Model-assisted epidemiological inference and surveillance for Xylella fastidiosa in France

This communication reviews different results on epidemiological parameters’ inference and risk-based surveillance obtained from the modelling of the Xylella fastidiosa outbreak in France. Firstly, statistical and mechanistic models have been used to infer plausible values for (or the role of) several epidemiological parameters, such as environmental conditions favoring the establishment of the infection, hidden host compartments, the date and location of introduction of the pest in Corse island, as well as host-to-vector and vector-to-host capacities of disease transmission. Secondly, we will show how the aforementioned models can predict the risk of establishment of the disease in previously uncolonized area, and how the resulting risk maps are exploited to design risk-based surveillance strategies. In terms of surveillance, we will also present a newly proposed desktop approach for passive surveillance based on text mining and web-scraping.