Logistic Growth Models for Estimating Vaccination Effects In Infectious Disease Transmission Experiments

dc.contributor.advisorDeardon, Rob
dc.contributor.authorCai, Longyao
dc.date.accessioned2013-01-14T15:42:16Z
dc.date.available2013-01-14T15:42:16Z
dc.date.copyright2013-01
dc.date.created2013-01-04
dc.date.issued2013-01-14
dc.degree.departmentDepartment of Mathematics and Statisticsen_US
dc.degree.grantorUniversity of Guelphen_US
dc.degree.nameMaster of Scienceen_US
dc.degree.programmeMathematics and Statisticsen_US
dc.description.abstractVeterinarians often perform controlled experiments in which they inoculate animals with infectious diseases. They then monitor the transmission process in infected animals. The aim of such experiments can be to assess vaccine effects. The fitting of individual-level models (ILMs) to the infectious disease data, typically achieved by means of Markov Chain Monte Carlo (MCMC) methods, can be computationally burdensome. Here, we want to see if a vaccination effect can be identified using simpler regression-type models rather than the complex infectious disease models. We examine the use of various logistic growth curve models, via a series of simulated experiments in which the underlying true model is a mechanistic model of infectious disease spread. We want to investigate whether a vaccination effect can be identified when only partial epidemic curves are observed, and to assess the performance of these models when experiments are run with various sets of observational times.en_US
dc.identifier.urihttp://hdl.handle.net/10214/5314
dc.language.isoenen_US
dc.publisherUniversity of Guelphen_US
dc.rightsAttribution 2.5 Canada*
dc.rights.urihttp://creativecommons.org/licenses/by/2.5/ca/*
dc.subjectindividual level modelsen_US
dc.subjectinfectious diseaseen_US
dc.subjectgrowth curve modelsen_US
dc.subjectlogistic growth modelsen_US
dc.subjectvaccination effecten_US
dc.subjectsimulationen_US
dc.subjectpartial epidemic curvesen_US
dc.subjectmixed effectsen_US
dc.subjectdisease transmission experimenten_US
dc.titleLogistic Growth Models for Estimating Vaccination Effects In Infectious Disease Transmission Experimentsen_US
dc.typeThesisen_US

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