Inferring pathogen dynamics from temporal count data: the emergence of Xylella fastidiosa in France is probably not recent.

ABSTRACT: Unravelling the ecological structure of emerging plant pathogens persisting in multi-host systems is challenging. In such systems, observations are often heterogeneous with respect to time, space and host species, and may lead to biases of perception. The biased perception of pathogen ecology may be exacerbated by hidden fractions of the whole host population, which may act as infection reservoirs. We designed a mechanistic-statistical approach to help understand the ecology of emerging pathogens by filtering out some biases of perception. This approach, based on SIR (Susceptible-Infected-Removed) models and a Bayesian framework, disentangles epidemiological and observational processes underlying temporal counting data. We applied our approach to French surveillance data on Xylella fastidi

SUBMITTER: Soubeyrand S 

PROVIDER: S-EPMC6032966 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature