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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

Fire as a management tool questionable in arid and semi-arid grassland areas
2015-03-24

Wild fire in the grassland
Photo: Supplied


The influence of fire on the ecosystem in the higher rainfall ‘‘sour’’ grassland areas of southern Africa has been well established. However, less information is available for arid and semi-arid ‘‘sweet’’ grassland areas, says Prof Hennie Snyman, Professor in the Department of Animal, Wildlife, and Grassland Sciences, about his research on the short-term impact of fire on the productivity of grasslands in semi-arid areas.

Sour and sweet grassland areas can be defined as receiving either higher or lower than approximately 600 mm of rainfall respectively. In quantifying the short-term impact of fire on the productivity of grasslands in semi-arid areas, a South African case study (experimental plot data) was investigated.

“Burned grassland can take at least two full growing seasons to recover in terms of above- and below-ground plant production and of water-use efficiency (WUE). The initial advantage in quality (crude protein) accompanying fire does not neutralise the reduction in half of the above-ground production and poor WUE occurring in the first season following the fire.

“The below-ground growth is more sensitive to burning than above-ground growth. Seasonal above-ground production loss to fire, which is a function of the amount and distribution of rainfall, can vary between 238 and 444 kg ha -1 for semi-arid grasslands. The importance of correct timing in the utilisation of burned semi-arid grassland, with respect to sustained high production, cannot be overemphasised,” said Prof Snyman.

In arid and semi-arid grassland areas, fire as a management tool is questionable if there is no specific purpose for it, as it can increase ecological and financial risk management in the short term.

Prof Snyman said: “More research is needed to quantify the impact of runaway fires on both productivity and soil properties, in terms of different seasonal climatic variations. The information to date may already serve as valuable guidelines regarding grassland productivity losses in semi-arid areas. These results can also provide a guideline in claims arising from unforeseen fires, in which thousands of rands can be involved, and which are often based on unscientific evidence.”

For more information or enquiries contact news@ufs.ac.za

 

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