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Adaptive Management and the Value of Information: Learning Via Intervention in Epidemiology

Overview of attention for article published in PLoS Biology, October 2014
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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 (98th percentile)
  • Good Attention Score compared to outputs of the same age and source (77th percentile)

Citations

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43 Dimensions

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151 Mendeley
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Title
Adaptive Management and the Value of Information: Learning Via Intervention in Epidemiology
Published in
PLoS Biology, October 2014
DOI 10.1371/journal.pbio.1001970
Pubmed ID
Authors

Katriona Shea, Michael J. Tildesley, Michael C. Runge, Christopher J. Fonnesbeck, Matthew J. Ferrari

Abstract

Optimal intervention for disease outbreaks is often impeded by severe scientific uncertainty. Adaptive management (AM), long-used in natural resource management, is a structured decision-making approach to solving dynamic problems that accounts for the value of resolving uncertainty via real-time evaluation of alternative models. We propose an AM approach to design and evaluate intervention strategies in epidemiology, using real-time surveillance to resolve model uncertainty as management proceeds, with foot-and-mouth disease (FMD) culling and measles vaccination as case studies. We use simulations of alternative intervention strategies under competing models to quantify the effect of model uncertainty on decision making, in terms of the value of information, and quantify the benefit of adaptive versus static intervention strategies. Culling decisions during the 2001 UK FMD outbreak were contentious due to uncertainty about the spatial scale of transmission. The expected benefit of resolving this uncertainty prior to a new outbreak on a UK-like landscape would be £45-£60 million relative to the strategy that minimizes livestock losses averaged over alternate transmission models. AM during the outbreak would be expected to recover up to £20.1 million of this expected benefit. AM would also recommend a more conservative initial approach (culling of infected premises and dangerous contact farms) than would a fixed strategy (which would additionally require culling of contiguous premises). For optimal targeting of measles vaccination, based on an outbreak in Malawi in 2010, AM allows better distribution of resources across the affected region; its utility depends on uncertainty about both the at-risk population and logistical capacity. When daily vaccination rates are highly constrained, the optimal initial strategy is to conduct a small, quick campaign; a reduction in expected burden of approximately 10,000 cases could result if campaign targets can be updated on the basis of the true susceptible population. Formal incorporation of a policy to update future management actions in response to information gained in the course of an outbreak can change the optimal initial response and result in significant cost savings. AM provides a framework for using multiple models to facilitate public-health decision making and an objective basis for updating management actions in response to improved scientific understanding.

Twitter Demographics

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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 States 8 5%
United Kingdom 2 1%
Germany 1 <1%
France 1 <1%
Vietnam 1 <1%
Unknown 138 91%

Demographic breakdown

Readers by professional status Count As %
Researcher 42 28%
Student > Ph. D. Student 33 22%
Student > Master 20 13%
Unspecified 13 9%
Professor 8 5%
Other 35 23%
Readers by discipline Count As %
Agricultural and Biological Sciences 51 34%
Unspecified 26 17%
Medicine and Dentistry 19 13%
Environmental Science 11 7%
Mathematics 10 7%
Other 34 23%

Attention Score in Context

This research output has an Altmetric Attention Score of 80. 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 19 February 2018.
All research outputs
#189,484
of 12,817,668 outputs
Outputs from PLoS Biology
#573
of 4,049 outputs
Outputs of similar age
#4,035
of 231,420 outputs
Outputs of similar age from PLoS Biology
#16
of 70 outputs
Altmetric has tracked 12,817,668 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 98th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,049 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 47.7. This one has done well, scoring higher than 85% 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 231,420 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 98% of its contemporaries.
We're also able to compare this research output to 70 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 77% of its contemporaries.