Modeling the Effect of Vaccination on Varicella using Bio-PEPA

Hamami, Dalila; Atmani, Baghdad

doi:10.2316/p.2012.783-077

Cited by 4 publications

(11 citation statements)

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“…For implementing our approach, we resumed work that we had already started in [21] which was to reproduce the spread and vaccination protocol of chickenpox in Bio-PEPA, as shown in Figure 2. The overall scheme of our approach is defined by three main steps:…”

Section: Methodsmentioning

confidence: 99%

“…An extensive literature search, which focused on methods of modeling with both, analytical and decision support tools, as well as the translation of the model in other specific formats, for approaching the narrative language, we were able to highlight Bio-PEPA [2,21], which is formal language based on the process algebras recommended for biochemical systems and which was perfectly suited to epidemiological field. Beyond this definition, Bio-PEPA is equipped with an extension that allows translating any model in Bio-PEPA in XML format better known as SBML (Systems Biology Markup Language).…”

Section: From Model To a Narrative Languagementioning

confidence: 99%

“…As defined by [24,21] Where, S: describe the species (different types of individuals); P: the model describing the system and interaction between species. The term (α, k) op S, express that the action α is described by k rate and performed by the species S, "op" define the role of S. Op={ ↓: reactant, ↑: product, ⊕ : activator, ⊖ : inhibitor, ⊙ : generic modifier}.…”

Section: Bio-pepa Syntaxmentioning

confidence: 99%

“…Our approach, as it was structured, allows us to share our work in two main stages, the first is to develop a model with Bio-PEPA (Formulation from epidemic model in Bio-PEPA), a work that has already been done [21] and demonstrated the importance of using such a tool. The second part (Exporting SBML file from Bio-PEPA, Representation of SBML text in narrative language) is developing a module that would translate the Bio-PEPA code in a language understood by the expert, who may easily check whether the contents of the model is adequate to the example and thus validate it.…”

Section: Description Of Model Structurementioning

confidence: 99%

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From Simulated Model By Bio-Pepa to Narrative Language Through SBML

Hamami¹,

Atmani²

2014

IJCTCM

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Many theoretical works and tools on epidemiological field reflect the emphasis on decision-making tools by both public health and the scientific community, which continues to increase.Indeed, in the epidemiological field, modeling tools are proving a very important way in helping to make decision. However, the variety, the large volume of data and the nature of epidemics lead us to seek solutions to alleviate the heavy burden imposed on both experts and developers. Among the important steps of modeling and simulation: model validation. It refers to the process of determining how well a model corresponds to the system that it intended to represent. So the question is: what happens if the model is invalid? Do we need to reproduce another one, or just optimize the existing one?In this paper, we present a new approach consisting on the passage of an epidemic model realized in Bio-PEPA to a narrative language using the basics of SBML language. Our goal is to allow on one hand, epidemiologists to verify and validate the model, and the other hand, developers to optimize the model in order to achieve a better model of decision making. We also present some preliminary results and some suggestions to improve the simulated model.

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Section: Methodsmentioning

confidence: 99%

Section: From Model To a Narrative Languagementioning

confidence: 99%

Section: Bio-pepa Syntaxmentioning

confidence: 99%

Section: Description Of Model Structurementioning

confidence: 99%

See 2 more Smart Citations

From Simulated Model By Bio-Pepa to Narrative Language Through SBML

Hamami¹,

Atmani²

2014

IJCTCM

Self Cite

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show abstract

“…An extensive literature search, which focused on methods of modeling with both, analytical and decision support tools, as well as the translation of the model in other specific formats, for approaching the narrative language, we were able to highlight Bio-PEPA [2,6], which is formal language based on the process algebras recommended for biochemical systems and which was perfectly suited to epidemiological field. Beyond this definition, Bio-PEPA is equipped with an extension that allows translating any model in Bio-PEPA in XML format better known as SBML (Systems Biology Markup Language).…”

Section: From Model To a Narrative Languagementioning

confidence: 99%

SBML for Optimizing Decision Supports Tools

Hamami¹,

Atmani²

2013

Computer Science &Amp; Information Technology ( CS &Amp; IT )

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Improving process algebra model structure and parameters in infectious disease epidemiology through data mining

et al. 2017

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Computational models are increasingly used to assist decision-making in public health epidemiology, but achieving the best model is a complex task due to the interaction of many components and variability of parameter values causing radically di↵erent dynamics. The modelling process can be enhanced through the use of data mining techniques. Here, we demonstrate this by applying association rules and clustering techniques to two stages of modelling: identifying pertinent structures in the initial model creation stage, and choosing optimal parameters to match that model to observed data. This is illustrated through application to the study of the circulating mumps virus in Scotland, 2004-2015. Keywords epidemiological modeling • mumps infection • process algebras • Bio-PEPA formalism • data mining • association rules • clustering • time series.

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Modeling the Effect of Vaccination on Varicella using Bio-PEPA

Cited by 4 publications

References 0 publications

From Simulated Model By Bio-Pepa to Narrative Language Through SBML

From Simulated Model By Bio-Pepa to Narrative Language Through SBML

SBML for Optimizing Decision Supports Tools

Improving process algebra model structure and parameters in infectious disease epidemiology through data mining

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