Heuristic optimisation of the management strategy of a plant epidemic using sequential sensitivity analyses

Optimisation of management strategies of epidemics is often limited by constraints on experiments at large spatiotemporal scales. A promising approach consists in modelling the biological epidemic process and human interventions, which both impact disease spread. However, few methods enable the simultaneous optimisation of the numerous parameters of sophisticated control strategies. To do so, we propose a heuristic approach based on sequential use of sensitivity analysis. This work is motivated by sharka (caused by Plum pox virus), a vector-borne disease of prunus trees (especially apricot, peach and plum), and its management in orchards, mainly based on surveillance and tree removal. Our approach is based on three sensitivity analyses which respectively aim to: i) identify the key parameters of a spatiotemporal model simulating disease spread and control; ii) approach optimal values for the key parameters; iii) refine the optimisation. We highlight the importance of carefully designing the removal procedure, and propose improved strategies with regard to an economic criterion accounting for both the cost of the different control measures and the benefit generated by productive trees. We expect that our general approach will help policymakers to design sustainable and cost-effective strategies for the management of infectious diseases.