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Test-and-treat approach to HIV/AIDS: a primer for mathematical modeling

Overview of attention for article published in Theoretical Biology and Medical Modelling, September 2017
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#40 of 287)
  • High Attention Score compared to outputs of the same age (82nd percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

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1 news outlet
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2 X users

Citations

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18 Dimensions

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115 Mendeley
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Title
Test-and-treat approach to HIV/AIDS: a primer for mathematical modeling
Published in
Theoretical Biology and Medical Modelling, September 2017
DOI 10.1186/s12976-017-0062-9
Pubmed ID
Authors

Kyeongah Nah, Hiroshi Nishiura, Naho Tsuchiya, Xiaodan Sun, Yusuke Asai, Akifumi Imamura

Abstract

The public benefit of test-and-treat has induced a need to justify goodness for the public, and mathematical modeling studies have played a key role in designing and evaluating the test-and-treat strategy for controlling HIV/AIDS. Here we briefly and comprehensively review the essence of contemporary understanding of the test-and-treat policy through mathematical modeling approaches and identify key pitfalls that have been identified to date. While the decrease in HIV incidence is achieved with certain coverages of diagnosis, care and continued treatment, HIV prevalence is not necessarily decreased and sometimes the test-and-treat is accompanied by increased long-term cost of antiretroviral therapy (ART). To confront with the complexity of assessment on this policy, the elimination threshold or the effective reproduction number has been proposed for its use in determining the overall success to anticipate the eventual elimination. Since the publication of original model in 2009, key issues of test-and-treat modeling studies have been identified, including theoretical problems surrounding the sexual partnership network, heterogeneities in the transmission dynamics, and realistic issues of achieving and maintaining high treatment coverage in the most hard-to-reach populations. To explicitly design country-specific control policy, quantitative modeling approaches to each single setting with differing epidemiological context would require multi-disciplinary collaborations among clinicians, public health practitioners, laboratory technologists, epidemiologists and mathematical modelers.

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X Demographics

The data shown below were collected from the profiles of 2 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 115 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 115 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 25 22%
Student > Postgraduate 11 10%
Researcher 8 7%
Student > Ph. D. Student 8 7%
Student > Doctoral Student 7 6%
Other 18 16%
Unknown 38 33%
Readers by discipline Count As %
Medicine and Dentistry 23 20%
Nursing and Health Professions 18 16%
Social Sciences 9 8%
Pharmacology, Toxicology and Pharmaceutical Science 4 3%
Mathematics 3 3%
Other 12 10%
Unknown 46 40%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 11. 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 19 October 2017.
All research outputs
#2,952,317
of 22,999,744 outputs
Outputs from Theoretical Biology and Medical Modelling
#40
of 287 outputs
Outputs of similar age
#56,620
of 315,613 outputs
Outputs of similar age from Theoretical Biology and Medical Modelling
#3
of 5 outputs
Altmetric has tracked 22,999,744 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 287 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.4. This one has done well, scoring higher than 86% 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 315,613 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 82% of its contemporaries.
We're also able to compare this research output to 5 others from the same source and published within six weeks on either side of this one. This one has scored higher than 2 of them.