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Addressing population heterogeneity and distribution in epidemics models using a cellular automata approach

Overview of attention for article published in BMC Research Notes, April 2014
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Mentioned by

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2 tweeters

Citations

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16 Dimensions

Readers on

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31 Mendeley
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Title
Addressing population heterogeneity and distribution in epidemics models using a cellular automata approach
Published in
BMC Research Notes, April 2014
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.

Twitter Demographics

The data shown below were collected from the profiles of 2 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 31 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 29 94%

Demographic breakdown

Readers by professional status Count As %
Student > Master 6 19%
Student > Ph. D. Student 5 16%
Student > Bachelor 5 16%
Researcher 3 10%
Student > Doctoral Student 2 6%
Other 5 16%
Unknown 5 16%
Readers by discipline Count As %
Computer Science 4 13%
Mathematics 4 13%
Biochemistry, Genetics and Molecular Biology 2 6%
Agricultural and Biological Sciences 2 6%
Environmental Science 2 6%
Other 10 32%
Unknown 7 23%

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 18 February 2020.
All research outputs
#10,578,969
of 17,009,769 outputs
Outputs from BMC Research Notes
#1,717
of 3,619 outputs
Outputs of similar age
#156,826
of 312,124 outputs
Outputs of similar age from BMC Research Notes
#120
of 239 outputs
Altmetric has tracked 17,009,769 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 3,619 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.0. This one is in the 48th percentile – i.e., 48% 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 312,124 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 46th percentile – i.e., 46% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 239 others from the same source and published within six weeks on either side of this one. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.