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Effects of georeferencing effort on mapping monkeypox case distributions and transmission risk

Overview of attention for article published in International Journal of Health Geographics, January 2012
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1 tweeter
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2 Facebook pages

Citations

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20 Dimensions

Readers on

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122 Mendeley
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Title
Effects of georeferencing effort on mapping monkeypox case distributions and transmission risk
Published in
International Journal of Health Geographics, January 2012
DOI 10.1186/1476-072x-11-23
Pubmed ID
Authors

R Lash, Darin S Carroll, Christine M Hughes, Yoshinori Nakazawa, Kevin Karem, Inger K Damon, A Peterson

Abstract

Maps of disease occurrences and GIS-based models of disease transmission risk are increasingly common, and both rely on georeferenced diseases data. Automated methods for georeferencing disease data have been widely studied for developed countries with rich sources of geographic referenced data. However, the transferability of these methods to countries without comparable geographic reference data, particularly when working with historical disease data, has not been as widely studied. Historically, precise geographic information about where individual cases occur has been collected and stored verbally, identifying specific locations using place names. Georeferencing historic data is challenging however, because it is difficult to find appropriate geographic reference data to match the place names to. Here, we assess the degree of care and research invested in converting textual descriptions of disease occurrence locations to numerical grid coordinates (latitude and longitude). Specifically, we develop three datasets from the same, original monkeypox disease occurrence data, with varying levels of care and effort: the first based on an automated web-service, the second improving on the first by reference to additional maps and digital gazetteers, and the third improving still more based on extensive consultation of legacy surveillance records that provided considerable additional information about each case. To illustrate the implications of these seemingly subtle improvements in data quality, we develop ecological niche models and predictive maps of monkeypox transmission risk based on each of the three occurrence data sets.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 122 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 3%
Brazil 2 2%
India 2 2%
Canada 1 <1%
Italy 1 <1%
Spain 1 <1%
Denmark 1 <1%
Unknown 110 90%

Demographic breakdown

Readers by professional status Count As %
Researcher 29 24%
Student > Ph. D. Student 22 18%
Student > Master 19 16%
Student > Doctoral Student 11 9%
Student > Bachelor 10 8%
Other 24 20%
Unknown 7 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 34 28%
Medicine and Dentistry 26 21%
Environmental Science 13 11%
Social Sciences 8 7%
Computer Science 6 5%
Other 19 16%
Unknown 16 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 12 July 2012.
All research outputs
#7,871,342
of 12,545,316 outputs
Outputs from International Journal of Health Geographics
#317
of 475 outputs
Outputs of similar age
#66,169
of 119,789 outputs
Outputs of similar age from International Journal of Health Geographics
#13
of 19 outputs
Altmetric has tracked 12,545,316 research outputs across all sources so far. This one is in the 23rd percentile – i.e., 23% of other outputs scored the same or lower than it.
So far Altmetric has tracked 475 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.3. This one is in the 25th percentile – i.e., 25% 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 119,789 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 19 others from the same source and published within six weeks on either side of this one. This one is in the 31st percentile – i.e., 31% of its contemporaries scored the same or lower than it.