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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

HEMIS training ‘shares insights across institutions’, says Prof Petersen
2017-08-22

 Description: HEMIS training ‘shares insights across institutions’ Tags: HEMIS training ‘shares insights across institutions’

UFS Rector and Vice-Chancellor Prof Francis Petersen
presents the welcoming address at the 2017 HEMIS Institute
in Bloemfontein.
Photo: Eugene Seegers

Higher education institutions such as universities need information and accurate data to make critically important management decisions. Prof Francis Petersen, Rector and Vice-Chancellor of the University of the Free State (UFS), expressed these sentiments during his introduction at the 2017 HEMIS Institute recently held in Bloemfontein.

Reporting a critical part of HE practice
The Department of Higher Education and Training (DHET) uses its Higher Education Management Information System (HEMIS) to manage and verify performance data from Higher Education Institutions (HEIs) regarding four crucial datasets, namely students, staff, space, and postdoctoral information and research fellows. HEMIS data is collected for quality control, funding, and planning purposes, in particular for steering the system and for monitoring the sector. This data must then be audited, since it is used for subsidy allocations to HEIs.

“Institutional reporting on aspects of what we do as public universities is a critical part of practice in Higher Education,” said Prof Petersen. He added, “Whether about insourcing statistics, … student accommodation, or transformation and indicators within that domain, it’s really all about accurate data with which informed, evidence-based decisions can be made. This HEMIS Institute 2017 ultimately enables us to share insights across institutions, which can grow and strengthen the sector as a whole.”

‘It’s about accurate data with
which informed decisions can
be made’—Prof Francis Petersen

Public and private HEIs attend training alongside government reps
The Institutional Information Systems Unit of the Directorate for Institutional Research and Academic Planning (DIRAP) hosted and presented the Southern African Association for Institutional Research (SAAIR) HEMIS Foundations workshop and the annual HEMIS Institute in Bloemfontein. These training opportunities were attended by university data managers and representatives from 26 public and private HEIs, as well as representatives from the Council on Higher Education (CHE), DHET, and the Namibian National Council for Higher Education (NCHE). The Foundations workshop was designed to assist those new to the platform to be better acquainted with this data management tool, while the two-day Institute was structured to answer complex questions and address issues around the use of the relevant reporting structures and software.

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