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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Leveraging social networks for understanding the evolution of epidemics
|
|---|---|
| Published in |
BMC Systems Biology, December 2011
|
| DOI | 10.1186/1752-0509-5-s3-s14 |
| Pubmed ID | |
| Authors |
Gonzalo Martín, Maria-Cristina Marinescu, David E Singh, Jesús Carretero |
| Abstract |
To understand how infectious agents disseminate throughout a population it is essential to capture the social model in a realistic manner. This paper presents a novel approach to modeling the propagation of the influenza virus throughout a realistic interconnection network based on actual individual interactions which we extract from online social networks. The advantage is that these networks can be extracted from existing sources which faithfully record interactions between people in their natural environment. We additionally allow modeling the characteristics of each individual as well as customizing his daily interaction patterns by making them time-dependent. Our purpose is to understand how the infection spreads depending on the structure of the contact network and the individuals who introduce the infection in the population. This would help public health authorities to respond more efficiently to epidemics. |
X Demographics
The data shown below were collected from the profiles of 8 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 6 | 75% |
| Unknown | 2 | 25% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 7 | 88% |
| Scientists | 1 | 13% |
Mendeley readers
The data shown below were compiled from readership statistics for 56 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Portugal | 1 | 2% |
| Unknown | 55 | 98% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 13 | 23% |
| Researcher | 11 | 20% |
| Professor > Associate Professor | 6 | 11% |
| Student > Master | 6 | 11% |
| Other | 3 | 5% |
| Other | 10 | 18% |
| Unknown | 7 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 13 | 23% |
| Medicine and Dentistry | 8 | 14% |
| Social Sciences | 6 | 11% |
| Agricultural and Biological Sciences | 3 | 5% |
| Nursing and Health Professions | 2 | 4% |
| Other | 13 | 23% |
| Unknown | 11 | 20% |
Attention Score in Context
This research output has an Altmetric Attention Score of 5. 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 03 April 2012.
All research outputs
#6,295,259
of 22,663,969 outputs
Outputs from BMC Systems Biology
#227
of 1,142 outputs
Outputs of similar age
#57,090
of 243,187 outputs
Outputs of similar age from BMC Systems Biology
#20
of 49 outputs
Altmetric has tracked 22,663,969 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 1,142 research outputs from this source. They receive a mean Attention Score of 3.6. This one has done well, scoring higher than 80% of its peers.
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 243,187 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 76% of its contemporaries.
We're also able to compare this research output to 49 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 59% of its contemporaries.