title: Disease spread across multiple scales in a spatial hierarchy: effect of host spatial structure and of inoculum quantity and distribution.
abstract: Spatial patterns of both the host and the disease influence disease spread and crop losses. Therefore, the manipulation of these patterns might help improve control strategies. Considering disease spread across multiple scales in a spatial hierarchy allows one to capture important features of epidemics developing in space without using explicitly spatialized variables. Thus, if the system under study is composed of roots, plants, and planting hills, the effect of host spatial pattern can be studied by varying the number of plants per planting hill. A simulation model based on hierarchy theory was used to simulate the effects of large versus small planting hills, low versus high level of initial infections, and aggregated versus uniform distribution of initial infections. The results showed that aggregating the initially infected plants always resulted in slower epidemics than spreading out the initial infections uniformly. Simulation results also showed that, in most cases, disease epidemics were slower in the case of large host aggregates (100 plants/hill) than with smaller aggregates (25 plants/hill), except when the initially infected plants were both numerous and spread out uniformly. The optimal strategy for disease control depends on several factors, including initial conditions. More importantly, the model offers a framework to account for the interplay between the spatial characteristics of the system, rates of infection, and aggregation of the disease.
authors.affiliation: UMR 1099, Biologie des Organismes et des Populations appliquée a la Protection des Plantes, Institut National de la Recherche Agronomique-Agrocampus, Le Rheu, France. marie.gosme@grignon.inra.fr
authors.forename: Marie
authors.initials: M
authors.lastname: Gosme
authors.forename: Philippe
authors.initials: P
authors.lastname: Lucas
mesh_terms: D054884:Host-Pathogen Interactions; D015994:Incidence; D015233:Models, Statistical; D010935:Plant Diseases / Q000382:microbiology* / Q000706:statistics & numerical data*; D010944:Plants / Q000382:microbiology*