Trends in theoretical plant epidemiology

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Abstract

We review trends and advances in three specific areas of theoretical plant epidemiology: models of temporal and spatial dynamics of disease, the synergism of epidemiology and population genetics, and progress in statistical epidemiology. Recent analytical modelling of disease dynamics has focused on SIR (susceptible-infected-removed) models modified to include spatial structure, stochasticity, and multiple management-related parameters. Such models are now applied routinely to derive threshold criteria for pathogen invasion or persistence based on pathogen demographics (e.g., Allee effect or fitness of fungicide-resistant strains) and/or host spatial structure (e.g., host density or patch size and arrangement). Traditionally focused on the field level, the scale of analytical models has broadened to range from individual plants to landscapes and continents; however, epidemiological models for interactions at the cellular level, e.g., during the process of virus infection, are still rare. There is considerable interest in the concept of scaling, i.e., to what degree and how data and models from one scale can be transferred to another (smaller or larger) scale. Despite assertions to the contrary, the linkages between epidemiology and population genetics are alive and well as exemplified by recent efforts to integrate epidemiological parameters into population genetics models (and vice versa) and by numerous integrated studies with an applied focus (e.g., to quantify sources and types of primary and secondary inoculum). Statistical plant epidemiology continues to rely heavily on the medical and ecological fields for inspiration and conceptual advances, as illustrated by the recent surge in papers utilizing ROC (receiver operating characteristic), Bayesian, or survival analysis. Among these, Bayesian analysis should prove especially fruitful given the reliance on uncertain and subjective information for practical disease management. However, apart from merely adopting statistical tools from other disciplines, plant epidemiologists should be more proactive in exploring potential applications of their concepts and procedures in rapidly expanding disciplines such as statistical genetics or bioinformatics. Although providing the scientific basis for disease management will always be the raison d'être for plant epidemiology, a broader perspective will help the discipline to remain relevant as more resources are being devoted to genomic and ecosystem-level science.

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