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A novel model fitted to multiple life stages of malaria for assessing efficacy of transmission-blocking interventions

Overview of attention for article published in Malaria Journal, April 2017
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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 (85th percentile)
  • High Attention Score compared to outputs of the same age and source (88th percentile)

Mentioned by

blogs
1 blog
twitter
12 X users

Citations

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5 Dimensions

Readers on

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46 Mendeley
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Title
A novel model fitted to multiple life stages of malaria for assessing efficacy of transmission-blocking interventions
Published in
Malaria Journal, April 2017
DOI 10.1186/s12936-017-1782-3
Pubmed ID
Authors

Ellie Sherrard-Smith, Thomas S. Churcher, Leanna M. Upton, Katarzyna A. Sala, Sara E. Zakutansky, Hannah C. Slater, Andrew M. Blagborough, Michael Betancourt

Abstract

Transmission-blocking interventions (TBIs) aim to eliminate malaria by reducing transmission of the parasite between the host and the invertebrate vector. TBIs include transmission-blocking drugs and vaccines that, when given to humans, are taken up by mosquitoes and inhibit parasitic development within the vector. Accurate methodologies are key to assess TBI efficacy to ensure that only the most potent candidates progress to expensive and time-consuming clinical trials. Measuring intervention efficacy can be problematic because there is substantial variation in the number of parasites in both the host and vector populations, which can impact transmission even in laboratory settings. A statistically robust empirical method is introduced for estimating intervention efficacy from standardised population assay experiments. This method will be more reliable than simple summary statistics as it captures changes in parasite density in different life-stages. It also allows efficacy estimates at a finer resolution than previous methods enabling the impact of the intervention over successive generations to be tracked. A major advantage of the new methodology is that it makes no assumptions on the population dynamics of infection. This enables both host-to-vector and vector-to-host transmission to be density-dependent (or other) processes and generates easy-to-understand estimates of intervention efficacy. This method increases the precision of intervention efficacy estimates and demonstrates that relying on changes in infection prevalence (the proportion of infected hosts) alone may be insufficient to capture the impact of TBIs, which also suppress parasite density in secondarily infected hosts. The method indicates that potentially useful, partially effective TBIs may require multiple infection cycles before substantial reductions in prevalence are observed, despite more rapidly suppressing parasite density. Accurate models to quantify efficacy will have important implications for understanding how TBI candidates might perform in field situations and how they should be evaluated in clinical trials.

X Demographics

X Demographics

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

Mendeley readers

The data shown below were compiled from readership statistics for 46 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 1 2%
Unknown 45 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 17%
Student > Master 7 15%
Researcher 7 15%
Student > Bachelor 3 7%
Other 2 4%
Other 5 11%
Unknown 14 30%
Readers by discipline Count As %
Medicine and Dentistry 6 13%
Agricultural and Biological Sciences 4 9%
Immunology and Microbiology 3 7%
Chemistry 2 4%
Biochemistry, Genetics and Molecular Biology 2 4%
Other 13 28%
Unknown 16 35%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 14. 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 08 September 2017.
All research outputs
#2,532,296
of 24,744,050 outputs
Outputs from Malaria Journal
#517
of 5,793 outputs
Outputs of similar age
#46,535
of 313,966 outputs
Outputs of similar age from Malaria Journal
#16
of 126 outputs
Altmetric has tracked 24,744,050 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,793 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.9. This one has done particularly well, scoring higher than 91% 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 313,966 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 85% of its contemporaries.
We're also able to compare this research output to 126 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 88% of its contemporaries.