Improving Management Strategies of Plant Diseases Using Sequential Sensitivity Analyses

Improvement of management strategies of epidemics is often hampered by constraints on experiments at large spatiotemporal scales. A promising approach consists of modeling the biological epidemic process and human interventions, which both impact disease spread. However, few methods enable the simultaneous optimization of the numerous parameters of sophisticated control strategies. To do so, we propose a heuristic approach (i.e., a practical improvement method approximating an optimal solution) based on sequential sensitivity analyses. In addition, we use an economic improvement criterion based on the net present value, accounting for both the cost of the different control measures and the benefit generated by disease suppression. This work is motivated by sharka (caused by Plum pox virus), a vector-borne disease of prunus trees (especially apricot, peach, and plum), the management of which in orchards is mainly based on surveillance and tree removal. We identified the key parameters of a spatiotemporal model simulating sharka spread and control and approximated optimal values for these parameters. The results indicate that the current French management of sharka efficiently controls the disease, but it can be economically improved using alternative strategies that are identified and discussed. The general approach should help policy makers to design sustainable and cost-effective strategies for disease management.

Using sensitivity analysis to identify key factors for the propagation of a plant epidemic

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.

Key determinants of resistance durability to plant viruses: insights from a model linking within- and between-host dynamics

The emergence of new genotypes of parasites involves several evolutionary, epidemiological and ecological processes whose individual effects and interactions are difficult to disentangle using experimental approaches. Here, a model is proposed to investigate how these processes lead to the emergence of plant viral genotypes breaking down qualitative resistance genes. At the individual plant scale, selection, drift and mutation processes shape the evolution of viral populations from a set of differential equations. The spatial segregation of virus genotypes in their hosts is also considered. At the host population scale, the epidemiological dynamics is given by an individual-based algorithm. Global sensitivity analyses allowed ranking the ten demo-genetic and epidemiological parameters of the model according to their impact on the mean and variance of the risk of breakdown of a plant resistance. Demo-genetic parameters (number and nature of mutations involved in breakdown, fitness of mutant genotypes) had the largest impact on the mean breakdown risk, whereas epidemiological parameters had more influence on its standard deviation. It is discussed how these results can be used to choose the potentially most durable resistance genes among a pool of candidates. Finally, our analyses point out the parameters which should be estimated more precisely to improve durability predictions.