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

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dc.contributor.advisor Deardon, Rob Cai, Longyao 2013-01-14T15:42:16Z 2013-01-14T15:42:16Z 2013-01 2013-01-04 2013-01-14
dc.description.abstract Veterinarians 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.language.iso en en_US
dc.rights.uri *
dc.subject individual level models en_US
dc.subject infectious disease en_US
dc.subject growth curve models en_US
dc.subject logistic growth models en_US
dc.subject vaccination effect en_US
dc.subject simulation en_US
dc.subject partial epidemic curves en_US
dc.subject mixed effects en_US
dc.subject disease transmission experiment en_US
dc.title Logistic Growth Models for Estimating Vaccination Effects In Infectious Disease Transmission Experiments en_US
dc.type Thesis en_US Mathematics and Statistics en_US Master of Science en_US Department of Mathematics and Statistics en_US
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