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DotMapper: an open source tool for creating interactive disease point maps

Overview of attention for article published in BMC Infectious Diseases, April 2016
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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 (80th percentile)
  • High Attention Score compared to outputs of the same age and source (84th percentile)

Mentioned by

policy
1 policy source
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8 X users

Citations

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18 Dimensions

Readers on

mendeley
65 Mendeley
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Title
DotMapper: an open source tool for creating interactive disease point maps
Published in
BMC Infectious Diseases, April 2016
DOI 10.1186/s12879-016-1475-5
Pubmed ID
Authors

Catherine M. Smith, Andrew C. Hayward

Abstract

Molecular strain typing of tuberculosis isolates has led to increased understanding of the epidemiological characteristics of the disease and improvements in its control, diagnosis and treatment. However, molecular cluster investigations, which aim to detect previously unidentified cases, remain challenging. Interactive dot mapping is a simple approach which could aid investigations by highlighting cases likely to share epidemiological links. Current tools generally require technical expertise or lack interactivity. We designed a flexible application for producing disease dot maps using Shiny, a web application framework for the statistical software, R. The application displays locations of cases on an interactive map colour coded according to levels of categorical variables such as demographics and risk factors. Cases can be filtered by selecting combinations of these characteristics and by notification date. It can be used to rapidly identify geographic patterns amongst cases in molecular clusters of tuberculosis in space and time; generate hypotheses about disease transmission; identify outliers, and guide targeted control measures. DotMapper is a user-friendly application which enables rapid production of maps displaying locations of cases and their epidemiological characteristics without the need for specialist training in geographic information systems. Enhanced understanding of tuberculosis transmission using this application could facilitate improved detection of cases with epidemiological links and therefore lessen the public health impacts of the disease. It is a flexible system and also has broad international potential application to other investigations using geo-coded health information.

X Demographics

X Demographics

The data shown below were collected from the profiles of 8 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 65 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 1 2%
Unknown 64 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 12 18%
Student > Bachelor 8 12%
Student > Ph. D. Student 5 8%
Student > Master 5 8%
Professor 4 6%
Other 12 18%
Unknown 19 29%
Readers by discipline Count As %
Medicine and Dentistry 17 26%
Agricultural and Biological Sciences 4 6%
Nursing and Health Professions 3 5%
Engineering 3 5%
Social Sciences 3 5%
Other 13 20%
Unknown 22 34%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 9. 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 25 February 2019.
All research outputs
#3,953,061
of 24,217,496 outputs
Outputs from BMC Infectious Diseases
#1,278
of 8,102 outputs
Outputs of similar age
#59,885
of 305,537 outputs
Outputs of similar age from BMC Infectious Diseases
#19
of 113 outputs
Altmetric has tracked 24,217,496 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,102 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.5. This one has done well, scoring higher than 84% 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 305,537 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 80% of its contemporaries.
We're also able to compare this research output to 113 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 84% of its contemporaries.