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Challenges in modeling complexity of neglected tropical diseases: a review of dynamics of visceral leishmaniasis in resource limited settings

Overview of attention for article published in Emerging Themes in Epidemiology, 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 (#12 of 114)
  • High Attention Score compared to outputs of the same age (88th percentile)

Mentioned by

news
1 news outlet
twitter
10 tweeters
wikipedia
1 Wikipedia page

Citations

dimensions_citation
9 Dimensions

Readers on

mendeley
67 Mendeley
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Title
Challenges in modeling complexity of neglected tropical diseases: a review of dynamics of visceral leishmaniasis in resource limited settings
Published in
Emerging Themes in Epidemiology, September 2017
DOI 10.1186/s12982-017-0065-3
Pubmed ID
Authors

Swati DebRoy, Olivia Prosper, Austin Mishoe, Anuj Mubayi

Abstract

Neglected tropical diseases (NTD), account for a large proportion of the global disease burden, and their control faces several challenges including diminishing human and financial resources for those distressed from such diseases. Visceral leishmaniasis (VL), the second-largest parasitic killer (after malaria) and an NTD affects poor populations and causes considerable cost to the affected individuals. Mathematical models can serve as a critical and cost-effective tool for understanding VL dynamics, however, complex array of socio-economic factors affecting its dynamics need to be identified and appropriately incorporated within a dynamical modeling framework. This study reviews literature on vector-borne diseases and collects challenges and successes related to the modeling of transmission dynamics of VL. Possible ways of creating a comprehensive mathematical model is also discussed. Published literature in three categories are reviewed: (i) identifying non-traditional but critical mechanisms for VL transmission in resource limited regions, (ii) mathematical models used for dynamics of Leishmaniasis and other related vector borne infectious diseases and (iii) examples of modeling that have the potential to capture identified mechanisms of VL to study its dynamics. This review suggests that VL elimination have not been achieved yet because existing transmission dynamics models for VL fails to capture relevant local socio-economic risk factors. This study identifies critical risk factors of VL and distribute them in six categories (atmosphere, access, availability, awareness, adherence, and accedence). The study also suggests novel quantitative models, parts of it are borrowed from other non-neglected diseases, for incorporating these factors and using them to understand VL dynamics and evaluating control programs for achieving VL elimination in a resource-limited environment. Controlling VL is expensive for local communities in endemic countries where individuals remain in the vicious cycle of disease and poverty. Smarter public investment in control programs would not only decrease the VL disease burden but will also help to alleviate poverty. However, dynamical models are necessary to evaluate intervention strategies to formulate a cost-effective optimal policy for eradication of VL.

Twitter Demographics

The data shown below were collected from the profiles of 10 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 67 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 1 1%
Unknown 66 99%

Demographic breakdown

Readers by professional status Count As %
Student > Doctoral Student 15 22%
Student > Master 12 18%
Student > Ph. D. Student 10 15%
Researcher 7 10%
Student > Bachelor 6 9%
Other 13 19%
Unknown 4 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 16 24%
Veterinary Science and Veterinary Medicine 7 10%
Medicine and Dentistry 7 10%
Immunology and Microbiology 5 7%
Biochemistry, Genetics and Molecular Biology 5 7%
Other 18 27%
Unknown 9 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 18. 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 28 March 2020.
All research outputs
#989,663
of 14,564,394 outputs
Outputs from Emerging Themes in Epidemiology
#12
of 114 outputs
Outputs of similar age
#30,376
of 274,549 outputs
Outputs of similar age from Emerging Themes in Epidemiology
#1
of 1 outputs
Altmetric has tracked 14,564,394 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 114 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.8. This one has done well, scoring higher than 89% 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 274,549 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 88% 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