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The Kendrick modelling platform: language abstractions and tools for epidemiology

BACKGROUND: Mathematical and computational models are widely used to study the transmission, pathogenicity, and propagation of infectious diseases. Unfortunately, complex mathematical models are difficult to define, reuse and reproduce because they are composed of several concerns that are intertwin...

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Autores principales: BUI T., Mai Anh, Papoulias, Nick, Stinckwich, Serge, Ziane, Mikal, Roche, Benjamin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6560906/
https://www.ncbi.nlm.nih.gov/pubmed/31185887
http://dx.doi.org/10.1186/s12859-019-2843-0
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author BUI T., Mai Anh
Papoulias, Nick
Stinckwich, Serge
Ziane, Mikal
Roche, Benjamin
author_facet BUI T., Mai Anh
Papoulias, Nick
Stinckwich, Serge
Ziane, Mikal
Roche, Benjamin
author_sort BUI T., Mai Anh
collection PubMed
description BACKGROUND: Mathematical and computational models are widely used to study the transmission, pathogenicity, and propagation of infectious diseases. Unfortunately, complex mathematical models are difficult to define, reuse and reproduce because they are composed of several concerns that are intertwined. The problem is even worse for computational models because the epidemiological concerns are also intertwined with low-level implementation details that are not easily accessible to non-computing scientists. Our goal is to make compartmental epidemiological models easier to define, reuse and reproduce by facilitating implementation of different simulation approaches with only very little programming knowledge. RESULTS: We achieve our goal through the definition of a domain-specific language (DSL), Kendrick, that relies on a very general mathematical definition of epidemiological concerns as stochastic automata that are combined using tensor-algebra operators. A very large class of epidemiological concerns, including multi-species, spatial concerns, control policies, sex or age structures, are supported and can be defined independently of each other and combined into models to be simulated by different methods. Implementing models does not require sophisticated programming skills any more. The various concerns involved within a model can be changed independently of the others as well as reused within other models. They are not plagued by low-level implementation details. CONCLUSIONS: Kendrick is one of the few DSLs for epidemiological modelling that does not burden its users with implementation details or required sophisticated programming skills. It is also currently the only language for epidemiology modelling that supports modularity through clear separation of concerns hence fostering reproducibility and reuse of models and simulations. Future work includes extending Kendrick to support non-compartmental models and improving its interoperability with existing complementary tools. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12859-019-2843-0) contains supplementary material, which is available to authorized users.
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spelling pubmed-65609062019-06-14 The Kendrick modelling platform: language abstractions and tools for epidemiology BUI T., Mai Anh Papoulias, Nick Stinckwich, Serge Ziane, Mikal Roche, Benjamin BMC Bioinformatics Software BACKGROUND: Mathematical and computational models are widely used to study the transmission, pathogenicity, and propagation of infectious diseases. Unfortunately, complex mathematical models are difficult to define, reuse and reproduce because they are composed of several concerns that are intertwined. The problem is even worse for computational models because the epidemiological concerns are also intertwined with low-level implementation details that are not easily accessible to non-computing scientists. Our goal is to make compartmental epidemiological models easier to define, reuse and reproduce by facilitating implementation of different simulation approaches with only very little programming knowledge. RESULTS: We achieve our goal through the definition of a domain-specific language (DSL), Kendrick, that relies on a very general mathematical definition of epidemiological concerns as stochastic automata that are combined using tensor-algebra operators. A very large class of epidemiological concerns, including multi-species, spatial concerns, control policies, sex or age structures, are supported and can be defined independently of each other and combined into models to be simulated by different methods. Implementing models does not require sophisticated programming skills any more. The various concerns involved within a model can be changed independently of the others as well as reused within other models. They are not plagued by low-level implementation details. CONCLUSIONS: Kendrick is one of the few DSLs for epidemiological modelling that does not burden its users with implementation details or required sophisticated programming skills. It is also currently the only language for epidemiology modelling that supports modularity through clear separation of concerns hence fostering reproducibility and reuse of models and simulations. Future work includes extending Kendrick to support non-compartmental models and improving its interoperability with existing complementary tools. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12859-019-2843-0) contains supplementary material, which is available to authorized users. BioMed Central 2019-06-11 /pmc/articles/PMC6560906/ /pubmed/31185887 http://dx.doi.org/10.1186/s12859-019-2843-0 Text en © The Author(s) 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Software
BUI T., Mai Anh
Papoulias, Nick
Stinckwich, Serge
Ziane, Mikal
Roche, Benjamin
The Kendrick modelling platform: language abstractions and tools for epidemiology
title The Kendrick modelling platform: language abstractions and tools for epidemiology
title_full The Kendrick modelling platform: language abstractions and tools for epidemiology
title_fullStr The Kendrick modelling platform: language abstractions and tools for epidemiology
title_full_unstemmed The Kendrick modelling platform: language abstractions and tools for epidemiology
title_short The Kendrick modelling platform: language abstractions and tools for epidemiology
title_sort kendrick modelling platform: language abstractions and tools for epidemiology
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6560906/
https://www.ncbi.nlm.nih.gov/pubmed/31185887
http://dx.doi.org/10.1186/s12859-019-2843-0
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