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Supersize me: how whole-genome sequencing and big data are transforming epidemiology

Overview of attention for article published in Trends in Microbiology, May 2014
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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 (93rd percentile)
  • High Attention Score compared to outputs of the same age and source (90th percentile)

Mentioned by

policy
1 policy source
twitter
32 tweeters

Citations

dimensions_citation
93 Dimensions

Readers on

mendeley
339 Mendeley
citeulike
1 CiteULike
Title
Supersize me: how whole-genome sequencing and big data are transforming epidemiology
Published in
Trends in Microbiology, May 2014
DOI 10.1016/j.tim.2014.02.011
Pubmed ID
Authors

Rowland R. Kao, Daniel T. Haydon, Samantha J. Lycett, Pablo R. Murcia

Abstract

In epidemiology, the identification of 'who infected whom' allows us to quantify key characteristics such as incubation periods, heterogeneity in transmission rates, duration of infectiousness, and the existence of high-risk groups. Although invaluable, the existence of many plausible infection pathways makes this difficult, and epidemiological contact tracing either uncertain, logistically prohibitive, or both. The recent advent of next-generation sequencing technology allows the identification of traceable differences in the pathogen genome that are transforming our ability to understand high-resolution disease transmission, sometimes even down to the host-to-host scale. We review recent examples of the use of pathogen whole-genome sequencing for the purpose of forensic tracing of transmission pathways, focusing on the particular problems where evolutionary dynamics must be supplemented by epidemiological information on the most likely timing of events as well as possible transmission pathways. We also discuss potential pitfalls in the over-interpretation of these data, and highlight the manner in which a confluence of this technology with sophisticated mathematical and statistical approaches has the potential to produce a paradigm shift in our understanding of infectious disease transmission and control.

Twitter Demographics

The data shown below were collected from the profiles of 32 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 339 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 10 3%
United Kingdom 4 1%
Brazil 3 <1%
Australia 2 <1%
Switzerland 2 <1%
Sweden 2 <1%
Kenya 1 <1%
Uruguay 1 <1%
Italy 1 <1%
Other 10 3%
Unknown 303 89%

Demographic breakdown

Readers by professional status Count As %
Researcher 73 22%
Student > Ph. D. Student 68 20%
Student > Master 48 14%
Student > Bachelor 24 7%
Professor > Associate Professor 21 6%
Other 73 22%
Unknown 32 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 129 38%
Medicine and Dentistry 33 10%
Biochemistry, Genetics and Molecular Biology 26 8%
Computer Science 24 7%
Veterinary Science and Veterinary Medicine 15 4%
Other 69 20%
Unknown 43 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 22. 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 01 January 2018.
All research outputs
#1,060,493
of 17,353,889 outputs
Outputs from Trends in Microbiology
#236
of 1,903 outputs
Outputs of similar age
#13,476
of 197,603 outputs
Outputs of similar age from Trends in Microbiology
#3
of 31 outputs
Altmetric has tracked 17,353,889 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,903 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.2. 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 197,603 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 93% of its contemporaries.
We're also able to compare this research output to 31 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 90% of its contemporaries.