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Real-time decision-making during emergency disease outbreaks

Overview of attention for article published in PLoS Computational Biology, July 2018
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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 (97th percentile)
  • High Attention Score compared to outputs of the same age and source (95th percentile)

Mentioned by

news
8 news outlets
blogs
2 blogs
twitter
29 tweeters
facebook
1 Facebook page
googleplus
2 Google+ users
reddit
1 Redditor

Citations

dimensions_citation
23 Dimensions

Readers on

mendeley
88 Mendeley
citeulike
1 CiteULike
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Title
Real-time decision-making during emergency disease outbreaks
Published in
PLoS Computational Biology, July 2018
DOI 10.1371/journal.pcbi.1006202
Pubmed ID
Authors

William J. M. Probert, Chris P. Jewell, Marleen Werkman, Christopher J. Fonnesbeck, Yoshitaka Goto, Michael C. Runge, Satoshi Sekiguchi, Katriona Shea, Matt J. Keeling, Matthew J. Ferrari, Michael J. Tildesley

Abstract

In the event of a new infectious disease outbreak, mathematical and simulation models are commonly used to inform policy by evaluating which control strategies will minimize the impact of the epidemic. In the early stages of such outbreaks, substantial parameter uncertainty may limit the ability of models to provide accurate predictions, and policymakers do not have the luxury of waiting for data to alleviate this state of uncertainty. For policymakers, however, it is the selection of the optimal control intervention in the face of uncertainty, rather than accuracy of model predictions, that is the measure of success that counts. We simulate the process of real-time decision-making by fitting an epidemic model to observed, spatially-explicit, infection data at weekly intervals throughout two historical outbreaks of foot-and-mouth disease, UK in 2001 and Miyazaki, Japan in 2010, and compare forward simulations of the impact of switching to an alternative control intervention at the time point in question. These are compared to policy recommendations generated in hindsight using data from the entire outbreak, thereby comparing the best we could have done at the time with the best we could have done in retrospect. Our results show that the control policy that would have been chosen using all the data is also identified from an early stage in an outbreak using only the available data, despite high variability in projections of epidemic size. Critically, we find that it is an improved understanding of the locations of infected farms, rather than improved estimates of transmission parameters, that drives improved prediction of the relative performance of control interventions. However, the ability to estimate undetected infectious premises is a function of uncertainty in the transmission parameters. Here, we demonstrate the need for both real-time model fitting and generating projections to evaluate alternative control interventions throughout an outbreak. Our results highlight the use of using models at outbreak onset to inform policy and the importance of state-dependent interventions that adapt in response to additional information throughout an outbreak.

Twitter Demographics

The data shown below were collected from the profiles of 29 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 88 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 88 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 23 26%
Researcher 21 24%
Student > Doctoral Student 8 9%
Student > Master 8 9%
Student > Bachelor 6 7%
Other 10 11%
Unknown 12 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 21 24%
Medicine and Dentistry 9 10%
Mathematics 8 9%
Veterinary Science and Veterinary Medicine 7 8%
Computer Science 5 6%
Other 14 16%
Unknown 24 27%

Attention Score in Context

This research output has an Altmetric Attention Score of 90. 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 18 September 2018.
All research outputs
#292,277
of 18,049,313 outputs
Outputs from PLoS Computational Biology
#256
of 6,906 outputs
Outputs of similar age
#8,530
of 287,234 outputs
Outputs of similar age from PLoS Computational Biology
#7
of 135 outputs
Altmetric has tracked 18,049,313 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 6,906 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.5. This one has done particularly well, scoring higher than 96% 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 287,234 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 97% of its contemporaries.
We're also able to compare this research output to 135 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 95% of its contemporaries.