Title page for ETD etd-08172008-213855

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
Advisor Name Title
Prof X Xia Committee Chair
  • identifiability
  • HIV and TB parameter estimation
  • simulation of states
  • bioengineering
  • dynamic system
  • modelling
  • medical system
  • non-linear systems
Date 2008-09-02
Availability unrestricted

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


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