↓ Skip to main content

in silico Surveillance: evaluating outbreak detection with simulation models

Overview of attention for article published in BMC Medical Informatics and Decision Making, January 2013
Altmetric Badge

About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (51st percentile)

Mentioned by

twitter
3 tweeters

Citations

dimensions_citation
9 Dimensions

Readers on

mendeley
36 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
in silico Surveillance: evaluating outbreak detection with simulation models
Published in
BMC Medical Informatics and Decision Making, January 2013
DOI 10.1186/1472-6947-13-12
Pubmed ID
Authors

Bryan Lewis, Stephen Eubank, Allyson M Abrams, Ken Kleinman

Abstract

Detecting outbreaks is a crucial task for public health officials, yet gaps remain in the systematic evaluation of outbreak detection protocols. The authors' objectives were to design, implement, and test a flexible methodology for generating detailed synthetic surveillance data that provides realistic geographical and temporal clustering of cases and use to evaluate outbreak detection protocols.

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 36 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 3%
United States 1 3%
Australia 1 3%
Unknown 33 92%

Demographic breakdown

Readers by professional status Count As %
Researcher 12 33%
Student > Ph. D. Student 9 25%
Student > Master 4 11%
Other 2 6%
Student > Bachelor 2 6%
Other 4 11%
Unknown 3 8%
Readers by discipline Count As %
Medicine and Dentistry 8 22%
Computer Science 7 19%
Agricultural and Biological Sciences 5 14%
Veterinary Science and Veterinary Medicine 2 6%
Nursing and Health Professions 2 6%
Other 8 22%
Unknown 4 11%

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 24 January 2013.
All research outputs
#5,665,571
of 10,502,506 outputs
Outputs from BMC Medical Informatics and Decision Making
#619
of 1,044 outputs
Outputs of similar age
#140,685
of 307,948 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#50
of 59 outputs
Altmetric has tracked 10,502,506 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,044 research outputs from this source. They receive a mean Attention Score of 4.8. 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 307,948 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 51% of its contemporaries.
We're also able to compare this research output to 59 others from the same source and published within six weeks on either side of this one. This one is in the 13th percentile – i.e., 13% of its contemporaries scored the same or lower than it.