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Infectious disease and health systems modelling for local decision making to control neglected tropical diseases

Overview of attention for article published in BMC Proceedings, December 2015
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Title
Infectious disease and health systems modelling for local decision making to control neglected tropical diseases
Published in
BMC Proceedings, December 2015
DOI 10.1186/1753-6561-9-s10-s6
Pubmed ID
Authors

T Deirdre Hollingsworth, Ivor Langley, D James Nokes, Eleanor E Macpherson, Gerry McGivern, Emily R Adams, Moses J Bockarie, Kevin Mortimer, Lisa J Reimer, Bertie Squire, Stephen J Torr, Graham F Medley

Abstract

Most neglected tropical diseases (NTDs) have complex life cycles and are challenging to control. The "2020 goals" of control and elimination as a public health programme for a number of NTDs are the subject of significant international efforts and investments. Beyond 2020 there will be a drive to maintain these gains and to push for true local elimination of transmission. However, these diseases are affected by variations in vectors, human demography, access to water and sanitation, access to interventions and local health systems. We therefore argue that there will be a need to develop local quantitative expertise to support elimination efforts. If available now, quantitative analyses would provide updated estimates of the burden of disease, assist in the design of locally appropriate control programmes, estimate the effectiveness of current interventions and support 'real-time' updates to local operations. Such quantitative tools are increasingly available at an international scale for NTDs, but are rarely tailored to local scenarios. Localised expertise not only provides an opportunity for more relevant analyses, but also has a greater chance of developing positive feedback between data collection and analysis by demonstrating the value of data. This is essential as rational program design relies on good quality data collection. It is also likely that if such infrastructure is provided for NTDs there will be an additional impact on the health system more broadly. Locally tailored quantitative analyses can help achieve sustainable and effective control of NTDs, but also underpin the development of local health care systems.

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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 45 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 2%
Brazil 1 2%
Unknown 43 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 20%
Student > Ph. D. Student 9 20%
Student > Master 5 11%
Student > Doctoral Student 3 7%
Professor 3 7%
Other 6 13%
Unknown 10 22%
Readers by discipline Count As %
Agricultural and Biological Sciences 12 27%
Medicine and Dentistry 5 11%
Immunology and Microbiology 5 11%
Mathematics 2 4%
Nursing and Health Professions 2 4%
Other 6 13%
Unknown 13 29%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 03 March 2016.
All research outputs
#14,243,242
of 22,836,570 outputs
Outputs from BMC Proceedings
#188
of 375 outputs
Outputs of similar age
#203,425
of 388,246 outputs
Outputs of similar age from BMC Proceedings
#13
of 16 outputs
Altmetric has tracked 22,836,570 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 375 research outputs from this source. They receive a mean Attention Score of 4.0. This one is in the 47th percentile – i.e., 47% of its peers scored the same or lower than it.
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 388,246 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 16 others from the same source and published within six weeks on either side of this one. This one is in the 12th percentile – i.e., 12% of its contemporaries scored the same or lower than it.