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Untangling the Interplay between Epidemic Spread and Transmission Network Dynamics

Overview of attention for article published in PLoS Computational Biology, November 2010
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (82nd percentile)
  • Good Attention Score compared to outputs of the same age and source (70th percentile)

Mentioned by

blogs
1 blog

Citations

dimensions_citation
62 Dimensions

Readers on

mendeley
132 Mendeley
citeulike
3 CiteULike
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Title
Untangling the Interplay between Epidemic Spread and Transmission Network Dynamics
Published in
PLoS Computational Biology, November 2010
DOI 10.1371/journal.pcbi.1000984
Pubmed ID
Authors

Christel Kamp

Abstract

The epidemic spread of infectious diseases is ubiquitous and often has a considerable impact on public health and economic wealth. The large variability in the spatio-temporal patterns of epidemics prohibits simple interventions and requires a detailed analysis of each epidemic with respect to its infectious agent and the corresponding routes of transmission. To facilitate this analysis, we introduce a mathematical framework which links epidemic patterns to the topology and dynamics of the underlying transmission network. The evolution, both in disease prevalence and transmission network topology, is derived from a closed set of partial differential equations for infections without allowing for recovery. The predictions are in excellent agreement with complementarily conducted agent-based simulations. The capacity of this new method is demonstrated in several case studies on HIV epidemics in synthetic populations: it allows us to monitor the evolution of contact behavior among healthy and infected individuals and the contributions of different disease stages to the spreading of the epidemic. This gives both direction to and a test bed for targeted intervention strategies for epidemic control. In conclusion, this mathematical framework provides a capable toolbox for the analysis of epidemics from first principles. This allows for fast, in silico modeling--and manipulation--of epidemics and is especially powerful if complemented with adequate empirical data for parameterization.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 132 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 7 5%
United States 4 3%
Italy 2 2%
Germany 2 2%
Switzerland 1 <1%
Australia 1 <1%
Israel 1 <1%
Portugal 1 <1%
Spain 1 <1%
Other 1 <1%
Unknown 111 84%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 42 32%
Researcher 35 27%
Professor 8 6%
Professor > Associate Professor 7 5%
Student > Master 7 5%
Other 21 16%
Unknown 12 9%
Readers by discipline Count As %
Medicine and Dentistry 20 15%
Agricultural and Biological Sciences 19 14%
Mathematics 16 12%
Computer Science 15 11%
Physics and Astronomy 11 8%
Other 25 19%
Unknown 26 20%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 July 2011.
All research outputs
#5,494,943
of 25,707,225 outputs
Outputs from PLoS Computational Biology
#4,145
of 9,024 outputs
Outputs of similar age
#33,631
of 189,988 outputs
Outputs of similar age from PLoS Computational Biology
#15
of 51 outputs
Altmetric has tracked 25,707,225 research outputs across all sources so far. Compared to these this one has done well and is in the 78th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 9,024 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.3. This one has gotten more attention than average, scoring higher than 54% 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 189,988 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 82% of its contemporaries.
We're also able to compare this research output to 51 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 70% of its contemporaries.