MARKOV-CHAIN MONTE-CARLO METHODS FOR FITTING SPATIOTEMPORAL STOCHASTIC-MODELS IN PLANT EPIDEMIOLOGY

Strategies for controlling plant epidemics are investigated by fitting continuous time spatiotemporal stochastic models to data consisting o f maps of disease incidence observed at discrete times. Markov chain M onte Carlo methods are used for fitting two such models to data descri bing the spread of citrus tristeza virus (CTV) in an orchard. The appr oach overcomes some of the difficulties encountered when fitting stoch astic models to infrequent observations of a continuous process. The r esults of the analysis cast doubt on the effectiveness of a strategy i dentified from a previous spatial analysis of the CTV data. Extensions of the approaches to more general models and other problems are also considered.