↓ Skip to main content

A Framework for Modeling Emerging Diseases to Inform Management

Overview of attention for article published in Emerging Infectious Diseases, January 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 (95th percentile)
  • High Attention Score compared to outputs of the same age and source (91st percentile)

Mentioned by

news
4 news outlets
twitter
25 tweeters
facebook
4 Facebook pages

Citations

dimensions_citation
16 Dimensions

Readers on

mendeley
82 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
A Framework for Modeling Emerging Diseases to Inform Management
Published in
Emerging Infectious Diseases, January 2017
DOI 10.3201/eid2301.161452
Pubmed ID
Authors

Robin E. Russell, Rachel A. Katz, Katherine L.D. Richgels, Daniel P. Walsh, Evan H.C. Grant

Abstract

The rapid emergence and reemergence of zoonotic diseases requires the ability to rapidly evaluate and implement optimal management decisions. Actions to control or mitigate the effects of emerging pathogens are commonly delayed because of uncertainty in the estimates and the predicted outcomes of the control tactics. The development of models that describe the best-known information regarding the disease system at the early stages of disease emergence is an essential step for optimal decision-making. Models can predict the potential effects of the pathogen, provide guidance for assessing the likelihood of success of different proposed management actions, quantify the uncertainty surrounding the choice of the optimal decision, and highlight critical areas for immediate research. We demonstrate how to develop models that can be used as a part of a decision-making framework to determine the likelihood of success of different management actions given current knowledge.

Twitter Demographics

The data shown below were collected from the profiles of 25 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 82 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 2%
Unknown 80 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 19 23%
Student > Ph. D. Student 16 20%
Student > Master 12 15%
Student > Doctoral Student 10 12%
Student > Postgraduate 7 9%
Other 10 12%
Unknown 8 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 30 37%
Environmental Science 13 16%
Medicine and Dentistry 9 11%
Veterinary Science and Veterinary Medicine 5 6%
Nursing and Health Professions 2 2%
Other 11 13%
Unknown 12 15%

Attention Score in Context

This research output has an Altmetric Attention Score of 50. 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 January 2018.
All research outputs
#509,124
of 17,362,547 outputs
Outputs from Emerging Infectious Diseases
#646
of 7,938 outputs
Outputs of similar age
#16,829
of 397,048 outputs
Outputs of similar age from Emerging Infectious Diseases
#11
of 134 outputs
Altmetric has tracked 17,362,547 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 7,938 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 39.6. This one has done particularly well, scoring higher than 91% 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 397,048 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 134 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 91% of its contemporaries.