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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Epidemic features affecting the performance of outbreak detection algorithms
|
|---|---|
| Published in |
BMC Public Health, June 2012
|
| DOI | 10.1186/1471-2458-12-418 |
| Pubmed ID | |
| Authors |
Jie Kuang, Wei Zhong Yang, Ding Lun Zhou, Zhong Jie Li, Ya Jia Lan |
| Abstract |
Outbreak detection algorithms play an important role in effective automated surveillance. Although many algorithms have been designed to improve the performance of outbreak detection, few published studies have examined how epidemic features of infectious disease impact on the detection performance of algorithms. This study compared the performance of three outbreak detection algorithms stratified by epidemic features of infectious disease and examined the relationship between epidemic features and performance of outbreak detection algorithms. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 34 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| India | 1 | 3% |
| United States | 1 | 3% |
| Colombia | 1 | 3% |
| Australia | 1 | 3% |
| Unknown | 30 | 88% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 9 | 26% |
| Student > Ph. D. Student | 8 | 24% |
| Student > Master | 6 | 18% |
| Student > Bachelor | 3 | 9% |
| Student > Doctoral Student | 1 | 3% |
| Other | 4 | 12% |
| Unknown | 3 | 9% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 8 | 24% |
| Medicine and Dentistry | 6 | 18% |
| Nursing and Health Professions | 3 | 9% |
| Veterinary Science and Veterinary Medicine | 2 | 6% |
| Social Sciences | 2 | 6% |
| Other | 9 | 26% |
| Unknown | 4 | 12% |
Attention Score in Context
This research output has an Altmetric Attention Score of 4. 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 26 March 2018.
All research outputs
#7,229,557
of 23,577,761 outputs
Outputs from BMC Public Health
#7,529
of 15,294 outputs
Outputs of similar age
#50,572
of 168,251 outputs
Outputs of similar age from BMC Public Health
#95
of 232 outputs
Altmetric has tracked 23,577,761 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 15,294 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.1. This one is in the 49th percentile – i.e., 49% 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 168,251 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 68% of its contemporaries.
We're also able to compare this research output to 232 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 57% of its contemporaries.