Predictive models for HIV related Lymphomas
Project ended 31 March 2018
Prof Farai Nyabadza
Title of the project
Predictive models for HIV related Lymphomas.
This project is part of a multidisciplinary study on HIV related Lymphomas (HRLs) in the Western Cape province of South Africa, under the Tygerberg Lymphoma Study Group (TLSG) of the University of Stellenbosch Medical School. The TLSG is a cross-disciplinary and highly-collaborative research entity based at Tygerberg Hospital. The team consists of scientists who aim to unravel the complexities of HIV related cancer progression through hematopathological techniques and mathematical modeling.The mastery of its investigators span the disciplines of Medical Sciences and Mathematics. Our collaborative mandate is to produce mathematical models that can be fitted to data based on known demographic features of the population and applying this to prospective scenarios. At the heart of this project is the application mathematics to cancer research. This is a field that is increasingly being seen as an essential component in the future of cancer research. The focus will be on two fronts (i) the use of data from the TLSG to design models that can be used as national HRLs trends prediction tools and (ii) modelling the interaction of the immune system and cancer cells. With regards to the former, a cost-benefit and cost-effectiveness analysis will be carried out based on the model outputs. Projections allow for planning and influencing of policy. With regards to the later, mathematical modelling is intertwined with experimental work, as it elucidates significant relationships that has contributed to the current cancer treatments. This innovative proposed project will therefore, through the interaction with experimental and social sciences, be well-aligned with the scientific objectives of CANSA.
Mathematics is studied because it is a rich and interesting discipline. It provides a set of ideas and tools that are effective in solving problems which arise in other fields of study. First, mathematical modeling usually begins with a situation in the real world, like in our case, observations in the rising of HIV related cancers. Second, we make the problem as precise as possible. The purpose here is to eliminate unnecessary information and to simplify that which is retained as much as possible. In our case, there are a lot of types of Lymphomas identified at TLSG. The sifting process of taking only that which is significant simplifies the model construction. Third, through some high level creativity, the real model is then represented symbolically looking at the dynamic processes involved. As a result, the real world model becomes a mathematical model in which the real quantities and processes are replaced by mathematical symbols and relations and mathematical operations. Any modeling process may involve several mathematical models for the same real situation. The choice of the model often depends on the question to be answered.The current work we are looking at, for the model that we designed, TreeAge Pro (software package) automatically generates the algorithms required to evaluate the model and choose the optimal strategy. This allows us to focus on the problem at hand and not the calculations needed to evaluate the model. It also allows us to compare strategies on the basis of cost-effectiveness via incremental cost effectiveness ratios and/or net benefits. We can also compare strategies in the same model based solely on cost or comparative effectiveness (CER). One can even use non-standard measurements such as infections, deaths, etc. The work also involved the use of model to predict the future of epidemics or observed trends as in the case of the data that was being collected by the TLSG.