FITTING AND TESTING SPATIOTEMPORAL STOCHASTIC-MODELS WITH APPLICATIONIN PLANT EPIDEMIOLOGY

We propose and illustrate a likelihood-based method for fitting spatio -temporal stochastic models for the spread of a plant disease to exper imental observations. The models considered are individual-based, with members of the population occupying discrete sites on a two-dimension al lattice. The disease is assumed to be characterized by presence/abs ence, and infection of susceptible individuals by infected individuals is represented as a stochastic process. The method described can be a pplied to estimate parameters in models of this kind when observations consisting of temporal sequences of disease maps are available. The u se of measures of spatial aggregation as measured from simulated and r eal epidemics is proposed as a means of assessing the relative merits of alternative models for the spread of disease. To illustrate the tec hnique we fit and compare two models, which differ in the relationship between infective pressure and distance, to observations of an epidem ic of citrus tristeza virus (CTV). It is demonstrated that a model in which this relationship is a power-law is superior to one which uses a negative exponential and the importance of model choice for the desig n of control strategies is discussed briefly.