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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

UFS doctors fight childhood cancer
2016-09-02

Description: Childhood cancer  Tags: Childhood cancer

Prof David Stones and Dr Jan du Plessis of the
University of Free State’s paediatric oncology ward
are helping little lives, one patient at a time.
Photo: Nonsindiso Qwabe

Of 23 paediatric oncology specialists nationally, Prof David Stones and Dr Jan du Plessis of the University of Free State are the only ones in the province.

Committed to giving holistic care to their patients, the two doctors specialise in all types of childhood cancers, the most common being leukaemia, brain tumour, and nephroblastoma.

They describe the childhood malignancy as a lethal disease, unpredictability being its harshest trait. “With cancer, you can just never know. It precipitates and multiplies, and leads to the failure of other organs. You can just always hope, and keep trying,” said Du Plessis.

The paediatric oncology unit of the Universitas Academic Hospital, their unit, is the liveliest floor in the entire building. It is also the third busiest in South Africa, serving a demographic that spans the Free State and Northern Cape, as well as parts of North West, Eastern Cape and Lesotho.

Each year, the unit receives more than 100 new childhood cancer patients. In 2015, the unit had 113 newly diagnosed patients, an increase from 93 in 2014.

Lack of knowledge poses a serious challenge
According to the two experts, the lack of insight and awareness of the disease remain a big challenge to fighting it. “It is frustrating. Parents and family members don’t know anything about it. Nurses and doctors aren’t always clinically trained to pick up the early warning signs. By the time a diagnosis is made, life and death is on a 50% margin,” Stones said.

Poverty, a lack of resources, overcrowding and a range of health issues are other factors that have a profound effect on the diagnosis and treatment of the disease.

Making a contribution that will last
With a desire to see an improvement on life outcomes in the health sector, the team is focusing on educating the country’s doctors of tomorrow. Their unit is the only one in the country that actively involves medical students in an oncology unit, giving them practical experience and exposure to the individual cases each patient presents. They have also produced a substantial amount of research literature on childhood malignancies in South Africa as a developing country.

Driven by passion to see a better South Africa
The doctors are passionate about the work they do, and remain hopeful there will be a change in the incidence of childhood cancer   not just in decreased levels of the disease, but also in the overall state of well-being of young South Africans.

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