title: The effect of heterogeneity on invasion in spatial epidemics: from theory to experimental evidence in a model system.
abstract: Heterogeneity in host populations is an important factor affecting the ability of a pathogen to invade, yet the quantitative investigation of its effects on epidemic spread is still an open problem. In this paper, we test recent theoretical results, which extend the established "percolation paradigm" to the spread of a pathogen in discrete heterogeneous host populations. In particular, we test the hypothesis that the probability of epidemic invasion decreases when host heterogeneity is increased. We use replicated experimental microcosms, in which the ubiquitous pathogenic fungus Rhizoctonia solani grows through a population of discrete nutrient sites on a lattice, with nutrient sites representing hosts. The degree of host heterogeneity within different populations is adjusted by changing the proportion and the nutrient concentration of nutrient sites. The experimental data are analysed via Bayesian inference methods, estimating pathogen transmission parameters for each individual population. We find a significant, negative correlation between heterogeneity and the probability of pathogen invasion, thereby validating the theory. The value of the correlation is also in remarkably good agreement with the theoretical predictions. We briefly discuss how our results can be exploited in the design and implementation of disease control strategies.
authors: Department of Plant Sciences, University of Cambridge, Cambridge, United Kingdom. fmn22@cam.ac.uk
authors: Franco M
authors: FM
authors: Neri
authors: Anne
authors: A
authors: Bates
authors: Winnie S
authors: WS
authors: Füchtbauer
authors: Francisco J
authors: FJ
authors: Pérez-Reche
authors: Sergei N
authors: SN
authors: Taraskin
authors: Wilfred
authors: W
authors: Otten
authors: Douglas J
authors: DJ
authors: Bailey
authors: Christopher A
authors: CA
authors: Gilligan
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D003140:Communicable Disease Control; D019295:Computational Biology; D003198:Computer Simulation; D018562:Disease Transmission, Infectious; D058872:Epidemics / Q000706:statistics & numerical data*; D054884:Host-Pathogen Interactions; D006801:Humans; D008954:Models, Biological