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87 results on '"Serge Stinckwich"'

2. A call for citizen science in pandemic preparedness and response: beyond data collection

Author

Rebecca Katz, Anurag Agrawal, Gershim Asiki, Andrea Sylvia Winkler, Malebona Precious Matsoso, Rene English, Jeffrey Victor Lazarus, Yi-Roe Tan, Peiling Yap, Olivia Keiser, Ayman El-Mohandes, Marc Choisy, Ashish Joshi, Sara L M Davis, Annalena Huber, Bruce Mellado, Caroline Antonia Mubaira, Felipe C Canlas, Harjyot Khosa, Mariana Recamonde-Mendoza, Patrick Okwen, Serge Stinckwich, Sylvia Kiwuwa-Muyingo, Tariro Kutadza, Tavpritesh Sethi, Thuso Mathaha, Vinh Kim Nguyen, and Amandeep Gill

Subjects
Medicine (General), R5-920, Infectious and parasitic diseases, RC109-216
Published
2022
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3. The Kendrick modelling platform: language abstractions and tools for epidemiology

Author

Mai Anh BUI T., Nick Papoulias, Serge Stinckwich, Mikal Ziane, and Benjamin Roche

Subjects
Domain-specific language, Modularity, Mathematical modelling, Epidemiological modelling, Compartmental models, Computer applications to medicine. Medical informatics, R858-859.7, Biology (General), QH301-705.5
Abstract

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.

Published
2019
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4. Correction to: The Kendrick modelling platform: language abstractions and tools for epidemiology

Author

Mai Anh BUI T, Nick Papoulias, Serge Stinckwich, Mikal Ziane, and Benjamin Roche

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Biology (General), QH301-705.5
Abstract

Following publication of the original article [1], the author noticed that the following lines were missing from the published article. The original article has been corrected.

Published
2019
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7. A Hitchhiker's Guide to Model-Driven Engineering for Data-Centric Systems.

Author

Benoît Combemale, Jörg Kienzle, Gunter Mussbacher, Hyacinth Ali, Daniel Amyot, Mojtaba Bagherzadeh, Edouard Batot, Nelly Bencomo, Benjamin Benni, Jean-Michel Bruel, Jordi Cabot, Betty H. C. Cheng, Philippe Collet, Gregor Engels, Robert Heinrich, Jean-Marc Jézéquel, Anne Koziolek, Sébastien Mosser 0001, Ralf H. Reussner, Houari A. Sahraoui, Rijul Saini, June Sallou, Serge Stinckwich, Eugene Syriani, and Manuel Wimmer

Published
2021
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34. A call for citizen science in pandemic preparedness and response: beyond data collection

Author

Yi-Roe Tan, Anurag Agrawal, Malebona Precious Matsoso, Rebecca Katz, Sara L M Davis, Andrea Sylvia Winkler, Annalena Huber, Ashish Joshi, Ayman El-Mohandes, Bruce Mellado, Caroline Antonia Mubaira, Felipe C Canlas, Gershim Asiki, Harjyot Khosa, Jeffrey Victor Lazarus, Marc Choisy, Mariana Recamonde-Mendoza, Olivia Keiser, Patrick Okwen, Rene English, Serge Stinckwich, Sylvia Kiwuwa-Muyingo, Tariro Kutadza, Tavpritesh Sethi, Thuso Mathaha, Vinh Kim Nguyen, Amandeep Gill, and Peiling Yap

Subjects
Analysis, COVID-19, health systems, public health, infections, diseases, disorders, injuries, Citizen Science, Health Policy, Data Collection, Public Health, Environmental and Occupational Health, Community Participation, Humans, Pandemics, ddc
Abstract

The COVID-19 pandemic has underlined the need to partner with the community in pandemic preparedness and response in order to enable trust-building among stakeholders, which is key in pandemic management. Citizen science, defined here as a practice of public participation and collaboration in all aspects of scientific research to increase knowledge and build trust with governments and researchers, is a crucial approach to promoting community engagement. By harnessing the potential of digitally enabled citizen science, one could translate data into accessible, comprehensible and actionable outputs at the population level. The application of citizen science in health has grown over the years, but most of these approaches remain at the level of participatory data collection. This narrative review examines citizen science approaches in participatory data generation, modelling and visualisation, and calls for truly participatory and co-creation approaches across all domains of pandemic preparedness and response. Further research is needed to identify approaches that optimally generate short-term and long-term value for communities participating in population health. Feasible, sustainable and contextualised citizen science approaches that meaningfully engage affected communities for the long-term will need to be inclusive of all populations and their cultures, comprehensive of all domains, digitally enabled and viewed as a key component to allow trust-building among the stakeholders. The impact of COVID-19 on people’s lives has created an opportune time to advance people’s agency in science, particularly in pandemic preparedness and response.

Published
2022

44. A Hitchhiker's Guide to Model-Driven Engineering for Data-Centric Systems

Author

Mojtaba Bagherzadeh, Jordi Cabot, Jörg Kienzle, Betty H. C. Cheng, Gunter Mussbacher, Manuel Wimmer, Rijul Saini, Ralf Reussner, Benoit Combemale, Benjamin Benni, Daniel Amyot, Robert Heinrich, Edouard Batot, Gregor Engels, Jean-Michel Bruel, Nelly Bencomo, Houari Sahraoui, June Sallou, Hyacinth Ali, Jean-Marc Jézéquel, Anne Koziolek, Eugene Syriani, Philippe Collet, Sébastien Mosser, Serge Stinckwich, Diversity-centric Software Engineering (DiverSe), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-LANGAGE ET GÉNIE LOGICIEL (IRISA-D4), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Smart Modeling for softw@re Research and Technology (IRIT-SM@RT), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Toulouse Mind & Brain Institut (TMBI), Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT), McGill University = Université McGill [Montréal, Canada], University of Ottawa [Ottawa], Queen's University [Kingston, Canada], Département d'Informatique et de Recherche Opérationnelle [Montreal] (DIRO), Université de Montréal (UdeM), Aston University [Birmingham], COMUE Université Côte d'Azur (2015-2019) (COMUE UCA), Institució Catalana de Recerca i Estudis Avançats (ICREA), Michigan State University [East Lansing], Michigan State University System, Laboratoire d'Informatique, Signaux, et Systèmes de Sophia-Antipolis (I3S) / Equipe MODALIS, Scalable and Pervasive softwARe and Knowledge Systems (Laboratoire I3S - SPARKS), Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA), University of Paderborn, Karlsruhe Institute of Technology (KIT), Université du Québec à Montréal = University of Québec in Montréal (UQAM), United Nations University (UNU), Johannes Kepler Universität Linz - Johannes Kepler University Linz [Autriche] (JKU), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées, Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (... - 2019) (UNS), Johannes Kepler University Linz [Linz] (JKU), Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), and Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1)

Subjects
Sociotechnical system, Computer science, business.industry, Group method of data handling, DATA processing & computer science, Software development, 020207 software engineering, 02 engineering and technology, [INFO.INFO-SE]Computer Science [cs]/Software Engineering [cs.SE], Database-centric architecture, Data modeling, Software, Unified Modeling Language, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, ddc:004, Model-driven architecture, business, Software engineering, computer, ComputingMilieux_MISCELLANEOUS, computer.programming_language
Abstract

The models and data framework demystifies the different roles that models and data play in software development and operation and clarifies where machine learning and artificial intelligence techniques could be used.

Published
2021

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