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The Effect of Heterogeneity on Invasion in Spatial Epidemics: From Theory to Experimental Evidence in a Model System

Overview of attention for article published in PLoS Computational Biology, September 2011
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Title
The Effect of Heterogeneity on Invasion in Spatial Epidemics: From Theory to Experimental Evidence in a Model System
Published in
PLoS Computational Biology, September 2011
DOI 10.1371/journal.pcbi.1002174
Pubmed ID
Authors

Franco M. Neri, Anne Bates, Winnie S. Füchtbauer, Francisco J. Pérez-Reche, Sergei N. Taraskin, Wilfred Otten, Douglas J. Bailey, Christopher A. Gilligan

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.

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Geographical breakdown

Country Count As %
United States 3 5%
Canada 2 3%
United Kingdom 1 2%
Belgium 1 2%
Netherlands 1 2%
Unknown 57 88%

Demographic breakdown

Readers by professional status Count As %
Researcher 24 37%
Student > Ph. D. Student 15 23%
Student > Master 5 8%
Student > Bachelor 4 6%
Student > Doctoral Student 3 5%
Other 11 17%
Unknown 3 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 31 48%
Mathematics 6 9%
Environmental Science 5 8%
Physics and Astronomy 3 5%
Computer Science 3 5%
Other 12 18%
Unknown 5 8%