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Attention Score in Context
Title |
Addressing population heterogeneity and distribution in epidemics models using a cellular automata approach
|
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Published in |
BMC Research Notes, April 2014
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DOI | 10.1186/1756-0500-7-234 |
Pubmed ID | |
Authors |
Leonardo López, Germán Burguerner, Leonardo Giovanini |
Abstract |
The spread of an infectious disease is determined by biological and social factors. Models based on cellular automata are adequate to describe such natural systems consisting of a massive collection of simple interacting objects. They characterize the time evolution of the global system as the emergent behaviour resulting from the interaction of the objects, whose behaviour is defined through a set of simple rules that encode the individual behaviour and the transmission dynamic. |
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 % |
---|---|---|
Unknown | 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 % |
---|---|---|
Malaysia | 1 | 3% |
United Kingdom | 1 | 3% |
Unknown | 32 | 94% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Master | 8 | 24% |
Student > Bachelor | 5 | 15% |
Student > Ph. D. Student | 5 | 15% |
Researcher | 3 | 9% |
Student > Doctoral Student | 2 | 6% |
Other | 5 | 15% |
Unknown | 6 | 18% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 4 | 12% |
Mathematics | 4 | 12% |
Agricultural and Biological Sciences | 3 | 9% |
Biochemistry, Genetics and Molecular Biology | 2 | 6% |
Medicine and Dentistry | 2 | 6% |
Other | 10 | 29% |
Unknown | 9 | 26% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 15 December 2014.
All research outputs
#16,539,065
of 24,333,504 outputs
Outputs from BMC Research Notes
#2,425
of 4,379 outputs
Outputs of similar age
#138,389
of 231,336 outputs
Outputs of similar age from BMC Research Notes
#43
of 72 outputs
Altmetric has tracked 24,333,504 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,379 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.0. This one is in the 33rd percentile – i.e., 33% 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 231,336 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 30th percentile – i.e., 30% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 72 others from the same source and published within six weeks on either side of this one. This one is in the 33rd percentile – i.e., 33% of its contemporaries scored the same or lower than it.