PESO: a modelling framework to help improve management strategies for epidemics – application to sharka

Abstract

BGPI: équipe 6Paper based on work presented at the Joint EFSA-EPPO Workshop: Modelling in Plant Health – how can models support risk assessment of plant pests and decision-making? 12th–14th December 2016, Parma, ItalyThe optimization of management strategies for plant diseases is a difficult task because of the complexity and variability of epidemic dynamics. Thanks to their ability to numerically simulate many scenarios, models can be used to estimate epidemiological parameters, assess the effectiveness of different management strategies and optimize them. This article presents the PESO (parameter estimation–simulation–optimization) modelling framework to help improve plant disease management strategies. This framework is based on (i) the characterization of the epidemic dynamics to estimate key epidemiological parameters, (ii) the use of spatially explicit models to simulate epidemic dynamics and disease management, and (iii) the use of numerical optimization methods to identify better management strategies. This approach is generic and can be applied to many diseases. The work presented here focuses on sharka (caused by Plum pox virus), which has a worldwide impact on the Prunus industry, and is associated with huge disease management costs in many countries, especially in France

On the possible impacts of a regulatory change on resource conservation: The case of the plumpox virus in France

“…[7] . Control measures consist in the identification of affected trees (first visually, see Figure 4, then with possible confirmation by sampling and laboratory testing) and their uprooting [8] . In 2020, 32,000 trees were found to be infected with sharka in the French region of Occitanie [7] .…”

Modelling interference between vectors of non-persistently transmitted plant viruses to identify effective control strategies

“…As far as we know, the effect of the interference between resident and transient aphids on the spread of NPT viruses has never been explored. Understanding NPT viruses spread is complex because experimentation is costly and difficult: symptoms may be difficult to detect and experimental trials in the vicinity of susceptible commercial crops may be restricted [ 21 , 22 ]. Mathematical models are thus particularly useful to provide complementary insights on virus spread [ 23 ], and to design and test management strategies, while circumventing the difficulties associated with experiments ( e .…”

Modelling interference between vectors of non-persistently transmitted plant viruses to identify effective control strategies

“…Understanding NPT viruses spread is complex because experimentation is costly and difficult: symptoms may be difficult to detect and experimental trials in the vicinity of susceptible commercial crops may be restricted (Cunniffe et al, 2014;Picard et al, 2017). Mathematical models are thus particularly useful to provide complementary insights on virus spread (Jeger, 2020) 2017) developed a model to assess the contributions of vector life history traits (e.g.…”

“…As far as we know, the effect of the interference between resident and transient aphids on the spread of NPT viruses has never been explored. Understanding NPT viruses spread is complex because experimentation is costly and difficult: symptoms may be difficult to detect and experimental trials in the vicinity of susceptible commercial crops may be restricted [21,22]. Mathematical models are thus particularly useful to provide complementary insights on virus spread [23], and to design and test management strategies, while circumventing the difficulties associated with experiments (e.g.…”