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Spatiotemporal dynamics of the Ebola epidemic in Guinea and implications for vaccination and disease elimination: a computational modeling analysis

Overview of attention for article published in BMC Medicine, September 2016
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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 (94th percentile)
  • Good Attention Score compared to outputs of the same age and source (74th percentile)

Mentioned by

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1 news outlet
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49 X users
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1 Facebook page

Citations

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35 Dimensions

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113 Mendeley
Title
Spatiotemporal dynamics of the Ebola epidemic in Guinea and implications for vaccination and disease elimination: a computational modeling analysis
Published in
BMC Medicine, September 2016
DOI 10.1186/s12916-016-0678-3
Pubmed ID
Authors

Marco Ajelli, Stefano Merler, Laura Fumanelli, Ana Pastore y Piontti, Natalie E. Dean, Ira M. Longini, M. Elizabeth Halloran, Alessandro Vespignani

Abstract

Among the three countries most affected by the Ebola virus disease outbreak in 2014-2015, Guinea presents an unusual spatiotemporal epidemic pattern, with several waves and a long tail in the decay of the epidemic incidence. Here, we develop a stochastic agent-based model at the level of a single household that integrates detailed data on Guinean demography, hospitals, Ebola treatment units, contact tracing, and safe burial interventions. The microsimulation-based model is used to assess the effect of each control strategy and the probability of elimination of the epidemic according to different intervention scenarios, including ring vaccination with the recombinant vesicular stomatitis virus-vectored vaccine. The numerical results indicate that the dynamics of the Ebola epidemic in Guinea can be quantitatively explained by the timeline of the implemented interventions. In particular, the early availability of Ebola treatment units and the associated isolation of cases and safe burials helped to limit the number of Ebola cases experienced by Guinea. We provide quantitative evidence of a strong negative correlation between the time series of cases and the number of traced contacts. This result is confirmed by the computational model that suggests that contact tracing effort is a key determinant in the control and elimination of the disease. In data-driven microsimulations, we find that tracing at least 5-10 contacts per case is crucial in preventing epidemic resurgence during the epidemic elimination phase. The computational model is used to provide an analysis of the ring vaccination trial highlighting its potential effect on disease elimination. We identify contact tracing as one of the key determinants of the epidemic's behavior in Guinea, and we show that the early availability of Ebola treatment unit beds helped to limit the number of Ebola cases in Guinea.

X Demographics

X Demographics

The data shown below were collected from the profiles of 49 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 113 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 2%
United States 2 2%
France 1 <1%
Unknown 108 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 19 17%
Student > Master 16 14%
Student > Ph. D. Student 15 13%
Student > Bachelor 10 9%
Student > Doctoral Student 8 7%
Other 21 19%
Unknown 24 21%
Readers by discipline Count As %
Medicine and Dentistry 22 19%
Nursing and Health Professions 13 12%
Mathematics 6 5%
Agricultural and Biological Sciences 6 5%
Social Sciences 5 4%
Other 23 20%
Unknown 38 34%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 38. 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 12 October 2016.
All research outputs
#1,100,953
of 25,732,188 outputs
Outputs from BMC Medicine
#771
of 4,080 outputs
Outputs of similar age
#19,951
of 346,439 outputs
Outputs of similar age from BMC Medicine
#13
of 50 outputs
Altmetric has tracked 25,732,188 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 4,080 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 45.9. This one has done well, scoring higher than 81% 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 346,439 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 94% of its contemporaries.
We're also able to compare this research output to 50 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.