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Avoidable errors in the modelling of outbreaks of emerging pathogens, with special reference to Ebola

Overview of attention for article published in Proceedings of the Royal Society B: Biological Sciences, May 2015
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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 (99th percentile)
  • High Attention Score compared to outputs of the same age and source (94th percentile)

Mentioned by

news
3 news outlets
blogs
1 blog
policy
1 policy source
twitter
197 tweeters
wikipedia
1 Wikipedia page
reddit
1 Redditor

Citations

dimensions_citation
92 Dimensions

Readers on

mendeley
239 Mendeley
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Title
Avoidable errors in the modelling of outbreaks of emerging pathogens, with special reference to Ebola
Published in
Proceedings of the Royal Society B: Biological Sciences, May 2015
DOI 10.1098/rspb.2015.0347
Pubmed ID
Authors

Aaron A. King, Matthieu Domenech de Cellès, Felicia M. G. Magpantay, Pejman Rohani

Abstract

As an emergent infectious disease outbreak unfolds, public health response is reliant on information on key epidemiological quantities, such as transmission potential and serial interval. Increasingly, transmission models fit to incidence data are used to estimate these parameters and guide policy. Some widely used modelling practices lead to potentially large errors in parameter estimates and, consequently, errors in model-based forecasts. Even more worryingly, in such situations, confidence in parameter estimates and forecasts can itself be far overestimated, leading to the potential for large errors that mask their own presence. Fortunately, straightforward and computationally inexpensive alternatives exist that avoid these problems. Here, we first use a simulation study to demonstrate potential pitfalls of the standard practice of fitting deterministic models to cumulative incidence data. Next, we demonstrate an alternative based on stochastic models fit to raw data from an early phase of 2014 West Africa Ebola virus disease outbreak. We show not only that bias is thereby reduced, but that uncertainty in estimates and forecasts is better quantified and that, critically, lack of model fit is more readily diagnosed. We conclude with a short list of principles to guide the modelling response to future infectious disease outbreaks.

Twitter Demographics

The data shown below were collected from the profiles of 197 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 239 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 8 3%
United Kingdom 2 <1%
Italy 1 <1%
France 1 <1%
Chile 1 <1%
Brazil 1 <1%
Israel 1 <1%
Switzerland 1 <1%
Portugal 1 <1%
Other 0 0%
Unknown 222 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 64 27%
Student > Ph. D. Student 57 24%
Student > Master 25 10%
Student > Doctoral Student 15 6%
Student > Bachelor 14 6%
Other 47 20%
Unknown 17 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 48 20%
Medicine and Dentistry 40 17%
Mathematics 36 15%
Environmental Science 9 4%
Engineering 8 3%
Other 49 21%
Unknown 49 21%

Attention Score in Context

This research output has an Altmetric Attention Score of 163. 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 22 October 2020.
All research outputs
#125,003
of 16,292,809 outputs
Outputs from Proceedings of the Royal Society B: Biological Sciences
#329
of 8,382 outputs
Outputs of similar age
#2,108
of 310,258 outputs
Outputs of similar age from Proceedings of the Royal Society B: Biological Sciences
#9
of 169 outputs
Altmetric has tracked 16,292,809 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,382 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 33.9. 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 310,258 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 99% of its contemporaries.
We're also able to compare this research output to 169 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 94% of its contemporaries.