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Dynamic Transmission Models: The Impact of Behavioural Feedbacks and Parametrization Methods on Disease Intervention Effectiveness

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Title: Dynamic Transmission Models: The Impact of Behavioural Feedbacks and Parametrization Methods on Disease Intervention Effectiveness
Author: Andrews, Michael
Department: Department of Mathematics and Statistics
Program: Mathematics and Statistics
Advisor: Bauch, Chris
Abstract: Infectious diseases impose significant health and economic burdens across the world, continuously threatening human quality of life. Mathematical models of infectious disease epidemiology can help to gain insight on potential health outcomes of a population that is vulnerable to disease spread. Models that incorporate decision-making mechanisms can furthermore capture how behaviour-driven aspects of transmission such as vaccination choices and the use of non-pharmaceutical interventions (NPIs) interact with disease dynamics. In this thesis, we present three models of disease spread using the dynamic transmission and behaviour-disease modelling frameworks. Firstly, we investigate an age-stratified compartmental model of influenza transmission and the impact of estimating this model's parameters using different surveillance data has on population level outcomes. In the latter two models, we remove random mixing assumptions and incorporate an individual-based approach. We also simultaneously integrate an individual's decision making processes for utilizing two fundamental disease interventions: vaccination and NPIs. In the past, models have only considered the use of these interventions separately. All of our approaches focus on examining health outcomes of populations that are exposed to acute self-limited diseases, and offering insight on the effectiveness of disease mitigation strategies.
URI: http://hdl.handle.net/10214/10076
Date: 2016-10
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