Designing experiments to assess the space-time dynamics of plant diseases
Infectious diseases of plants can cause havoc both economically and environmentally. In order to control such disease it is desirable to understand the dynamics, which are generally spatio-temporal in nature, of the disease spread. In some situations experiments may be carried out to assess such dynamics. However, little work has been done exploring how best to design such experiments. Using a simple spatial Individual Level Model (ILM) we explore three spatial layouts (grid, Gaussian and deterministic). Employing Markov chain Monte Carlo (MCMC) methods within a Bayesian framework we investigate the estimation of disease spread model parameters under the different experimental layouts and spatio-temporal restrictions.