↓ Skip to main content

Unraveling the disease consequences and mechanisms of modular structure in animal social networks

Overview of attention for article published in Proceedings of the National Academy of Sciences of the United States of America, April 2017
Altmetric Badge

About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (94th percentile)
  • Good Attention Score compared to outputs of the same age and source (70th percentile)

Mentioned by

news
2 news outlets
policy
1 policy source
twitter
50 tweeters

Citations

dimensions_citation
127 Dimensions

Readers on

mendeley
183 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
Unraveling the disease consequences and mechanisms of modular structure in animal social networks
Published in
Proceedings of the National Academy of Sciences of the United States of America, April 2017
DOI 10.1073/pnas.1613616114
Pubmed ID
Authors

Pratha Sah, Stephan T. Leu, Paul C. Cross, Peter J. Hudson, Shweta Bansal

Abstract

Disease risk is a potential cost of group living. Although modular organization is thought to reduce this cost in animal societies, empirical evidence toward this hypothesis has been conflicting. We analyzed empirical social networks from 43 animal species to motivate our study of the epidemiological consequences of modular structure in animal societies. From these empirical studies, we identified the features of interaction patterns associated with network modularity and developed a theoretical network model to investigate when and how subdivisions in social networks influence disease dynamics. Contrary to prior work, we found that disease risk is largely unaffected by modular structure, although social networks beyond a modular threshold experience smaller disease burden and longer disease duration. Our results illustrate that the lowering of disease burden in highly modular social networks is driven by two mechanisms of modular organization: network fragmentation and subgroup cohesion. Highly fragmented social networks with cohesive subgroups are able to structurally trap infections within a few subgroups and also cause a structural delay to the spread of disease outbreaks. Finally, we show that network models incorporating modular structure are necessary only when prior knowledge suggests that interactions within the population are highly subdivided. Otherwise, null networks based on basic knowledge about group size and local contact heterogeneity may be sufficient when data-limited estimates of epidemic consequences are necessary. Overall, our work does not support the hypothesis that modular structure universally mitigates the disease impact of group living.

Twitter Demographics

The data shown below were collected from the profiles of 50 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 183 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 1%
Italy 1 <1%
Belgium 1 <1%
Canada 1 <1%
Unknown 178 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 48 26%
Researcher 42 23%
Student > Master 22 12%
Student > Doctoral Student 13 7%
Student > Bachelor 12 7%
Other 19 10%
Unknown 27 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 89 49%
Environmental Science 13 7%
Veterinary Science and Veterinary Medicine 9 5%
Social Sciences 5 3%
Business, Management and Accounting 4 2%
Other 20 11%
Unknown 43 23%

Attention Score in Context

This research output has an Altmetric Attention Score of 49. 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 13 March 2021.
All research outputs
#707,258
of 22,487,039 outputs
Outputs from Proceedings of the National Academy of Sciences of the United States of America
#12,274
of 97,830 outputs
Outputs of similar age
#15,746
of 285,282 outputs
Outputs of similar age from Proceedings of the National Academy of Sciences of the United States of America
#283
of 958 outputs
Altmetric has tracked 22,487,039 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 97,830 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 36.6. This one has done well, scoring higher than 87% 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 285,282 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 94% of its contemporaries.
We're also able to compare this research output to 958 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.