Dallot, S. | Search bioRxiv

bioRxiv2018

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

O_LIOptimisation 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.\nC_LIO_LIOur 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.\nC_LIO_LIWe 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.\nC_LIO_LIWe expect that our general approach will help policymakers to design sustainable and cost-effective strategies for the management of infectious diseases.\nC_LI

epidemiology↗

bioRxiv2017

Estimation of the dispersal distances of an aphid-borne virus in a patchy landscape

Characterising the spatio-temporal dynamics of pathogens in natura is key to ensuring their efficient prevention and control. However, it is notoriously difficult to estimate dispersal parameters at scales that are relevant to real epidemics. Epidemiological surveys can provide informative data, but parameter estimation can be hampered when the timing of the epidemiological events is uncertain, and in the presence of interactions between disease spread, surveillance, and control. Further complications arise from imperfect detection of disease, and from the computationally intractable number of data on individual hosts arising from landscape-level surveys. Here, we present a Bayesian framework that overcomes these barriers by integrating over associated uncertainties in a model explicitly combining the processes of disease dispersal, surveillance and control. Using a novel computationally efficient approach to account for patch geometry, we demonstrate that disease dispersal distances can be estimated accurately in a fragmented landscape when disease control is ongoing. Applying this model to data for an aphid-borne virus (Plum pox virus) surveyed for 15 years over 600 orchards, we obtain the first estimate of the distribution of the flight distances of infectious aphids at the landscape scale. Most infectious aphids leaving a tree land beyond the bounds of a 1-ha orchard (50% of flights terminate within about 90 m). Moreover, long-distance flights are not rare (10% of flights exceed 1 km). By their impact on our quantitative understanding of winged aphids dispersal, these results can inform the design of management strategies for plant viruses, which are mainly aphid-borne.\n\nAuthor SummaryIn spatial epidemiology, dispersal kernels quantify how the probability of pathogen dissemination varies with distance. Spatial models of pathogen spread are sensitive to kernel parameters; yet these parameters have rarely been estimated using field data gathered at relevant scales. Robust estimation is rendered difficult by practical constraints limiting the number of surveyed individuals, and uncertainties concerning their disease status. Here, we present a framework that overcomes these barriers and permits inference for a between-patch transmission model. Extensive simulations show that dispersal kernels can be estimated from epidemiological surveillance data. When applied to such data collected from more than 600 orchards during 15 years of a plant virus epidemic our approach enables the estimation of the dispersal kernel of infectious winged aphids. This kernel is long-tailed, as 50% of the infectious aphids leaving a tree terminate their infectious flight within 90 m and 10% beyond 1 km. This first estimate of flight distances at the landscape scale for aphids-a group of vectors transmitting numerous viruses-is crucial for the science-based design of control strategies targeting plant virus epidemics.

epidemiology↗