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Simulations for designing and interpreting intervention trials in infectious diseases

Overview of attention for article published in BMC Medicine, December 2017
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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 (92nd percentile)
  • Good Attention Score compared to outputs of the same age and source (74th percentile)

Mentioned by

twitter
45 tweeters

Citations

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15 Dimensions

Readers on

mendeley
46 Mendeley
Title
Simulations for designing and interpreting intervention trials in infectious diseases
Published in
BMC Medicine, December 2017
DOI 10.1186/s12916-017-0985-3
Pubmed ID
Authors

M. Elizabeth Halloran, Kari Auranen, Sarah Baird, Nicole E. Basta, Steven E. Bellan, Ron Brookmeyer, Ben S. Cooper, Victor DeGruttola, James P. Hughes, Justin Lessler, Eric T. Lofgren, Ira M. Longini, Jukka-Pekka Onnela, Berk Özler, George R. Seage, Thomas A. Smith, Alessandro Vespignani, Emilia Vynnycky, Marc Lipsitch

Abstract

Interventions in infectious diseases can have both direct effects on individuals who receive the intervention as well as indirect effects in the population. In addition, intervention combinations can have complex interactions at the population level, which are often difficult to adequately assess with standard study designs and analytical methods. Herein, we urge the adoption of a new paradigm for the design and interpretation of intervention trials in infectious diseases, particularly with regard to emerging infectious diseases, one that more accurately reflects the dynamics of the transmission process. In an increasingly complex world, simulations can explicitly represent transmission dynamics, which are critical for proper trial design and interpretation. Certain ethical aspects of a trial can also be quantified using simulations. Further, after a trial has been conducted, simulations can be used to explore the possible explanations for the observed effects. Much is to be gained through a multidisciplinary approach that builds collaborations among experts in infectious disease dynamics, epidemiology, statistical science, economics, simulation methods, and the conduct of clinical trials.

Twitter Demographics

The data shown below were collected from the profiles of 45 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 46 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 46 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 18 39%
Student > Master 8 17%
Student > Ph. D. Student 5 11%
Professor > Associate Professor 5 11%
Student > Doctoral Student 4 9%
Other 6 13%
Readers by discipline Count As %
Unspecified 11 24%
Medicine and Dentistry 9 20%
Mathematics 9 20%
Veterinary Science and Veterinary Medicine 4 9%
Engineering 3 7%
Other 10 22%

Attention Score in Context

This research output has an Altmetric Attention Score of 25. 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 06 March 2019.
All research outputs
#642,103
of 13,457,898 outputs
Outputs from BMC Medicine
#551
of 2,138 outputs
Outputs of similar age
#29,701
of 385,243 outputs
Outputs of similar age from BMC Medicine
#54
of 212 outputs
Altmetric has tracked 13,457,898 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,138 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 34.9. This one has gotten more attention than average, scoring higher than 74% 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 385,243 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 92% of its contemporaries.
We're also able to compare this research output to 212 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 74% of its contemporaries.