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

• Published in: Phytopathology Vol. 101; no. 6; pp. 725 - 731
• Main Authors: Demon, I, Cunniffe, N.J, Marchant, B.P, Gilligan, C.A, Bosch, F. van den
• Format: Journal Article
• Language: English
• Published: St. Paul, MN American Phytopathological Society 01-06-2011
• Subjects: annealing; Biological and medical sciences; Computer Simulation; Demography - statistics & numerical data; disease detection; Disease Outbreaks; Fundamental and applied biological sciences. Psychology; hosts; Introduced Species - statistics & numerical data; Models, Statistical; Normal Distribution; pathogens; Phytopathology. Animal pests. Plant and forest protection; Plant Diseases - statistics & numerical data; plant pathology; Probability; Reproducibility of Results; sampling; Sampling Studies; Sensitivity and Specificity; simulation models; system optimization; Plant pathology; Plant; Plant pathogen; Detection; Samplings
• ISSN: 0031-949X; 1943-7684
• DOI: 10.1094/PHYTO-05-09-0120

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.