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Estimating malaria transmission intensity from Plasmodium falciparum serological data using antibody density models

Overview of attention for article published in Malaria Journal, February 2016
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (58th percentile)

Mentioned by

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5 tweeters
facebook
1 Facebook page

Citations

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30 Dimensions

Readers on

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93 Mendeley
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Title
Estimating malaria transmission intensity from Plasmodium falciparum serological data using antibody density models
Published in
Malaria Journal, February 2016
DOI 10.1186/s12936-016-1121-0
Pubmed ID
Authors

Emilie Pothin, Neil M. Ferguson, Chris J. Drakeley, Azra C. Ghani

Abstract

Serological data are increasingly being used to monitor malaria transmission intensity and have been demonstrated to be particularly useful in areas of low transmission where traditional measures such as EIR and parasite prevalence are limited. The seroconversion rate (SCR) is usually estimated using catalytic models in which the measured antibody levels are used to categorize individuals as seropositive or seronegative. One limitation of this approach is the requirement to impose a fixed cut-off to distinguish seropositive and negative individuals. Furthermore, the continuous variation in antibody levels is ignored thereby potentially reducing the precision of the estimate. An age-specific density model which mimics antibody acquisition and loss was developed to make full use of the information provided by serological measures of antibody levels. This was fitted to blood-stage antibody density data from 12 villages at varying transmission intensity in Northern Tanzania to estimate the exposure rate as an alternative measure of transmission intensity. The results show a high correlation between the exposure rate estimates obtained and the estimated SCR obtained from a catalytic model (r = 0.95) and with two derived measures of EIR (r = 0.74 and r = 0.81). Estimates of exposure rate obtained with the density model were also more precise than those derived from catalytic models. This approach, if validated across different epidemiological settings, could be a useful alternative framework for quantifying transmission intensity, which makes more complete use of serological data.

Twitter Demographics

The data shown below were collected from the profiles of 5 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 93 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 1%
United States 1 1%
Belgium 1 1%
Brazil 1 1%
Unknown 89 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 28 30%
Student > Ph. D. Student 20 22%
Student > Master 18 19%
Student > Bachelor 4 4%
Student > Doctoral Student 3 3%
Other 8 9%
Unknown 12 13%
Readers by discipline Count As %
Medicine and Dentistry 20 22%
Agricultural and Biological Sciences 17 18%
Immunology and Microbiology 8 9%
Biochemistry, Genetics and Molecular Biology 5 5%
Social Sciences 4 4%
Other 23 25%
Unknown 16 17%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 23 September 2016.
All research outputs
#11,032,544
of 20,110,162 outputs
Outputs from Malaria Journal
#2,689
of 5,170 outputs
Outputs of similar age
#152,099
of 368,357 outputs
Outputs of similar age from Malaria Journal
#1
of 1 outputs
Altmetric has tracked 20,110,162 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 5,170 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.3. This one is in the 47th percentile – i.e., 47% of its peers scored the same or lower than it.
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 368,357 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 58% 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