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X Demographics
Mendeley readers
Attention Score in Context
Title |
A data-driven epidemiological prediction method for dengue outbreaks using local and remote sensing data
|
---|---|
Published in |
BMC Medical Informatics and Decision Making, November 2012
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DOI | 10.1186/1472-6947-12-124 |
Pubmed ID | |
Authors |
Anna L Buczak, Phillip T Koshute, Steven M Babin, Brian H Feighner, Sheryl H Lewis |
Abstract |
Dengue is the most common arboviral disease of humans, with more than one third of the world's population at risk. Accurate prediction of dengue outbreaks may lead to public health interventions that mitigate the effect of the disease. Predicting infectious disease outbreaks is a challenging task; truly predictive methods are still in their infancy. |
X Demographics
The data shown below were collected from the profiles of 9 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 3 | 33% |
India | 2 | 22% |
Tunisia | 1 | 11% |
Italy | 1 | 11% |
Unknown | 2 | 22% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Practitioners (doctors, other healthcare professionals) | 4 | 44% |
Members of the public | 4 | 44% |
Scientists | 1 | 11% |
Mendeley readers
The data shown below were compiled from readership statistics for 226 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 7 | 3% |
Brazil | 3 | 1% |
Pakistan | 1 | <1% |
Netherlands | 1 | <1% |
Peru | 1 | <1% |
United Kingdom | 1 | <1% |
Japan | 1 | <1% |
China | 1 | <1% |
Unknown | 210 | 93% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Master | 42 | 19% |
Student > Ph. D. Student | 38 | 17% |
Researcher | 37 | 16% |
Student > Bachelor | 23 | 10% |
Student > Doctoral Student | 15 | 7% |
Other | 40 | 18% |
Unknown | 31 | 14% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 42 | 19% |
Medicine and Dentistry | 37 | 16% |
Agricultural and Biological Sciences | 30 | 13% |
Environmental Science | 17 | 8% |
Earth and Planetary Sciences | 9 | 4% |
Other | 53 | 23% |
Unknown | 38 | 17% |
Attention Score in Context
This research output has an Altmetric Attention Score of 23. 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 29 August 2013.
All research outputs
#1,373,422
of 22,684,168 outputs
Outputs from BMC Medical Informatics and Decision Making
#58
of 1,979 outputs
Outputs of similar age
#9,179
of 183,394 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#3
of 41 outputs
Altmetric has tracked 22,684,168 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 1,979 research outputs from this source. They receive a mean Attention Score of 4.9. This one has done particularly well, scoring higher than 97% 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 183,394 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 94% of its contemporaries.
We're also able to compare this research output to 41 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 92% of its contemporaries.