Search

Xylella fastidiosa was in situ detected in France in 2015 (in Corsica and Provence-Alpes-Côte d'Azur) but several studies, grounded on phylogenetic and population-dynamics models, trace the emergence of Xylella in Corsica from the 50's to the 80's (at least with regard to the strains observed since 2015). At this temporal scale (i.e., at the scale of the decades separating the emergence of Xylella and its detection), Xylella and its vector(s) encountered definitively diverse climatic conditions, because of both weather variations and climate change. They especially encountered varying winter temperatures, which are classically used to build proxies of Xylella presence or severity. Combining knowledge from the literature, surveillance data and a parsimonious dynamic model that explicitly takes into account the spatio-temporal variation in winter temperatures, I will (i) present possible past propagation scenarios up to the detection of Xylella, (ii) disentangle the impact of annual variation in winter temperatures in these scenarios, (iii) assess the impact of surveillance and control measures in the contemporary period, and (iii) draw forecasts of the sanitary situation during the next decades grounded on different representative pathways of CO2 concentration. Such an analysis, based on a dynamic vision of the spatial distribution of Xylella over broad temporal horizons, could become an interesting tool to informing the choices and the timing of strategies for eradication, containment, living with, etc.

Fig S1. Mean temperatures during the observation period together with a scatter plot of the mean temperature vs immunity rate. Fig S2. timeline of the spatio-temporal dynamics of the immunity rate during the observation period.

Auxiliary models used for parameter estimation over a moving window of fixed duration.

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.

Since the detection of X. fastidiosa in 2015 in Corsica, France, a rather intense surveillance of infections has been carried out and has generated a total of nearly 18000 samples, 5% of which have been analyzed as positive. Using these data and statistical inference techniques, we have developed different models of the temporal and spatio-temporal dynamics of X. fastidiosa in southeastern France. In this talk, we will present two approaches: the first one is based on a stochastic temporal model allowing us to provide estimates of the introduction date in Corsica and the hidden component of the epidemic; the second one is based on a multi-component system of differential equations allowing us to reconstruct the regional spatio-temporal dynamics of the pathogen and the vectors, and to estimate epidemiological parameters. Both approaches can be used to draw forward projections of the epidemic under various control strategies and, therefore, could be used in the future for decision making., Aknowledgment 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.

The accurate identification of the route of transmission taken by an infectious agent through a host population is critical to understanding its epidemiology and informing measures for its control. However, reconstruction of transmission routes during an epidemic is often an underdetermined problem: data about the location and timings of infections can be incomplete, inaccurate, and compatible with a large number of different transmission scenarios. For fast-evolving pathogens like RNA viruses, inference can be strengthened by using genetic data, nowadays easily and affordably generated. However, significant statistical challenges remain to be overcome in the full integration of these different data types if transmission trees are to be reliably estimated. We present here a framework leading to a bayesian inference scheme that combines genetic and epidemiological data, able to reconstruct most likely transmission patterns and infection dates. After testing our approach with simulated data, we apply the method to two UK epidemics of Foot-and-Mouth Disease Virus (FMDV): the 2007 outbreak, and a subset of the large 2001 epidemic. In the first case, we are able to confirm the role of a specific premise as the link between the two phases of the epidemics, while transmissions more densely clustered in space and time remain harder to resolve. When we consider data collected from the 2001 epidemic during a time of national emergency, our inference scheme robustly infers transmission chains, and uncovers the presence of undetected premises, thus providing a useful tool for epidemiological studies in real time. The generation of genetic data is becoming routine in epidemiological investigations, but the development of analytical tools maximizing the value of these data remains a priority. Our method, while applied here in the context of FMDV, is general and with slight modification can be used in any situation where both spatiotemporal and genetic data are available.

* INRA Documentation, Domaine St Paul, Site Agroparc, 84914 Avignon cedex 9 Diffusion du document : INRA Documentation, Domaine St Paul, Site Agroparc, 84914 Avignon cedex 9; International audience