, and 3) concentrations modelled by an ADM using three simple emission profiles. We used a generalized linear model to correlate the observed incidences to modelled concentrations and validated it using cross-validation. ADM concentrations generally correlated the best to the incidence data. The DISTANCE model always performed significantly better than the NULL model. ADM concentrations based on wind speeds exceeding threshold values of 0 and 2 m/s performed better than those based on 4 or 6 m/s. This might indicate additional exposure to bacteria originating from a contaminated environment. By adding meteorological information the correlation between modelled concentration and observed incidence improved, despite using three simple emission profiles. Although additional information is needed - especially regarding emission data - these results provide a basis for the use of ADMs to predict and to visualize the spread of airborne pathogens during livestock, industry and even bio-terroristic related outbreaks or releases to a surrounding human population.

Mendeley readers

The data shown below were compiled from readership statistics for 41 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 2%
Unknown 40 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 22%
Researcher 8 20%
Student > Bachelor 6 15%
Student > Master 6 15%
Student > Postgraduate 2 5%
Other 5 12%
Unknown 5 12%
Readers by discipline Count As %
Medicine and Dentistry 7 17%
Veterinary Science and Veterinary Medicine 6 15%
Environmental Science 6 15%
Agricultural and Biological Sciences 6 15%
Engineering 4 10%
Other 7 17%
Unknown 5 12%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 30 October 2017.
All research outputs
#5,416,453
of 17,349,416 outputs
Outputs from International Journal of Health Geographics
#228
of 583 outputs
Outputs of similar age
#74,143
of 236,491 outputs
Outputs of similar age from International Journal of Health Geographics
#1
of 1 outputs
Altmetric has tracked 17,349,416 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 583 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.1. This one has gotten more attention than average, scoring higher than 59% 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 236,491 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 67% 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