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Outbreak detection algorithms for seasonal disease data: a case study using ross river virus disease

Overview of attention for article published in BMC Medical Informatics and Decision Making, November 2010
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Mentioned by

twitter
3 tweeters

Citations

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12 Dimensions

Readers on

mendeley
61 Mendeley
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Title
Outbreak detection algorithms for seasonal disease data: a case study using ross river virus disease
Published in
BMC Medical Informatics and Decision Making, November 2010
DOI 10.1186/1472-6947-10-74
Pubmed ID
Authors

Anita M Pelecanos, Peter A Ryan, Michelle L Gatton

Abstract

Detection of outbreaks is an important part of disease surveillance. Although many algorithms have been designed for detecting outbreaks, few have been specifically assessed against diseases that have distinct seasonal incidence patterns, such as those caused by vector-borne pathogens.

Twitter Demographics

The data shown below were collected from the profiles of 3 tweeters 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 61 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Colombia 1 2%
United States 1 2%
Pakistan 1 2%
Brazil 1 2%
Unknown 57 93%

Demographic breakdown

Readers by professional status Count As %
Student > Master 14 23%
Researcher 10 16%
Student > Ph. D. Student 6 10%
Student > Doctoral Student 5 8%
Other 5 8%
Other 13 21%
Unknown 8 13%
Readers by discipline Count As %
Computer Science 14 23%
Medicine and Dentistry 12 20%
Agricultural and Biological Sciences 9 15%
Biochemistry, Genetics and Molecular Biology 3 5%
Mathematics 3 5%
Other 10 16%
Unknown 10 16%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 06 August 2013.
All research outputs
#7,480,375
of 12,409,138 outputs
Outputs from BMC Medical Informatics and Decision Making
#743
of 1,122 outputs
Outputs of similar age
#74,454
of 150,841 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#3
of 3 outputs
Altmetric has tracked 12,409,138 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,122 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 30th percentile – i.e., 30% 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 150,841 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 3 others from the same source and published within six weeks on either side of this one.