Student Number: 9811822T -
MSc Dissertation -
School of Computational and Applied Mathematics -
Faculty of Science / This dissertation reviews population dynamic type models of viral infection and
introduces some new models to describe strain competition and the infected cell
lifecycle. Laboratory data from a recent clinical trial, tracking drug resistant virus
in patients given a short course of monotherapy is comprehensively analysed, paying
particular attention to reproducibility. A Bayesian framework is introduced, which
facilitates the inference of model parameters from the clinical data. It appears that
the rapid emergence of resistance is a challenge to popular unstructured models of
viral infection, and this challenge is partly addressed. In particular, it appears that
minimal ordinary differential equations, with their implicit exponential lifetime (constant
hazard) distributions in all compartments, lack the short transient timescales
observed clinically. Directions for future work, both in terms of obtaining more informative
data, and developing more systematic approaches to model building, are
identified.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:wits/oai:wiredspace.wits.ac.za:10539/2183 |
| Date | 01 March 2007 |
| Creators | Pretorius, Carel Diederik |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | 3032883 bytes, application/pdf, application/pdf |
Page generated in 0.0019 seconds