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Presentation

Vianney Sicard is a computer engineer graduated from the École Polytechnique of the University of Tours. He arrived in the UMR BIOEPAR in September 2015.
He first contributed as a study engineer on the declination of research models into decision support tools (EvalBVD and EvalParaTuB). He then worked on the IVAN (Innovative Veterinary Assisted Necropsy) project for the development of a veterinary autopsy assistance tool based on Bayesian networks.

He is currently preparing a thesis on the coupling between agent, environment and levels of organization in mechanistic models in epidemiology, with an application to porcine reproductive and respiratory syndrome virus (PRRSV)

Activities

• Software development
• Simulation framework
• Setting up consortium
• Bayesian network

Thesis subject: 

The reduction of health risks, at animal, farm and territorial level, is essential for the profitability of livestock farms and production chains. Porcine respiratory and dysgenic syndrome is a recent viral contagious disease (1991 in France), extremely widespread in areas with dense pig populations (nearly 60% prevalence in Brittany), with a threat of emergence of even more virulent strains from Eastern Europe. In order to understand and predict pathogen transmission and to compare the efficacy over time of realistic control strategies, mechanistic epidemiological modelling is essential to complement the expertise of health managers and to quantify the epidemiological and economic impacts.

The aim of the thesis is twofold. In Artificial Intelligence, the objective is to develop generic methods to specify, via a domain-specific language (DSL), the relationships between agents, environments and organization levels in a multi-agent, multi-level simulation architecture. In epidemiological modelling, the objective is to assess the impact of complex farming practices on the spread of pathogens and to facilitate modelling via DSL. The model developed for the DSL will make it possible to assess the impact of the complex spatio-temporal organization induced by batch management and to identify action levers and promising control scenarios. The proposed computer solutions will be designed with a view to extending the epidemiological models to a larger scale (inter-herd) or a smaller scale (intra-host) where questions of interest in epidemiology arise.

Links to other websites

researchgate

linkedin

Publications

• Picault, S., Huang, Y. L., Sicard, V., Hoch, T., Vergu, E., Beaudeau, F., and Ezanno. A Generic Multi-Level Modelling and Simulation Approach in Computational pidemiology. Soumis et en révision à BMC Bioinformatics.

• Picault S., Huang Y.-L., Sicard V., Arnoux S., Beaunée G., Ezanno P. 2019. EMULSION: transparent and flexible multiscale stochastic models in epidemiology. PLoS Computational Biology, 15(9):e1007342 [IF19=4.428] DOI: 10.1371/journal.pcbi.1007342.

• Ezanno P., Beaunée G., Picault S., Arnoux S., Sicard V., Beaudeau F., Rault A., Vergu E. 2018. Gestion des maladies endémiques du troupeau aux territoires : contribution de la modélisation épidémiologique pour soutenir la prise de décision (projet MIHMES, 2012-2017). Innovations Agronomiques, 66:53-65.

• Picault, S., Huang, Y. L., Sicard, V., Beaudeau, F., and Ezanno, P. 2017. A Multi-Level Multi-Agent Simulation Framework in Animal Epidemiology. International Conference on Practical Applications of Agents and Multi-Agent Systems (PAAMS), Porto, Portugal, 2017/06/21-23.

• Picault, S., Huang, Y. L., Sicard, V., and Ezanno, P. 2017. Enhancing Sustainability of Epidemiological Models through a Generic Multilevel Agent-based Approach. International Joint Conference on Artificial Intelligence (IJCAI), Melbourne, Australie, 2017/08/19-25.