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Inferring infection hazard in wildlife populations by linking data across individual and population scales

Overview of attention for article published in Ecology Letters, January 2017
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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 (85th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
16 tweeters
googleplus
1 Google+ user

Citations

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13 Dimensions

Readers on

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134 Mendeley
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Title
Inferring infection hazard in wildlife populations by linking data across individual and population scales
Published in
Ecology Letters, January 2017
DOI 10.1111/ele.12732
Pubmed ID
Authors

Kim M. Pepin, Shannon L. Kay, Ben D. Golas, Susan S. Shriner, Amy T. Gilbert, Ryan S. Miller, Andrea L. Graham, Steven Riley, Paul C. Cross, Michael D. Samuel, Mevin B. Hooten, Jennifer A. Hoeting, James O. Lloyd-Smith, Colleen T. Webb, Michael G. Buhnerkempe

Abstract

Our ability to infer unobservable disease-dynamic processes such as force of infection (infection hazard for susceptible hosts) has transformed our understanding of disease transmission mechanisms and capacity to predict disease dynamics. Conventional methods for inferring FOI estimate a time-averaged value and are based on population-level processes. Because many pathogens exhibit epidemic cycling and FOI is the result of processes acting across the scales of individuals and populations, a flexible framework that extends to epidemic dynamics and links within-host processes to FOI is needed. Specifically, within-host antibody kinetics in wildlife hosts can be short-lived and produce patterns that are repeatable across individuals, suggesting individual-level antibody concentrations could be used to infer time since infection and hence FOI. Using simulations and case studies (influenza A in lesser snow geese and Yersinia pestis in coyotes), we argue that with careful experimental and surveillance design, the population-level FOI signal can be recovered from individual-level antibody kinetics, despite substantial individual-level variation. In addition to improving inference, the cross-scale quantitative antibody approach we describe can reveal insights into drivers of individual-based variation in disease response, and the role of poorly understood processes such as secondary infections, in population-level dynamics of disease.

Twitter Demographics

The data shown below were collected from the profiles of 16 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 134 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 3%
Brazil 1 <1%
United Kingdom 1 <1%
Finland 1 <1%
France 1 <1%
Unknown 126 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 37 28%
Student > Ph. D. Student 26 19%
Student > Master 18 13%
Unspecified 14 10%
Student > Bachelor 9 7%
Other 30 22%
Readers by discipline Count As %
Agricultural and Biological Sciences 65 49%
Unspecified 30 22%
Environmental Science 19 14%
Veterinary Science and Veterinary Medicine 6 4%
Biochemistry, Genetics and Molecular Biology 3 2%
Other 11 8%

Attention Score in Context

This research output has an Altmetric Attention Score of 11. 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 21 March 2018.
All research outputs
#1,310,275
of 12,960,324 outputs
Outputs from Ecology Letters
#869
of 2,059 outputs
Outputs of similar age
#48,456
of 341,294 outputs
Outputs of similar age from Ecology Letters
#26
of 38 outputs
Altmetric has tracked 12,960,324 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,059 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 19.9. This one has gotten more attention than average, scoring higher than 56% 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 341,294 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 85% of its contemporaries.
We're also able to compare this research output to 38 others from the same source and published within six weeks on either side of this one. This one is in the 31st percentile – i.e., 31% of its contemporaries scored the same or lower than it.