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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)

Mentioned by

policy
1 policy source
twitter
7 tweeters

Citations

dimensions_citation
10 Dimensions

Readers on

mendeley
39 Mendeley
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.

Twitter Demographics

The data shown below were collected from the profiles of 7 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 39 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 1 3%
Unknown 38 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 26%
Unspecified 5 13%
Student > Master 5 13%
Professor 4 10%
Student > Bachelor 4 10%
Other 11 28%
Readers by discipline Count As %
Medicine and Dentistry 15 38%
Unspecified 7 18%
Agricultural and Biological Sciences 4 10%
Social Sciences 3 8%
Biochemistry, Genetics and Molecular Biology 2 5%
Other 8 21%

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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
#2,014,903
of 13,415,696 outputs
Outputs from BMC Infectious Diseases
#647
of 5,000 outputs
Outputs of similar age
#51,113
of 265,668 outputs
Outputs of similar age from BMC Infectious Diseases
#1
of 4 outputs
Altmetric has tracked 13,415,696 research outputs across all sources so far. Compared to these this one has done well and is in the 84th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,000 research outputs from this source. They receive a mean Attention Score of 4.8. This one has done well, scoring higher than 87% 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 265,668 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 4 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