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A dynamic Bayesian Markov model for health economic evaluations of interventions in infectious disease

Overview of attention for article published in BMC Medical Research Methodology, August 2018
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (77th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (55th percentile)

Mentioned by

blogs
1 blog
policy
1 policy source

Citations

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15 Dimensions

Readers on

mendeley
70 Mendeley
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Title
A dynamic Bayesian Markov model for health economic evaluations of interventions in infectious disease
Published in
BMC Medical Research Methodology, August 2018
DOI 10.1186/s12874-018-0541-7
Pubmed ID
Authors

Katrin Haeussler, Ardo van den Hout, Gianluca Baio

Abstract

Health economic evaluations of interventions in infectious disease are commonly based on the predictions of ordinary differential equation (ODE) systems or Markov models (MMs). Standard MMs are static, whereas ODE systems are usually dynamic and account for herd immunity which is crucial to prevent overestimation of infection prevalence. Complex ODE systems including distributions on model parameters are computationally intensive. Thus, mainly ODE-based models including fixed parameter values are presented in the literature. These do not account for parameter uncertainty. As a consequence, probabilistic sensitivity analysis (PSA), a crucial component of health economic evaluations, cannot be conducted straightforwardly. We present a dynamic MM under a Bayesian framework. We extend a static MM by incorporating the force of infection into the state allocation algorithm. The corresponding output is based on dynamic changes in prevalence and thus accounts for herd immunity. In contrast to deterministic ODE-based models, PSA can be conducted straightforwardly. We introduce a case study of a fictional sexually transmitted infection and compare our dynamic Bayesian MM to a deterministic and a Bayesian ODE system. The models are calibrated to simulated time series data. By means of the case study, we show that our methodology produces outcome which is comparable to the "gold standard" of the Bayesian ODE system. In contrast to ODE systems in the literature, the dynamic MM includes distributions on all model parameters at manageable computational effort (including calibration). The run time of the Bayesian ODE system is 15 times longer.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 70 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 70 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 15 21%
Researcher 10 14%
Student > Bachelor 9 13%
Student > Master 8 11%
Other 4 6%
Other 6 9%
Unknown 18 26%
Readers by discipline Count As %
Medicine and Dentistry 13 19%
Mathematics 5 7%
Nursing and Health Professions 5 7%
Social Sciences 5 7%
Economics, Econometrics and Finance 3 4%
Other 17 24%
Unknown 22 31%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 9. 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 07 May 2020.
All research outputs
#4,274,179
of 25,271,884 outputs
Outputs from BMC Medical Research Methodology
#657
of 2,256 outputs
Outputs of similar age
#75,624
of 337,395 outputs
Outputs of similar age from BMC Medical Research Methodology
#17
of 38 outputs
Altmetric has tracked 25,271,884 research outputs across all sources so far. Compared to these this one has done well and is in the 82nd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,256 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.4. This one has gotten more attention than average, scoring higher than 69% 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 337,395 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 77% of its contemporaries.
We're also able to compare this research output to 38 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.