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Individual-level Models for use with Incomplete Infectious Disease Data and Related Topics

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Title: Individual-level Models for use with Incomplete Infectious Disease Data and Related Topics
Author: Bifolchi, Nadia
Department: Department of Mathematics and Statistics
Program: Mathematics and Statistics
Advisor: Deardon, RobFeng, Zeny
Abstract: Individual-level models (ILMs) of infectious disease transmission have the ability to incorporate individual-level covariate information and thus, account for heterogeneity within the population. The amount of required data to parametrize these models and the inherent uncertainty associated with collecting infection history data can lead to large amounts of missing/incomplete information. This thesis contains three chapters describing work related to using individual-level models for incomplete infectious disease data. Infectious disease is generally spread via complex individual-level interactions. The full population’s individual-level interactions comprise the contact network but, often this contact network is unobserved. In Chapter 2, a simulation study is used to determine the effect of using spatial information as a proxy to more complex network information within ILMs fitted for predictive epidemic modelling purposes. Infectious disease models are frequently employed to predict disease spread and determine optimal strategies for disease control. In Chapter 3, a simulation study is used to examine the use of risk-based surveillance/control strategies in effectively minimizing the number of infected farms. An outbreak of an emergent strain of swine influenza within the southern Ontario pork industry is used as an example. Each farm’s risk is estimated using an ILM fit to varying degrees of available data. Limited resources are also considered through restrictions on the number of available tests. Various schemes for the allocation of these testing resources (e.g. by farm production type) is compared. In Chapter 4, several parameterizations of ILMs are proposed to better account for unobserved data due to the choice of sampling/surveillance scheme. A simulation study is used to carry out this research, with infectious disease data collected under various sampling/ surveillance scenarios. These sampling schemes are defined by two factors, the number of farms observed (sample size) and the time interval between consecutive observations. Models are parameterized to better account for time-varying epidemic strength and the effect of temporal discretization. The final chapter, Chapter 5, of this thesis looks at determining the extent to which proximity to cattle and weather events in Alberta predispose human populations to E. coli O157 disease.
URI: http://hdl.handle.net/10214/8760
Date: 2015-04


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