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12 October 2020 | Story Andre Damons
Prof Ivan Turok
Prof Ivan Turok, National Research Foundation research professor at the University of the Free State (UFS) and distinguished research fellow at the Human Sciences Research Council (HSRC).
New evidence provides a detailed picture of the extraordinary economic fallout from the COVID-19 pandemic. All regions lost about a fifth of their jobs between February-April, although the cities began to show signs of recovery with the easing of the lockdown to level 3. Half of all adults in rural areas were unemployed by June, compared with a third in the metros. So the crisis has amplified pre-existing disparities between cities and rural areas.

Prof Ivan Turok, National Research Foundation research professor at the University of the Free State (UFS) and distinguished research fellow at the Human Sciences Research Council (HSRC), and Dr Justin Visagie, a research specialist with the HSRC, analysed the impact of the crisis on different locations in a research report (Visagie & Turok 2020).

The main conclusion is that government responses need to be targeted more carefully to the distinctive challenges and opportunities of different places. A uniform, nationwide approach that treats places equally will not narrow (or even maintain) the gaps between them, just as the blanket lockdown reflex had adverse unintended consequences for jobs and livelihoods.

According to the authors, the crisis has also enlarged the chasm between suburbs, townships and informal settlements within cities. More than a third of all shack dwellers (36%) lost their jobs between February and April, compared with a quarter (24%) in the townships and one in seven (14%) in the suburbs. These effects are unprecedented.

Government grants have helped to ameliorate hardship in poor communities, but premature withdrawal of temporary relief schemes would be a serious setback for people who have come to rely on these resources following the collapse of jobs, such as unemployed men.

Before COVID-19

In February 2020, the proportion of adults in paid employment in the metros was 57%. In smaller cities and towns it was 46% and in rural areas 42%. This was a big gap, reflecting the relatively fragile local economies outside the large cities.
Similar differences existed within urban areas. The proportion of adults living in the suburbs who were in paid employment was 58%. In the townships it was 51% and in peri-urban areas it was 45%.

These employment disparities were partly offset by cash transfers to alleviate poverty among children and pensioners. Social grants were the main source of income for more than half of rural households and were also important in townships and informal settlements, although not to the same extent as in rural areas.  

Despite the social grants, households in rural areas were still far more likely to run out of money to buy food than in the cities.

How did the lockdown affect jobs?

The hard lockdown haemorrhaged jobs and incomes everywhere. However, the effects were worse in some places than in others. Shack dwellers were particularly vulnerable to the level 5 lockdown and restrictions on informal enterprise. This magnified pre-existing divides between suburbs, townships and informal settlements within cities.
There appears to have been a slight recovery in the suburbs between April-June, mostly as a result of furloughed workers being brought back onto the payroll. Few new jobs were created. Other areas showed less signs of bouncing back.

Overall, the economic crisis has hit poor urban communities much harder than the suburbs, resulting in a rate of unemployment in June of 42-43% in townships and informal settlements compared with 24% in the suburbs. The collapse poses a massive challenge for the recovery, and requires the government to mobilise resources from the whole of society.


News Archive

Fight against Ebola virus requires more research
2014-10-22

 

Dr Abdon Atangana
Photo: Ifa Tshishonge
Dr Abdon Atangana, a postdoctoral researcher in the Institute for Groundwater Studies at the University of the Free State (UFS), wrote an article related to the Ebola virus: Modelling the Ebola haemorrhagic fever with the beta-derivative: Deathly infection disease in West African countries.

“The filoviruses belong to a virus family named filoviridae. This virus can cause unembellished haemorrhagic fever in humans and nonhuman monkeys. In literature, only two members of this virus family have been mentioned, namely the Marburg virus and the Ebola virus. However, so far only five species of the Ebola virus have been identified, including:  Ivory Coast, Sudan, Zaire, Reston and Bundibugyo.

“Among these families, the Ebola virus is the only member of the Zaire Ebola virus species and also the most dangerous, being responsible for the largest number of outbreaks.

“Ebola is an unusual, but fatal virus that causes bleeding inside and outside the body. As the virus spreads through the body, it damages the immune system and organs. Ultimately, it causes the blood-clotting levels in cells to drop. This leads to severe, uncontrollable bleeding.

Since all physical problems can be modelled via mathematical equation, Dr Atangana aimed in his research (the paper was published in BioMed Research International with impact factor 2.701) to analyse the spread of this deadly disease using mathematical equations. We shall propose a model underpinning the spread of this disease in a given Sub-Saharan African country,” he said.

The mathematical equations are used to predict the future behaviour of the disease, especially the spread of the disease among the targeted population. These mathematical equations are called differential equation and are only using the concept of rate of change over time.

However, there is several definitions for derivative, and the choice of the derivative used for such a model is very important, because the more accurate the model, the better results will be obtained.  The classical derivative describes the change of rate, but it is an approximation of the real velocity of the object under study. The beta derivative is the modification of the classical derivative that takes into account the time scale and also has a new parameter that can be considered as the fractional order.  

“I have used the beta derivative to model the spread of the fatal disease called Ebola, which has killed many people in the West African countries, including Nigeria, Sierra Leone, Guinea and Liberia, since December 2013,” he said.

The constructed mathematical equations were called Atangana’s Beta Ebola System of Equations (ABESE). “We did the investigation of the stable endemic points and presented the Eigen-Values using the Jacobian method. The homotopy decomposition method was used to solve the resulted system of equations. The convergence of the method was presented and some numerical simulations were done for different values of beta.

“The simulations showed that our model is more realistic for all betas less than 0.5.  The model revealed that, if there were no recovery precaution for a given population in a West African country, the entire population of that country would all die in a very short period of time, even if the total number of the infected population is very small.  In simple terms, the prediction revealed a fast spread of the virus among the targeted population. These results can be used to educate and inform people about the rapid spread of the deadly disease,” he said.

The spread of Ebola among people only occurs through direct contact with the blood or body fluids of a person after symptoms have developed. Body fluid that may contain the Ebola virus includes saliva, mucus, vomit, faeces, sweat, tears, breast milk, urine and semen. Entry points include the nose, mouth, eyes, open wounds, cuts and abrasions. Note should be taken that contact with objects contaminated by the virus, particularly needles and syringes, may also transmit the infection.

“Based on the predictions in this paper, we are calling on more research regarding this disease; in particular, we are calling on researchers to pay attention to finding an efficient cure or more effective prevention, to reduce the risk of contamination,” Dr Atangana said.


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