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Spatial Sampling to Detect an Invasive Pathogen Outside of an Eradication Zone

First, third, and fifth authors: Department of Biomathematics and Bioinformatics, Rothamsted Research, West Common, Harpenden, Hertfordshire, AL5 2JQ, United Kingdom; and second and fourth authors: University of Cambridge, Department of Plant Sciences, Downing Site, Cambridge, CB2 3EA, United Kingdom.

Invasive pathogens are known to cause major damage to the environments they invade. Effective control of such invasive pathogens depends on early detection. In this paper we focus on sampling with the aim of detecting an invasive pathogen. To that end, we introduce the concept of optimized spatial sampling, using spatial simulated annealing, to plant pathology. It has been mathematically proven (15) that this optimization method converges to the optimum allocation of sampling points that give the largest detection probability. We show the benefits of the method to plant pathology by (i) first illustrating that optimized spatial sampling can easily be applied for disease detection, and then we show that (ii) combining it with a spatially explicit epidemic model, we can develop optimum sample schemes, i.e., optimum locations to sample that maximize the probability of detecting an invasive pathogen. This method is then used as baseline against which other sampling methods can be tested for their accuracy. For the specific example case of this paper, we test (i) random sampling, (ii) stratified sampling as well as (iii) sampling based on the output of the simulation model (using the most frequently infected hosts as sample points), and (iv) sampling the hosts closest to the outbreak point.

Additional keywords: dispersal kernel, objective function, SEIR model.