Document Type	Master's Dissertation
Author	Du Toit, Eben Francois
Email	ebendutoit@gmail.com
URN	etd-08172008-213855
Document Title	Modelling the co-infection dynamics of HIV-1 and M. tuberculosis
Degree	MEng (Electronic Engineering)
Department	Electrical, Electronic and Computer Engineering
Supervisor	Advisor Name Title Prof X Xia Committee Chair
Keywords	identifiability HIV and TB parameter estimation simulation of states bioengineering dynamic system modelling medical system non-linear systems
Date	2008-09-02
Availability	unrestricted
Abstract This dissertation focuses on the modelling, identification and the parameter estimation for the co-infection of HIV-1 and M. tuberculosis. Many research papers in this field focus primarily on HIV, but multiple infections are explored here, as it is common in many individuals infected by HIV. Tuberculosis is also responsible for the highest number of casualties per year in the group of HIV-infected individuals. A model is proposed to indicate the populations of both pathogen as well as key information factors, such as the overall infected cell population and antigen-presenting cells. Simulations are made to indicate the growth and decline in cell-type numbers for a specific individual. Such simulations would provide a means for further, well-founded investigation into appropriate treatment strategies. One previous such model developed by Kirschner is used to obtain a nominal parameter set. Furthermore, the nominal set is then used in conjunction with real-world samples provided by the National Institute for Communicable Diseases in South Africa, to solidify the credibility of the model in the practical case. This is achieved via simulations and employs parameter estimation techniques, namely the Nelder-Mead cost-function method. An identifiability study of the model is also done. Conclusions drawn from this study include the result that the treatment of M. tuberculosis does not affect the course of HIV-1 progression in a notable way, and that the model can indeed be used in the process of better understanding the disease profile over time of infected individuals. © University of Pretoria 2008 C114/eo
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