Model & data based prediction of the future dynamics of X. fastidiosa in France

Abstract: The spread of Xylella fastidiosa, bacterium detected in Corsica during 2015, might cause a major sanitary crisis in France. It might also have diverse impacts on the French agricultural production through important direct or indirect costs. In this poster, we will present a short-time approach to predict the future dynamics of emerging plant pathogens such as X. fastidiosa in France. This would be a major advantage for adopting control and surveillance measures at an early stage. We used several spatio-temporal propagation models to estimate epidemiological parameters and make prediction from surveillance data. These models were built by following a mechanistic-statistical approach, which combines (i) a sub-model based on partial differential equations describing the dynamics of the pathogen and (ii) a stochastic sub-model describing the observation process. To draw a unique prediction of the future extent of the pathogen, we used model-averaging as a way to take account of uncertainty about model forms. We will show the results obtained in the case of the expansion of X. fastidiosa in France, but could be applied to other post-emergent epidemics to endorse a fast reaction.Acknowledgment This work has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement N. 727987 "Xylella fastidiosa Active Containment Through a multidisciplinary-Oriented Research Strategy XF-ACTORS" and from INRA-DGAL project 2100067