Will the "fight" ever end? : a critical reading of the metaphors and discourses that construct HIV/AIDS in an African context

In this thesis, I critically examine the discourses that inform how we conceptualise HIV/AIDS in Sub-Saharan Africa as they are produced in a sample of Canadian news articles, two nonfiction texts - Stephanie Nolen's 28 Stories of AIDS in Africa and Jonathan Morgan and the Bambanani Women's Group's Long Life ... Positive HIV Stories - as well as two literary texts - John Le Carre's popular fiction novel The Constant Gardener and an anthology of stories and poems from Southern Africa titled Nobody Ever Said AIDS, compiled and edited by Nobantu Rasebotsa, Meg Samuelson and Kylie Thomas. Paying particular attention to the role of metaphor in discursive formation, I have found that military metaphors, usually used in conjunction with biomedical discourses, continue to dominate what is said about HIV/AIDS. However, the use of military metaphors to conceptualise HIV/AIDS contributes to stigma and limits the effectiveness of responses to the pandemic. I argue that accessing alternative metaphors and discourses, such as biopsychosocial discourse, can lead to a more layered - and more beneficial - conceptualisation of HIV/AIDS, encouraging a more active response to the pandemic.

Heuristics for the Critical Node Detection Problem in Large Complex Networks

Complex networks have recently attracted a significant amount of research attention due to their ability to model real world phenomena. One important problem often encountered is to limit diffusive processes spread over the network, for example mitigating pandemic disease or computer virus spread. A number of problem formulations have been proposed that aim to solve such problems based on desired network characteristics, such as maintaining the largest network component after node removal. The recently formulated critical node detection problem aims to remove a small subset of vertices from the network such that the residual network has minimum pairwise connectivity. Unfortunately, the problem is NP-hard and also the number of constraints is cubic in number of vertices, making very large scale problems impossible to solve with traditional mathematical programming techniques. Even many approximation algorithm strategies such as dynamic programming, evolutionary algorithms, etc. all are unusable for networks that contain thousands to millions of vertices. A computationally efficient and simple approach is required in such circumstances, but none currently exist. In this thesis, such an algorithm is proposed. The methodology is based on a depth-first search traversal of the network, and a specially designed ranking function that considers information local to each vertex. Due to the variety of network structures, a number of characteristics must be taken into consideration and combined into a single rank that measures the utility of removing each vertex. Since removing a vertex in sequential fashion impacts the network structure, an efficient post-processing algorithm is also proposed to quickly re-rank vertices. Experiments on a range of common complex network models with varying number of vertices are considered, in addition to real world networks. The proposed algorithm, DFSH, is shown to be highly competitive and often outperforms existing strategies such as Google PageRank for minimizing pairwise connectivity.