Latest News Archive

Please select Category, Year, and then Month to display items
Previous Archive
11 December 2019 | Story Leonie Bolleurs
Aids read more

According to Global Statistics, there were approximately 37,9 million people across the globe with HIV/Aids in 2018. They also state that in 2018, an estimated 1,7 million individuals worldwide became newly infected with HIV. 

In the city of Masvingo, Zimbabwe, Claris Shoko is a Statistics lecturer at the Great Zimbabwe University. In her PhD thesis at the University of the Free State (UFS) in the Department of Mathematical Statistics and Actuarial Sciences, she presented the argument that the inclusion of both the CD4 cell count and the viral-load counts in the monitoring and management of HIV+ patients on antiretroviral therapy (ART), is helping in reducing mortality rates, leading to improved life expectancy for HIV/Aids patients. 

She received her doctoral degree at the December UFS Graduation Ceremonies, with her thesis: Continuous-time Markov modelling of the effects of treatment regimens on HIV/Aids immunology and virology. 

CD4 cell count and viral-load count

Dr Shoko explains: “When the human immunodeficiency virus (HIV) enters the human body, the virus attacks the CD4 cells in their blood. This process damages CD4 cells, causing the number of white blood cells in the body to drop, making it difficult to fight infections.”

“Clinical markers such as CD4 cell count and viral-load count (number of HIV particles in a ml of blood) provide information about the progression of HIV/Aids in infected individuals. These markers fully define the immunology and the virology of HIV-infected individuals, thereby giving us a clear picture of how HIV/Aids evolve within an individual.”

Dr Shoko continues: “The development of highly active antiretroviral therapy (HAART) has helped substantially to reduce the death rate from HIV. HAART reduces viral load-count levels, blocking replication of HIV particles in the blood, resulting in an increase of CD4 cell counts and the life expectancy of individuals infected with HIV. This has made CD4 cell counts and viral-load counts the fundamental laboratory markers that are regularly used for patient management, in addition to predicting HIV/Aids disease progression or treatment outcomes.”

In the treatment of HIV/Aids, medical practitioners prescribe combination therapy to attack the virus at different stages of its life cycle, and medication to treat the opportunistic infections that may occur. “The introduction of combined antiretroviral therapy (cART) has led to the dramatic reduction in morbidity and mortality at both individual level and population level,” states Dr Shoko.

Once HIV-positive patients are put on cART, the effectiveness of treatment is monitored after the first three months and a further follow-up is done every six months thereafter. During the monitoring stages, CD4 cell count and viral load is measured. Patients are also screened for any tuberculosis (TB) co-infection and checked for any signs of drug resistance. These variables determine whether or not there is a need for treatment change. 

She continues: “Previous studies on HIV modelling could not include both CD4 cell count and viral load in one model, because of the collinearity between the two variables. In this study, the principal component approach for the treatment of collinearity between variables is used. Both variables were then included in one model, resulting in a better prediction of mortality than when only one of the variables is used.”

“Viral-load monitoring helps in checking for any possibilities of virologic failure or viral rebound, which increases the rate of mortality if not managed properly. CD4 cell count then comes in to monitor the potential development of opportunistic infections such as TB. TB is extremely fatal, but once detected and treated, the survival of HIV/Aids patients is assured,” Dr Shoko explains.

Markov model

She applied the Markov model in her study. The model, named after the Russian mathematician Andrey Markov, represents a general category of stochastic processes, characterised by six basic attributes: states, stages, actions, rewards, transitions, and constraints. 

According to Dr Shoko, Markov models assume that a patient is always in one of a finite number of discrete states, called Markov states. All events are modelled as transitions from one state to another. Each state is assigned a utility, and the contribution of this utility to the overall prognosis depends on the length of time spent in each state. For example, for a patient who is HIV positive, these states could be HIV+ (CD4 cell count above 200 cells/mm3), Aids (CD4 cell count below 200 cells/mm3) and Dead.

“Markov models are ideal for use in HIV/Aids studies, because they estimate the rate of transition between multiple-disease states while allowing for the possible reversibility of some states,” says Dr Shoko, quoting Hubbard and Zhou.

“Relatively fewer HIV modelling studies include a detailed description of the dynamics of HIV viral load count during stages of HIV disease progression. This could be due to the unavailability of data on viral load, particularly from low- and middle-income countries that have historically relied on monitoring CD4 cell counts for patients on ART because of higher costs of viral load-count testing,” Dr Shoko concludes

News Archive

Inaugural lecture: Prof. Phillipe Burger
2007-11-26

 

Attending the lecture were, from the left: Prof. Tienie Crous (Dean of the Faculty of Economic and Management Sciences at the UFS), Prof. Phillipe Burger (Departmental Chairperson of the Department of Economics at the UFS), and Prof. Frederick Fourie (Rector and Vice-Chancellor of the UFS).
Photo: Stephen Collet

 
A summary of an inaugural lecture presented by Prof. Phillipe Burger on the topic: “The ups and downs of the South African Economy: Rough seas or smooth sailing?”

South African business cycle shows reduction in volatility

Better monetary policy and improvements in the financial sector that place less liquidity constraints on individuals is one of the main reasons for the reduction in the volatility of the South African economy. The improvement in access to the financial sector also enables individuals to manage their debt better.

These are some of the findings in an analysis on the volatility of the South African business cycle done by Prof. Philippe Burger, Departmental Chairperson of the University of the Free State’s (UFS) Department of Economics.

Prof. Burger delivered his inaugural lecture last night (22 November 2007) on the Main Campus in Bloemfontein on the topic “The ups and downs of the South African Economy: Rough seas or smooth sailing?”

In his lecture, Prof. Burger emphasised a few key aspects of the South African business cycle and indicated how it changed during the periods 1960-1976, 1976-1994 en 1994-2006.

With the Gross Domestic Product (GDP) as an indicator of the business cycle, the analysis identified the variables that showed the highest correlation with the GDP. During the periods 1976-1994 and 1994-2006, these included durable consumption, manufacturing investment, private sector investment, as well as investment in machinery and non-residential buildings. Other variables that also show a high correlation with the GDP are imports, non-durable consumption, investment in the financial services sector, investment by general government, as well as investment in residential buildings.

Prof. Burger’s analysis also shows that changes in durable consumption, investment in the manufacturing sector, investment in the private sector, as well as investment in non-residential buildings preceded changes in the GDP. If changes in a variable such as durable consumption precede changes in the GDP, it is an indication that durable consumption is one of the drivers of the business cycle. The up or down swing of durable consumption may, in other words, just as well contribute to an up or down swing in the business cycle.

A surprising finding of the analysis is the particularly strong role durable consumption has played in the business cycle since 1994. This finding is especially surprising due to the fact that durable consumption only constitutes about 12% of the total household consumption.

A further surprising finding is the particularly small role exports have been playing since 1960 as a driver of the business cycle. In South Africa it is still generally accepted that exports are one of the most important drivers of the business cycle. It is generally accepted that, should the business cycles of South Africa’s most important trade partners show an upward phase; these partners will purchase more from South Africa. This increase in exports will contribute to the South African economy moving upward. Prof. Burger’s analyses shows, however, that exports have generally never fulfil this role.

Over and above the identification of the drivers of the South African business cycle, Prof. Burger’s analysis also investigated the volatility of the business cycle.

When the periods 1976-1994 and 1994-2006 are compared, the analysis shows that the volatility of the business cycle has reduced since 1994 with more than half. The reduction in volatility can be traced to the reduction in the volatility of household consumption (especially durables and services), as well as a reduction in the volatility of investment in machinery, non-residential buildings and transport equipment. The last three coincide with the general reduction in the volatility of investment in the manufacturing sector. Investment in sectors such as electricity and transport (not to be confused with investment in transport equipment by various sectors) which are strongly dominated by the government, did not contribute to the decrease in volatility.

In his analysis, Prof. Burger supplies reasons for the reduction in volatility. One of the explanations is the reduction in the shocks affecting the economy – especially in the South African context. Another explanation is the application of an improved monetary policy by the South African Reserve Bank since the mid 1990’s. A third explanation is the better access to liquidity and credit since the mid 1990’s, which enables the better management of household finance and the absorption of financial shocks.

A further reason which contributed to the reduction in volatility in countries such as the United States of America’s business cycle is better inventory management. While the volatility of inventory in South Africa has also reduced there is, according to Prof. Burger, little proof that better inventory management contributed to the reduction in volatility of the GDP.

We use cookies to make interactions with our websites and services easy and meaningful. To better understand how they are used, read more about the UFS cookie policy. By continuing to use this site you are giving us your consent to do this.

Accept