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Mathematical Models of Tuberculosis Reactivation and Relapse

Overview of attention for article published in Frontiers in Microbiology, May 2016
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (64th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (58th percentile)

Mentioned by

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5 X users

Citations

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33 Dimensions

Readers on

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65 Mendeley
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Title
Mathematical Models of Tuberculosis Reactivation and Relapse
Published in
Frontiers in Microbiology, May 2016
DOI 10.3389/fmicb.2016.00669
Pubmed ID
Authors

Robert S. Wallis

Abstract

The natural history of human infection with Mycobacterium tuberculosis (Mtb) is highly variable, as is the response to treatment of active tuberculosis. There is presently no direct means to identify individuals in whom Mtb infection has been eradicated, whether by a bactericidal immune response or sterilizing antimicrobial chemotherapy. Mathematical models can assist in such circumstances by measuring or predicting events that cannot be directly observed. The 3 models discussed in this review illustrate instances in which mathematical models were used to identify individuals with innate resistance to Mtb infection, determine the etiologic mechanism of tuberculosis in patients treated with tumor necrosis factor blockers, and predict the risk of relapse in persons undergoing tuberculosis treatment. These examples illustrate the power of various types of mathematic models to increase knowledge and thereby inform interventions in the present global tuberculosis epidemic.

X Demographics

X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 65 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 3%
United States 1 2%
Unknown 62 95%

Demographic breakdown

Readers by professional status Count As %
Student > Master 11 17%
Researcher 8 12%
Student > Ph. D. Student 8 12%
Student > Bachelor 7 11%
Professor 3 5%
Other 11 17%
Unknown 17 26%
Readers by discipline Count As %
Medicine and Dentistry 11 17%
Mathematics 6 9%
Nursing and Health Professions 5 8%
Biochemistry, Genetics and Molecular Biology 5 8%
Agricultural and Biological Sciences 4 6%
Other 13 20%
Unknown 21 32%
Attention Score in Context

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 24 March 2017.
All research outputs
#7,381,390
of 22,870,727 outputs
Outputs from Frontiers in Microbiology
#8,000
of 24,889 outputs
Outputs of similar age
#115,188
of 326,819 outputs
Outputs of similar age from Frontiers in Microbiology
#237
of 569 outputs
Altmetric has tracked 22,870,727 research outputs across all sources so far. This one has received more attention than most of these and is in the 67th percentile.
So far Altmetric has tracked 24,889 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.3. This one has gotten more attention than average, scoring higher than 67% 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 326,819 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 64% of its contemporaries.
We're also able to compare this research output to 569 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 58% of its contemporaries.