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Decision-making for foot-and-mouth disease control: Objectives matter

Overview of attention for article published in Epidemics : The Journal of Infectious Disease Dynamics, December 2015
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  • Good Attention Score compared to outputs of the same age (73rd percentile)

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Citations

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Title
Decision-making for foot-and-mouth disease control: Objectives matter
Published in
Epidemics : The Journal of Infectious Disease Dynamics, December 2015
DOI 10.1016/j.epidem.2015.11.002
Pubmed ID
Authors

William J.M. Probert, Katriona Shea, Christopher J. Fonnesbeck, Michael C. Runge, Tim E. Carpenter, Salome Dürr, M. Graeme Garner, Neil Harvey, Mark A. Stevenson, Colleen T. Webb, Marleen Werkman, Michael J. Tildesley, Matthew J. Ferrari

Abstract

Formal decision-analytic methods can be used to frame disease control problems, the first step of which is to define a clear and specific objective. We demonstrate the imperative of framing clearly-defined management objectives in finding optimal control actions for control of disease outbreaks. We illustrate an analysis that can be applied rapidly at the start of an outbreak when there are multiple stakeholders involved with potentially multiple objectives, and when there are also multiple disease models upon which to compare control actions. The output of our analysis frames subsequent discourse between policy-makers, modellers and other stakeholders, by highlighting areas of discord among different management objectives and also among different models used in the analysis. We illustrate this approach in the context of a hypothetical foot-and-mouth disease (FMD) outbreak in Cumbria, UK using outputs from five rigorously-studied simulation models of FMD spread. We present both relative rankings and relative performance of controls within each model and across a range of objectives. Results illustrate how control actions change across both the base metric used to measure management success and across the statistic used to rank control actions according to said metric. This work represents a first step towards reconciling the extensive modelling work on disease control problems with frameworks for structured decision making.

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X Demographics

The data shown below were collected from the profiles of 9 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 151 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 1%
United States 2 1%
Switzerland 1 <1%
Unknown 146 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 32 21%
Student > Ph. D. Student 26 17%
Student > Master 24 16%
Student > Bachelor 10 7%
Student > Doctoral Student 9 6%
Other 31 21%
Unknown 19 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 37 25%
Veterinary Science and Veterinary Medicine 25 17%
Mathematics 12 8%
Medicine and Dentistry 11 7%
Environmental Science 6 4%
Other 26 17%
Unknown 34 23%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 23 May 2022.
All research outputs
#7,212,870
of 25,394,764 outputs
Outputs from Epidemics : The Journal of Infectious Disease Dynamics
#244
of 515 outputs
Outputs of similar age
#103,419
of 394,999 outputs
Outputs of similar age from Epidemics : The Journal of Infectious Disease Dynamics
#4
of 5 outputs
Altmetric has tracked 25,394,764 research outputs across all sources so far. This one has received more attention than most of these and is in the 71st percentile.
So far Altmetric has tracked 515 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 18.1. This one has gotten more attention than average, scoring higher than 52% 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 394,999 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 73% of its contemporaries.
We're also able to compare this research output to 5 others from the same source and published within six weeks on either side of this one.