7. Information

7.1. Contributors and contact

https://img.shields.io/twitter/follow/bioepar_dynamo?label=DYNAMO%20research%20group&style=plastic&logo=twitter

INRAE, Oniris, BIOEPAR, 44300, Nantes, France

7.2. How to cite

Main reference (software paper)

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}
}

7.3. Selected publications

  • On bovine respiratory disease (BRD) in feedlots:

    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”, in: SVEPM Annual Conference and General Meeting (in press)

  • 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.

      DOI: 10.1007/978-3-319-59930-4_17

  • On Artificial Intelligence research involved in EMULSION:

    • 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.

      DOI: 10.1016/j.simpat.2017.12.015

    • 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.

      DOI: 10.24963/ijcai.2017/53

    • 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.

      DOI: 10.5591/978-1-57735-516-8/IJCAI11-065

7.4. Acknowledgements

This work was carried out with the financial support of:

  • the French Research Agency (ANR), Program Investments for the Future, project ANR-10-BINF-07 (MIHMES)
  • the European Union through the European fund for the regional development (FEDER) of Pays-de-la-Loire
  • the French Research Agency (ANR), project ANR-16-CE32-0007-01 (CADENCE)
  • INRAE, the French National Research Institute for Agriculture, Food and Environment (Animal Health Division)
  • the SANT’Innov project of the For and On Regional Development (PSDR) French research programme, which is funded by INRA, the National Research Institute of Science and Technology for Environment and Agriculture (IRSTEA), and the French regions of Bretagne, Pays de la Loire, Normandie and Nouvelle Aquitaine.