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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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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.

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X Demographics

The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 66 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 62 94%

Demographic breakdown

Readers by professional status Count As %
Student > Master 15 23%
Researcher 10 15%
Student > Ph. D. Student 6 9%
Student > Doctoral Student 5 8%
Other 5 8%
Other 14 21%
Unknown 11 17%
Readers by discipline Count As %
Computer Science 14 21%
Medicine and Dentistry 12 18%
Agricultural and Biological Sciences 9 14%
Biochemistry, Genetics and Molecular Biology 3 5%
Mathematics 3 5%
Other 11 17%
Unknown 14 21%
Attention Score in Context

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
#14,756,074
of 22,715,151 outputs
Outputs from BMC Medical Informatics and Decision Making
#1,227
of 1,982 outputs
Outputs of similar age
#138,898
of 179,936 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#9
of 13 outputs
Altmetric has tracked 22,715,151 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% 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 is in the 34th percentile – i.e., 34% 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 179,936 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 22nd percentile – i.e., 22% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 13 others from the same source and published within six weeks on either side of this one. This one is in the 23rd percentile – i.e., 23% of its contemporaries scored the same or lower than it.