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

David R J Pleydell,Sylvie Dallot,Samuel Soubeyrand,Gérard Labonne,Emmanuel Jacquot,Joël Chadœuf,Gaël Thébaud,Samuel Alizon

doi:10.1371/journal.pcbi.1006085

Abstract

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 huge 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 patchy (i.e. fragmented) landscape when disease control is ongoing. Applying this model to data for an aphid-borne virus (Plum pox virus) surveyed for 15 years in 605 orchards, we obtain the first estimate of the distribution of flight distances of infectious aphids at the landscape scale. About 50% of aphid flights terminate beyond 90 m, which implies that most infectious aphids leaving a tree land outside the bounds of a 1-ha orchard. Moreover, long-distance flights are not rare–10% of flights exceed 1 km. By their impact on our quantitative understanding of winged aphid dispersal, these results can inform the design of management strategies for plant viruses, which are mainly aphid-borne.

Highlights

Infectious diseases of humans, animals and plants severely impact the world’s health and economy
Dispersal kernels quantify how the probability of pathogen dissemination varies with distance from an infection source
The aims of this study are: (i) to develop a Bayesian inference framework for estimating, from surveillance data, the parameters of a spatially-explicit epidemiological model that accounts for patch geometry and for interactions between disease spread, surveillance and control, (ii) to assess through simulations the accuracy and precision of the dispersal parameters estimated under various epidemic scenarios, and (iii) to apply our method to 15 years of geo-referenced surveillance data collected during an epidemic of Plum pox virus in order to estimate the dispersal kernel of the aphid vectors

Summary

Introduction

Infectious diseases of humans, animals and plants severely impact the world’s health and economy. Models frequently employ dispersal kernels to represent how the probability of dispersal events diminishes as a function of distance, and simulation studies have proven that dispersal parameters can be identified in idealised scenarios [5]. This has been achieved for simple models or small-scale datasets [8,9,10,11,12,13]. Further difficulties arise when surveillance data are aggregated at the patch scale because a landscape comprising patches of various shapes or sizes often cannot be summarized by patch centroids without biasing connectivity estimates All these issues require appropriate correction measures to avoid biased inference and prediction [25]

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Conclusion