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X Demographics
Mendeley readers
Attention Score in Context
| Title |
A simulation study comparing aberration detection algorithms for syndromic surveillance
|
|---|---|
| Published in |
BMC Medical Informatics and Decision Making, March 2007
|
| DOI | 10.1186/1472-6947-7-6 |
| Pubmed ID | |
| Authors |
Michael L Jackson, Atar Baer, Ian Painter, Jeff Duchin |
| Abstract |
The usefulness of syndromic surveillance for early outbreak detection depends in part on effective statistical aberration detection. However, few published studies have compared different detection algorithms on identical data. In the largest simulation study conducted to date, we compared the performance of six aberration detection algorithms on simulated outbreaks superimposed on authentic syndromic surveillance data. |
X Demographics
The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| India | 2 | 50% |
| Australia | 1 | 25% |
| Unknown | 1 | 25% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Practitioners (doctors, other healthcare professionals) | 2 | 50% |
| Science communicators (journalists, bloggers, editors) | 1 | 25% |
| Members of the public | 1 | 25% |
Mendeley readers
The data shown below were compiled from readership statistics for 86 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 4 | 5% |
| Switzerland | 1 | 1% |
| Cuba | 1 | 1% |
| Iran, Islamic Republic of | 1 | 1% |
| United Kingdom | 1 | 1% |
| Spain | 1 | 1% |
| Denmark | 1 | 1% |
| Unknown | 76 | 88% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 25 | 29% |
| Researcher | 16 | 19% |
| Student > Master | 10 | 12% |
| Other | 7 | 8% |
| Student > Bachelor | 4 | 5% |
| Other | 13 | 15% |
| Unknown | 11 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 23 | 27% |
| Computer Science | 14 | 16% |
| Agricultural and Biological Sciences | 12 | 14% |
| Veterinary Science and Veterinary Medicine | 6 | 7% |
| Business, Management and Accounting | 4 | 5% |
| Other | 17 | 20% |
| Unknown | 10 | 12% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 05 August 2013.
All research outputs
#13,312,387
of 22,715,151 outputs
Outputs from BMC Medical Informatics and Decision Making
#971
of 1,982 outputs
Outputs of similar age
#64,879
of 76,272 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#2
of 4 outputs
Altmetric has tracked 22,715,151 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,982 research outputs from this source. They receive a mean Attention Score of 4.9. This one has gotten more attention than average, scoring higher than 50% 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 76,272 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 14th percentile – i.e., 14% of its contemporaries scored the same or lower than it.
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 2 of them.