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The reachability of contagion in temporal contact networks: how disease latency can exploit the rhythm of human behavior

Overview of attention for article published in BMC Infectious Diseases, May 2018
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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 (95th percentile)

Mentioned by

news
2 news outlets
twitter
66 tweeters

Citations

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9 Dimensions

Readers on

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35 Mendeley
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Title
The reachability of contagion in temporal contact networks: how disease latency can exploit the rhythm of human behavior
Published in
BMC Infectious Diseases, May 2018
DOI 10.1186/s12879-018-3117-6
Pubmed ID
Authors

Ewan Colman, Kristen Spies, Shweta Bansal

Abstract

The symptoms of many infectious diseases influence their host to withdraw from social activity limiting their potential to spread. Successful transmission therefore requires the onset of infectiousness to coincide with a time when the host is socially active. Since social activity and infectiousness are both temporal phenomena, we hypothesize that diseases are most pervasive when these two processes are synchronized. We consider disease dynamics that incorporate behavioral responses that effectively shorten the infectious period of the pathogen. Using data collected from face-to-face social interactions and synthetic contact networks constructed from empirical demographic data, we measure the reachability of this disease model and perform disease simulations over a range of latent period durations. We find that maximum transmission risk results when the disease latent period (and thus the generation time) are synchronized with human circadian rhythms of 24 h, and minimum transmission risk when latent periods are out of phase with circadian rhythms by 12 h. The effect of this synchronization is present for a range of disease models with realistic disease parameters and host behavioral responses. The reproductive potential of pathogens is linked inextricably to the host social behavior required for transmission. We propose that future work should consider contact periodicity in models of disease dynamics, and suggest the possibility that disease control strategies may be designed to optimize against the effects of synchronization.

Twitter Demographics

The data shown below were collected from the profiles of 66 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 35 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 35 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 8 23%
Researcher 6 17%
Student > Ph. D. Student 5 14%
Student > Postgraduate 3 9%
Other 3 9%
Other 6 17%
Unknown 4 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 9 26%
Medicine and Dentistry 4 11%
Social Sciences 4 11%
Physics and Astronomy 3 9%
Veterinary Science and Veterinary Medicine 2 6%
Other 6 17%
Unknown 7 20%

Attention Score in Context

This research output has an Altmetric Attention Score of 61. 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 16 December 2020.
All research outputs
#473,573
of 19,192,362 outputs
Outputs from BMC Infectious Diseases
#94
of 6,711 outputs
Outputs of similar age
#13,370
of 291,798 outputs
Outputs of similar age from BMC Infectious Diseases
#1
of 1 outputs
Altmetric has tracked 19,192,362 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 6,711 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.6. This one has done particularly well, scoring higher than 98% 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 291,798 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 95% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them