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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Evaluation of the Performance of a Dengue Outbreak Detection Tool for China
|
|---|---|
| Published in |
PLOS ONE, August 2014
|
| DOI | 10.1371/journal.pone.0106144 |
| Pubmed ID | |
| Authors |
Honglong Zhang, Zhongjie Li, Shengjie Lai, Archie C. A. Clements, Liping Wang, Wenwu Yin, Hang Zhou, Hongjie Yu, Wenbiao Hu, Weizhong Yang |
| Abstract |
An outbreak detection and response system, using time series moving percentile method based on historical data, in China has been used for identifying dengue fever outbreaks since 2008. For dengue fever outbreaks reported from 2009 to 2012, this system achieved a sensitivity of 100%, a specificity of 99.8% and a median time to detection of 3 days, which indicated that the system was a useful decision tool for dengue fever control and risk-management programs in China. |
X Demographics
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.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Australia | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 50% |
| Members of the public | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 73 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 4% |
| United Kingdom | 1 | 1% |
| Indonesia | 1 | 1% |
| Unknown | 68 | 93% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 20 | 27% |
| Student > Ph. D. Student | 13 | 18% |
| Student > Master | 9 | 12% |
| Student > Bachelor | 7 | 10% |
| Professor > Associate Professor | 3 | 4% |
| Other | 7 | 10% |
| Unknown | 14 | 19% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 14 | 19% |
| Medicine and Dentistry | 12 | 16% |
| Computer Science | 7 | 10% |
| Nursing and Health Professions | 6 | 8% |
| Biochemistry, Genetics and Molecular Biology | 3 | 4% |
| Other | 15 | 21% |
| Unknown | 16 | 22% |
Attention Score in Context
This research output has an Altmetric Attention Score of 35. 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 December 2014.
All research outputs
#1,006,449
of 23,340,595 outputs
Outputs from PLOS ONE
#13,494
of 199,597 outputs
Outputs of similar age
#10,851
of 237,533 outputs
Outputs of similar age from PLOS ONE
#352
of 4,972 outputs
Altmetric has tracked 23,340,595 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 199,597 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.3. This one has done particularly well, scoring higher than 93% 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 237,533 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 95% of its contemporaries.
We're also able to compare this research output to 4,972 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.