Designing experiments to assess the space-time dynamics of plant diseases

dc.contributor.advisorDeardon, R.
dc.contributor.advisorMcNicholas, P.
dc.contributor.authorMartchenko, Daria
dc.date.accessioned2020-08-24T15:30:20Z
dc.date.available2020-08-24T15:30:20Z
dc.date.copyright2011
dc.degree.departmentDepartment of Mathematics and Statisticsen_US
dc.degree.grantorUniversity of Guelphen_US
dc.degree.nameMaster of Scienceen_US
dc.description.abstractInfectious 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.en_US
dc.identifier.urihttps://hdl.handle.net/10214/19459
dc.language.isoen
dc.publisherUniversity of Guelphen_US
dc.rights.licenseAll items in the Atrium are protected by copyright with all rights reserved unless otherwise indicated.
dc.subjectPlant diseaseen_US
dc.subjectExperimentsen_US
dc.subjectDesigningen_US
dc.subjectSpace-time dynamicsen_US
dc.subjectSpatio-temporalen_US
dc.titleDesigning experiments to assess the space-time dynamics of plant diseasesen_US
dc.typeThesisen_US

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