Highly Pathogenic Avian Influenza (HPAI) represents major veterinary, public health and societal concerns throughout the world. France coped with three successive epizootics, which put in danger the sustainability of the whole avian production system. Despite major changes in husbandry practices, aiming at reducing the risk of introduction, the latest 2022 outbreak was dramatic with more than 1,375 infected sites. This situation triggers an urgent need for researches for a thorough understanding of the transmission drivers of the virus. The objectives of the thesis project is to unravel the transmission routes involving actors of the production chain, and to provide new insights to stakeholders for decision making. To reach these objectives, the successful candidate will analyse data related to historical outbreaks and develop a mechanistic model of HPAI transmission responding to identified requirements raised by stakeholders.
The project is driven around three complementary axes:

Area 1. Modelling of contact networks
- Analysis of the structural components and nodal characteristics influencing the topology of direct (animal movements) and indirect (rendering plants) contact networks.
- Assess the topological impact of alternative networks
Axis 2. Epidemiological modelling
-Development of a mechanistic model of HPAI virus spread
-Evaluation of surveillance and control measures
-Estimation of transmission parameters and evaluation of the relative role of transmission routes
Axis 3. Integration of socio-economic factors in epidemiological modelling
-Consultation of stakeholders and managers on current prevention and control practices as well as on the feasibility of alternative prevention and control scenarios and the associated technical and socio-economic constraints (in support of axis 2)
- Identification of the socio-economic factors to be integrated into the modelling resulting from axis 2 considered relevant

Methodological and technical approaches considered

            - Social network analysis (SNA, ERGM)
            - Mecanistic epidemiological modelling.
            - Parameter inference (Appoximate Bayesian Computation, MCMC, Deep learning)
            - Participatory approach to understand the technical and socio-economic constraints