Latest News Archive

Please select Category, Year, and then Month to display items
Previous Archive
05 September 2022 | Story Andrè Damons | Photo Andrè Damons
Prof Abdon Atangana
Prof Abdon Atangana, Professor of Applied Mathematics in the Institute for Groundwater Studies (IGS) and a highly cited mathematician for the years 2019-2021, says existing mathematical models are used to first fit collected data and then predict future events. It is for this reason he introduced a new concept that can be used to test whether the spread will have one or several waves.

With a new outbreak of the Ebola Virus Disease (EVD) reported this year in Democratic Republic of the Congo (DRC) – the 14th EVD outbreak in the country – researchers at the University of the Free State (UFS) introduced a new concept that can be used to test whether the spread will have one or several waves. They believe the focus should be to identify the source or the hosts of this virus for it to be a complete eradication. 

According to the Centers for Disease Control and Prevention (CDC), the Ministry of Health in the Democratic Republic of the Congo (DRC) declared an outbreak of Ebola in Mbandaka health zone, Equateur Province on April 23, 2022. EVD, formerly known as Ebola haemorrhagic fever, is a severe, often fatal illness affecting humans and other primates. The virus is transmitted to people from wild animals (such as fruit bats, porcupines and non-human primates) and then spreads in the human population through direct contact with the blood, secretions, organs or other bodily fluids of infected people, and with surfaces and materials (e.g. bedding, clothing) contaminated with these fluids, according to the World Health Organisation (WHO).
 
Prof Abdon Atangana, Professor of Applied Mathematics in the Institute for Groundwater Studies (IGS), says existing mathematical models are used to first fit collected data and then predict future events. Predictions help lawmakers to take decisions that will help protect their citizens and their environments. The outbreaks of COVID-19 and other infectious diseases have exposed the weakness of these models as they failed to predict the number of waves and in several instances; they failed to predict accurately day-to-day new infections, daily deaths and recoveries.

Solving the challenges of the current models

In the case of COVID-19 in South Africa, it is predicted that the country had far more infections than what was recorded, which is due to challenges faced by the medical facilities, poverty, inequality, and other factors. With Ebola in the DRC, data recorded are not far from reality due to the nature of the virus and its symptoms. However, the predictions show although some measures have been put in place in DRC and other places where the Ebola virus spread, they will still face some challenges in the future, as the virus will continue to spread but may have less impact. 

“To solve the challenges with the current models, we suggested a new methodology. We suggested that each class should be divided into two subclasses (Detected and undetected) and we also suggested that rates of infection, recovery, death and vaccination classes should be a function of time not constant as suggested previously. These rates are obtained from what we called daily indicator functions. For example, an infection rate should be obtained from recorded data with the addition of an uncertain function that represents non-recorded data (Here more work is still to be done to get a better approximation).

“I introduced a new concept called strength number that can be used to test whether the spread will have one or several waves. The strength number is an accelerative force that helps to provide speed changes, thus if this number is less than zero we have deceleration, meaning there will be a decline in the number of infections. If the number is positive, we have acceleration, meaning we will have an increase in numbers. If the number is zero, the current situation will remain the same,” according to Prof Atangana. 

To provide better prediction, he continues, reliable data are first fitted with the suggested mathematical model. This helps them to know if their mathematical model is replicating the dynamic process of the spread. The next step is to predict future events, to do this, we create three sub-daily indicator functions (minimum, actual, and maximum). These will lead to three systems, the first system represents the worst-case scenario, the second is the actual scenario, and the last is a best-case scenario.

Virus will continue to spread but with less impact

Using this method, Prof Atangana, a highly cited mathematician for the years 2019-2021, says he and Dr Seda Igret Araz, postdoctoral student, were able to predict that, although some measures have been put in place in DRC and other places where the Ebola virus spreads, they will still face some challenges in the future as the virus will continue to spread but may have less impact. 

To properly achieve the conversion from observed facts into mathematical formulations and to address these limitations, he had to ask fundamental questions such as what is the rate of infection, what is the strength of the infection, what are the crossover patterns presented by the spread, how can day-to-day new infected numbers be predicted and what differential operator should be used to model a dynamic process followed by the spread?

This approach was tested for several infectious diseases where we present the case of Ebola in Congo and Covid-19 in South Africa.  

News Archive

Census 2011 overshadowed by vuvuzela announcements
2012-11-20

Mike Schüssler, economist
Photo: Hannes Pieterse
15 November 2012

Census 2011 contains good statistics but these are overshadowed by vuvuzela announcements and a selective approach, economist Mike Schüssler said at a presentation at the UFS.

“Why highlight one inequality and not another success factor? Is Government that negative about itself?” Mr Schüssler, owner of Economist.co.za, asked.

“Why is all the good news such as home ownership, water, lights, cars, cellphones, etc. put on the back burner? For example, we have more rooms than people in our primary residence. Data shows that a third of Africans have a second home. Why are some statistics that are racially based not made available, e.g. orphans? So are “bad” statistics not always presented?”

He highlighted statistics that did not get the necessary attention in the media. One such statistic is that black South Africans earn 46% of all income compared to 39% of whites. The census also showed that black South Africans fully own nearly ten times the amount of houses that whites do. Another statistic is that black South Africans are the only population group to have a younger median age. “This is against worldwide trends and in all likelihood has to do with AIDS. It is killing black South Africans more than other race groups.”

Mr Schüssler also gave insight into education. He said education does count when earnings are taken into account. “I could easily say that the average degree earns nearly five times more than a matric and the average matric earns twice the pay of a grade 11.”

He also mentioned that people lie in surveys. On the expenditure side he said, “People apparently do not admit that they gamble or drink or smoke when asked. They also do not eat out but when looking at industry and sector sales, this is exposed and the CPI is, for example, reweighted. They forget their food expenditure and brag about their cars. They seemingly spend massively on houses but little on maintenance. They spend more than they earn.”

“On income, the lie is that people forget or do not know the difference between gross and net salaries. People forget garnishee orders, loan repayments and certainly do not have an idea what companies pay on their behalf to pensions and medical aid. People want to keep getting social grants so they are more motivated to forget income. People are scared of taxes too so they lower income when asked. They spend more than they earn in many categories.”

On household assets Mr Schüssler said South Africans are asset rich but income poor. Over 8,3 million black African families stay in brick or concrete houses out of a total of 11,2 million total. About 4,9 million black families own their own home fully while only 502 000 whites do (fully paid off or nearly ten times more black families own their own homes fully). Just over 880 000 black South Africans are paying off their homes while 518 000 white families are.

Other interesting statistics are that 13,2 million people work, 22,5 million have bank accounts, 19,6 million have credit records. Thirty percent of households have cars, 90% of households have cellphones and 80% of households have TVs.
 

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