1. Information¶
1.1. Contributors and contact¶
Sébastien Picault (sebastien.picault@inrae.fr)
Yu-Lin Huang
Sandie Arnoux
Vianney Sicard
Gaël Beaunée
Pauline Ezanno (pauline.ezanno@inrae.fr)
Oniris, INRAE, BIOEPAR, 44300 Nantes, France
1.2. How to cite¶
If you are using EMULSION in your work, please acknowledge it when publishing, with a reference to the following article:
Main reference
S. Picault, Y.-L. Huang, V. Sicard, S. Arnoux, G. Beaunée, P. Ezanno (2019). “EMULSION: Transparent and flexible multiscale stochastic models in human, animal and plant epidemiology”, PLoS Computational Biology 15(9): e1007342.
DOI: 10.1371/journal.pcbi.1007342@Article{EMULSION-ploscb2019,
author = {Picault, Sébastien and Huang, Yu-Lin and Sicard,
Vianney and Arnoux, Sandie and Beaunée, Gaël and
Ezanno, Pauline},
title = {{EMULSION}: Transparent and flexible multiscale
stochastic models in human, animal and plant
epidemiology},
journal = {{PLoS} {C}omputational {B}iology},
year = {2019},
volume = {15},
number = {9},
pages = {e1007342},
DOI = {10.1371/journal.pcbi.1007342},
url = {https://doi.org/10.1371/journal.pcbi.1007342}
}
1.3. Educational Resources¶
On-line doctoral training on EMULSION, providing an online virtual machine through Binder
1.4. Selected publications¶
On bovine respiratory disease (BRD) in fattening farms:
S. Picault, P. Ezanno, K. Smith, D. Amrine, B. White, S. Assié (2022). “Modelling the effects of antimicrobial metaphylaxis and pen size on bovine respiratory disease in high and low risk fattening cattle”, Veterinary Research 53:77,
S. Picault, P. Ezanno, S. Assié (2019). “Combining early hyperthermia detection with metaphylaxis for reducing antibiotics usage in newly received beef bulls at fattening operations: a simulation-based approach”, Proceedings of the Conference of the Society for Veterinary Epidemiology and Preventive Medicine (SVEPM), p. 148-159
On Swine influenza virus in batch-rearing pig farms:
V. Sicard, M. Andraud, S. Picault (2022). “Coupling spatial and temporal structure in batch rearing modeling for understanding the spread of the swine influenza A virus”, Proceedings of the Conference of the Society for Veterinary Epidemiology and Preventive Medicine (SVEPM)
On the generation of synthetic epidemiological data for the first modelling challenge in animal health (ASF Challenge):
S. Picault, T. Vergne, M. Mancini, S. Bareille, P. Ezanno (2022). “The African swine fever modelling challenge: Objectives, model description and synthetic data generation”, Epidemics 40
On Q fever (Coxiellosis) in dairy herds:
S. Picault, Y.-L. Huang, V. Sicard, F. Beaudeau, P. Ezanno (2017). “A Multi-Level Multi-Agent Simulation Framework in Animal Epidemiology”, in: Demazeau et al. (eds.), 15th International Conference on Practical Applications of Agents and Multi-Agent Systems (PAAMS), Lecture Notes in Computer Science 10349, p. 209-221, Springer.
On Artificial Intelligence research involved in EMULSION:
V. Sicard, M. Andraud, S. Picault (2022). “A declarative modelling language for the design of complex structured agent-based epidemiological models”, Proceedings of the 20th International Conference on Practical Applications of Agents and Multi-Agent Systems (PAAMS), Springer.
V. Sicard, M. Andraud, S. Picault (2021). “Organization as a multi-level design pattern for agent-based simulation of complex systems”, Proceedings of the 13th International Conference on Agents and Artificial Intelligence (ICAART’2021), p. 232-241, SCITEPRESS
P. Mathieu, G. Morvan, S. Picault (2018). “Multi-level agent-based simulations: Four design patterns”, Simulation Modelling Practice and Theory 83, p. 51-64, Agent-Based Modelling and Simulation special issue Elsevier.
S. Picault, Y.-L. Huang, V. Sicard, P. Ezanno (2017). “Enhancing Sustainability of Complex Epidemiological Models through a Generic Multilevel Agent-based Approach” in: C. Sierra (ed.), 26th International Joint Conference on Artificial Intelligence (IJCAI), AAAI, p. 374-380.
S. Picault, P. Mathieu (2011). “An Interaction-Oriented Model for Multi-Scale Simulation”, in: T. Walsh (ed.), 22nd International Joint Conference on Artificial Intelligence (IJCAI), AAAI, p. 332-337.
1.5. Funding¶
This work was carried out with the financial support of:
INRAE, the French National Research Institute for Agriculture, Food and Environment:
Animal Health Division
Partnerships and Innovation Transfer Division (“DPTI”)
the European Union:
the European fund for the regional development (FEDER) of Pays de la Loire
project H2020 DECIDE (101000494)
the French Research Agency (ANR):
Program Investments for the Future, project ANR-10-BINF-0007 (MIHMES)
project ANR-16-CE32-0007-01 (CADENCE)
the French Région Pays de la Loire:
PULSAR grant
CAENOME Ph.D. grant
the Carnot Institute “France Futur Élevage” (SEPTIME project)
the For and On Regional Development (PSDR) French research programme, funded by INRAE and the French regions of Bretagne, Pays de la Loire, Normandie and Nouvelle Aquitaine (SANT’Innov)