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Recalibrating disease parameters for increasing realism in modeling epidemics in closed settings

Overview of attention for article published in BMC Infectious Diseases, November 2016
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  • Above-average Attention Score compared to outputs of the same age (53rd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (55th percentile)

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4 tweeters

Citations

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4 Dimensions

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34 Mendeley
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Title
Recalibrating disease parameters for increasing realism in modeling epidemics in closed settings
Published in
BMC Infectious Diseases, November 2016
DOI 10.1186/s12879-016-2003-3
Pubmed ID
Authors

Livio Bioglio, Mathieu Génois, Christian L. Vestergaard, Chiara Poletto, Alain Barrat, Vittoria Colizza

Abstract

The homogeneous mixing assumption is widely adopted in epidemic modelling for its parsimony and represents the building block of more complex approaches, including very detailed agent-based models. The latter assume homogeneous mixing within schools, workplaces and households, mostly for the lack of detailed information on human contact behaviour within these settings. The recent data availability on high-resolution face-to-face interactions makes it now possible to assess the goodness of this simplified scheme in reproducing relevant aspects of the infection dynamics. We consider empirical contact networks gathered in different contexts, as well as synthetic data obtained through realistic models of contacts in structured populations. We perform stochastic spreading simulations on these contact networks and in populations of the same size under a homogeneous mixing hypothesis. We adjust the epidemiological parameters of the latter in order to fit the prevalence curve of the contact epidemic model. We quantify the agreement by comparing epidemic peak times, peak values, and epidemic sizes. Good approximations of the peak times and peak values are obtained with the homogeneous mixing approach, with a median relative difference smaller than 20 % in all cases investigated. Accuracy in reproducing the peak time depends on the setting under study, while for the peak value it is independent of the setting. Recalibration is found to be linear in the epidemic parameters used in the contact data simulations, showing changes across empirical settings but robustness across groups and population sizes. An adequate rescaling of the epidemiological parameters can yield a good agreement between the epidemic curves obtained with a real contact network and a homogeneous mixing approach in a population of the same size. The use of such recalibrated homogeneous mixing approximations would enhance the accuracy and realism of agent-based simulations and limit the intrinsic biases of the homogeneous mixing.

Twitter Demographics

The data shown below were collected from the profiles of 4 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 34 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 3%
France 1 3%
Belgium 1 3%
Unknown 31 91%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 26%
Student > Doctoral Student 5 15%
Student > Ph. D. Student 4 12%
Other 4 12%
Student > Master 3 9%
Other 8 24%
Unknown 1 3%
Readers by discipline Count As %
Physics and Astronomy 6 18%
Medicine and Dentistry 4 12%
Agricultural and Biological Sciences 3 9%
Computer Science 3 9%
Social Sciences 2 6%
Other 12 35%
Unknown 4 12%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 01 October 2019.
All research outputs
#9,002,192
of 15,949,335 outputs
Outputs from BMC Infectious Diseases
#2,443
of 5,806 outputs
Outputs of similar age
#176,439
of 390,743 outputs
Outputs of similar age from BMC Infectious Diseases
#253
of 582 outputs
Altmetric has tracked 15,949,335 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 5,806 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.9. This one has gotten more attention than average, scoring higher than 57% 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 390,743 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 53% of its contemporaries.
We're also able to compare this research output to 582 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 55% of its contemporaries.