The Kendrick modelling platform: language abstractions and tools for epidemiology

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Abstract

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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Most cited references 21

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Antibiotic resistance-the need for global solutions.

Ramanan Laxminarayan, Adriano Duse, Chand Wattal … (2013)

The causes of antibiotic resistance are complex and include human behaviour at many levels of society; the consequences affect everybody in the world. Similarities with climate change are evident. Many efforts have been made to describe the many different facets of antibiotic resistance and the interventions needed to meet the challenge. However, coordinated action is largely absent, especially at the political level, both nationally and internationally. Antibiotics paved the way for unprecedented medical and societal developments, and are today indispensible in all health systems. Achievements in modern medicine, such as major surgery, organ transplantation, treatment of preterm babies, and cancer chemotherapy, which we today take for granted, would not be possible without access to effective treatment for bacterial infections. Within just a few years, we might be faced with dire setbacks, medically, socially, and economically, unless real and unprecedented global coordinated actions are immediately taken. Here, we describe the global situation of antibiotic resistance, its major causes and consequences, and identify key areas in which action is urgently needed. Copyright © 2013 Elsevier Ltd. All rights reserved.

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Changing patterns of infectious disease.

M. Cohen (2000)

Despite a century of often successful prevention and control efforts, infectious diseases remain an important global problem in public health, causing over 13 million deaths each year. Changes in society, technology and the microorganisms themselves are contributing to the emergence of new diseases, the re-emergence of diseases once controlled, and to the development of antimicrobial resistance. Two areas of special concern in the twenty-first century are food-borne disease and antimicrobial resistance. The effective control of infectious diseases in the new millennium will require effective public health infrastructures that will rapidly recognize and respond to them and will prevent emerging problems.

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Imperfect vaccines and the evolution of pathogen virulence.

S Gandon, M J Mackinnon, S Nee … (2001)

Vaccines rarely provide full protection from disease. Nevertheless, partially effective (imperfect) vaccines may be used to protect both individuals and whole populations. We studied the potential impact of different types of imperfect vaccines on the evolution of pathogen virulence (induced host mortality) and the consequences for public health. Here we show that vaccines designed to reduce pathogen growth rate and/or toxicity diminish selection against virulent pathogens. The subsequent evolution leads to higher levels of intrinsic virulence and hence to more severe disease in unvaccinated individuals. This evolution can erode any population-wide benefits such that overall mortality rates are unaffected, or even increase, with the level of vaccination coverage. In contrast, infection-blocking vaccines induce no such effects, and can even select for lower virulence. These findings have policy implications for the development and use of vaccines that are not expected to provide full immunity, such as candidate vaccines for malaria.

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Author and article information

Contributors

Mai Anh BUI T.: anhbtm@soict.hust.edu.vn

Nick Papoulias: npapoylias@gmail.com

Serge Stinckwich: serge.stinckwich@ird.fr

Mikal Ziane: mikal.ziane@lip6.fr

Benjamin Roche: benjamin.roche@ird.fr

Journal

Journal ID (nlm-ta): BMC Bioinformatics

Journal ID (iso-abbrev): BMC Bioinformatics

Title: BMC Bioinformatics

Publisher: BioMed Central (London )

ISSN (Electronic): 1471-2105

Publication date (Electronic): 11 June 2019

Publication date PMC-release: 11 June 2019

Publication date Collection: 2019

Volume: 20

Electronic Location Identifier: 312

Affiliations

[1 ]GRID grid.440792.c, Software Engineering Department, School of Information and Communication Technology, Hanoi University of Science and Technology, ; Hanoi, Vietnam

[2 ]ISNI 0000 0001 2169 7335, GRID grid.11698.37, Université de La Rochelle, UMR 7266 LIENSs, CNRS, ; La Rochelle, France

[3 ]GRID grid.464114.2, Sorbonne Université, IRD, Unité de Modélisation Mathématiques et Informatique des Systèmes Complexes, UMMISCO, F-93143, ; Bondy, France

[4 ]ISNI 0000 0001 2173 8504, GRID grid.412661.6, Université de Yaoundé I, IRD, UMMISCO, ; Yaoundé, Cameroon

[5 ]ISNI 0000 0001 2186 4076, GRID grid.412043.0, Université de Caen Normandie, ; Caen, France

[6 ]ISNI 0000 0001 2171 2558, GRID grid.5842.b, Université de Paris, ; Paris, France

[7 ]ISNI 0000 0001 2112 9282, GRID grid.4444.0, Sorbonne Université, CNRS, Laboratoire d’Informatique de Paris 6, LIP6, F-75005, ; Paris, France

Article

Publisher ID: 2843

DOI: 10.1186/s12859-019-2843-0

PMC ID: 6560906

PubMed ID: 31185887

SO-VID: ed8dc5f6-f1b0-40a3-af5b-5b9ad99670dc

License:

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.

History

Date received : 19 April 2018

Date accepted : 24 April 2019

Funding

Funded by: Hanoi University of Science and Technology

Award ID: T2017-TT-001

Funded by: FundRef http://dx.doi.org/10.13039/501100001665, Agence Nationale de la Recherche;

Award ID: PANIC Project

Funded by: European Smalltalk User Group (ESUG)

Custom metadata

ScienceOpen disciplines: Bioinformatics & Computational biology

Keywords: domain-specific language,modularity,mathematical modelling,epidemiological modelling,compartmental models

Data availability:

ScienceOpen disciplines: Bioinformatics & Computational biology

Keywords: domain-specific language, modularity, mathematical modelling, epidemiological modelling, compartmental models

The Kendrick modelling platform: language abstractions and tools for epidemiology

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Abstract

Background

Results

Conclusions

Electronic supplementary material

Related collections

Genetoberfest

Most cited references 21

Antibiotic resistance-the need for global solutions.

Changing patterns of infectious disease.

Imperfect vaccines and the evolution of pathogen virulence.

Author and article information

Contributors

Journal

Affiliations

Article

History

Funding

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