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Modelling Co-Infection with Malaria and Lymphatic Filariasis

Overview of attention for article published in PLoS Computational Biology, June 2013
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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 (93rd percentile)
  • High Attention Score compared to outputs of the same age and source (87th percentile)

Mentioned by

news
1 news outlet
blogs
1 blog
twitter
5 X users
facebook
2 Facebook pages

Citations

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27 Dimensions

Readers on

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109 Mendeley
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Title
Modelling Co-Infection with Malaria and Lymphatic Filariasis
Published in
PLoS Computational Biology, June 2013
DOI 10.1371/journal.pcbi.1003096
Pubmed ID
Authors

Hannah C. Slater, Manoj Gambhir, Paul E. Parham, Edwin Michael

Abstract

Malaria and lymphatic filariasis (LF) continue to cause a considerable public health burden globally and are co-endemic in many regions of sub-Saharan Africa. These infections are transmitted by the same mosquito species which raises important questions about optimal vector control strategies in co-endemic regions, as well as the effect of the presence of each infection on endemicity of the other; there is currently little consensus on the latter. The need for comprehensive modelling studies to address such questions is therefore significant, yet very few have been undertaken to date despite the recognised explanatory power of reliable dynamic mathematical models. Here, we develop a malaria-LF co-infection modelling framework that accounts for two key interactions between these infections, namely the increase in vector mortality as LF mosquito prevalence increases and the antagonistic Th1/Th2 immune response that occurs in co-infected hosts. We consider the crucial interplay between these interactions on the resulting endemic prevalence when introducing each infection in regions where the other is already endemic (e.g. due to regional environmental change), and the associated timescale for such changes, as well as effects on the basic reproduction number R₀ of each disease. We also highlight potential perverse effects of vector controls on human infection prevalence in co-endemic regions, noting that understanding such effects is critical in designing optimal integrated control programmes. Hence, as well as highlighting where better data are required to more reliably address such questions, we provide an important framework that will form the basis of future scenario analysis tools used to plan and inform policy decisions on intervention measures in different transmission settings.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 109 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 2%
United States 2 2%
Indonesia 1 <1%
India 1 <1%
Unknown 103 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 17 16%
Researcher 15 14%
Student > Master 12 11%
Student > Bachelor 12 11%
Student > Doctoral Student 9 8%
Other 20 18%
Unknown 24 22%
Readers by discipline Count As %
Agricultural and Biological Sciences 23 21%
Mathematics 12 11%
Biochemistry, Genetics and Molecular Biology 10 9%
Medicine and Dentistry 10 9%
Environmental Science 7 6%
Other 21 19%
Unknown 26 24%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 22. 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 15 March 2016.
All research outputs
#1,770,249
of 25,914,360 outputs
Outputs from PLoS Computational Biology
#1,499
of 9,071 outputs
Outputs of similar age
#14,530
of 210,684 outputs
Outputs of similar age from PLoS Computational Biology
#14
of 108 outputs
Altmetric has tracked 25,914,360 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 9,071 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.2. This one has done well, scoring higher than 83% 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 210,684 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 93% of its contemporaries.
We're also able to compare this research output to 108 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 87% of its contemporaries.