Abstract
Identifying the key factors underlying the spread of a disease is an essential but challenging prerequisite to design management strategies. To tackle this issue, we propose an approach based on sensitivity analyses of a spatiotemporal stochastic model simulating the spread of a plant epidemic. This work is motivated by the spread of sharka, caused by plum pox virus, in a real landscape. We first carried out a broad-range sensitivity analysis, ignoring any prior information on six epidemiological parameters, to assess their intrinsic influence on model behaviour. A second analysis benefited from the available knowledge on sharka epidemiology and was thus restricted to more realistic values. The broad-range analysis revealed that the mean duration of the latent period is the most influential parameter of the model, whereas the sharka-specific analysis uncovered the strong impact of the connectivity of the first infected orchard. In addition to demonstrating the interest of sensitivity analyses for a stochastic model, this study highlights the impact of variation ranges of target parameters on the outcome of a sensitivity analysis. With regard to sharka management, our results suggest that sharka surveillance may benefit from paying closer attention to highly connected patches whose infection could trigger serious epidemics.
Highlights
Many factors impact the spread of a plant disease
The identification of the key factors of an epidemic through experiments rapidly becomes intractable, because there can be a huge number of candidate parameters, not counting their interactions
The first aim of this study was to present a rigorous framework for the sensitivity analysis of a stochastic spatially explicit model simulating the spread of a plant disease, and to better understand the influence of different epidemiological parameters on model outputs
Summary
Many factors impact the spread of a plant disease. Such factors include the biology of the different species involved in the pathosystem, the processes governing primary and secondary infections and, generally, landscape structure and composition [1]. Identifying the key factors for disease spread. The identification of the key factors of an epidemic through experiments rapidly becomes intractable, because there can be a huge number of candidate parameters, not counting their interactions. Epidemics must be considered at a large spatiotemporal scale, which is rarely possible in experiments Epidemiological models are an interesting approach because of their ability to test several scenarios, while circumventing the difficulties associated with experiments
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