The Western Cape is likely to reach peak mortality rate between June 23 and July 7

Piet Streicher PhD Engineering


The Western Cape is approaching peak mortality rate for Covid-19 and should reach this point before July 7. This prediction is based on the most up to date information emerging in the Western Cape. Most countries follow a consistent trajectory which can be modelled mathematically and then used for short term predictions.

In this paper, Brazil was selected as an illustration of a country that is slightly ahead of South Africa. The same mathematical model was fitted to both Brazil and South Africa. Cumulative deaths and the peak mortality rate from Covid-19 can be modelled using the Gompertz curve. It is an S shaped curve that starts off with exponential growth, then slows down until the slope is more linear, then tapers off and levels out towards a ceiling level (asymptote).

The point with the steepest slope represents the peak mortality rate. Once this point is passed, less people will be dying each day and the hospital and ICU bed occupancy will decrease. From this turning point accurate predictions of total deaths from Covid-19 can be made.

As a sanity check, the predicted peak mortality rate for the Western Cape was compared to other regions in the world. The implications of the Western Cape experience for South African mortality predictions were considered.

Gompertz function

The Gompertz function is a sigmoid function which describes growth as being slowest at the start and end of a given time period. The right-hand or future value asymptote of the function is approached much more gradually by the curve than the left-hand or lower valued asymptote. The Gompertz function is widely used to model Covid-19 cases and deaths (Torrealba-Rodriguez et al., 2020) (Levitt et al. 2020).

• a is an asymptote, since
• b sets the displacement along the x-axis (translates the graph to the left or right). Symmetry is when b = log (2).
• c sets the growth rate (y scaling)
• e is Euler’s Number (e = 2.71828…)

Figure 1: Gompertz curves and Gompertz constants (Wikipedia)

The Gompertz curve was fitted to SA and Brazil data in Excel using the method described in this tutorial:

The constants a, b and c were then manually adjusted for a better fit.

Modelling mortalities in Brazil using the Gompertz curve

Brazil is approximately three weeks ahead of South Africa (SA) in terms of the Covid-19 pandemic. Brazil was used mainly to illustrate the trajectory of a country that is slightly ahead of SA.

Brazil was also chosen as it has several similarities to SA. Brazil has a similar age demographic and GDP per person to SA. Brazil has a warm climate with several large cities in the Southern Hemisphere which follow the same seasons as does SA. Note however that none of these parameters such as age demographics or climate were used in this analysis. This analysis is purely based on the trajectory of the cumulative deaths curve.

On June 9, Brazil had only tested 4 706 people per million. On the other hand SA had tested 16 335 per million. This could partly explain the higher case fatality rate (CFR) in Brazil of 5.2% as Brazil only tests those with severe Covid-19 symptoms. South Africa has a consistent CFR of 2.2%. Italy had a CFR of 8.6 % on March 20, almost 4 times higher compared to SA. Again, the CFR was not used in this analysis as this analysis only modelled fatalities.

From figure 2 we can see that Brazil has reached a peak mortality rate at 984 deaths per day on average. Note the large variation in daily figures, especially towards the peak. While the average peaked at 984 deaths per day, the highest number recorded in a single day was 1 491 on June 5. The lowest recent numbers were 355 on June 9 and 480 on June 1.

Figure 2: Modelling deaths in Brazil using Gompertz curves.

Projecting the Gompertz curve into the future predicts 82 000 deaths for Brazil after 180 days. Brazil is a country with a population of 210 million people. This works out to a peak mortality rate of 4.7 deaths per million per day and a total death toll of 39 people per million.

Modelling mortalities in the Western Cape using the Gompertz curve

Two Gompertz curves where fitted to the Western Cape data, one conservative upper bound curve and another optimistic lower bound curve (figure 3).

Figure 3: Conservative and optimistic curves fitted to WC actual data.

From figure 4 cumulative deaths follow a relatively tight envelope that can be used for short term projections.

From figure 5 and table 1 we can see that the Western Cape could reach a peak mortality rate between June 23 and July 7 at a level of between 44 and 59 deaths per day. Projecting the Gompertz curve into the future predicts between 3 500 and 5 000 deaths for the Western Cape after 180 days (April to September).

For any one day the daily deaths number can be significantly above or below the curve as a death can only be counted once the positive Covid-19 test result is recorded (figure 5). Currently there is a 5day delay in test results on average. Recent numbers include 15 deaths recorded on June 6 and 75 deaths recorded on June 7.

Figure 4: Zooming in on the last few days – 927 deaths on June 11.

Figure 5: First derivative of Gompertz curves with actual daily deaths.

(Figure 5 update on 11 September.)

The Western Cape has a population of 5.81 million people. This works out to a peak mortality rate of 7.6 to 9.8 deaths per million per day and a total predicted death toll of 603 to 837 people per million. Note that the Western Cape is the worst affected region in South Africa and should rather be compared to similar regions in the world. The worst affected region in the world is New York State which is likely to end up with 1 800 deaths per million after 180 days, 2-3 times higher compared to this prediction for the Western Cape. This comparison is made as a sanity check. Any analysis that predicts a higher death toll compared to New York would need immediate scrutiny.

Table 1: Gompertz optimistic and conservative predictions for Rt=1 for deaths.

Hospital beds and ICU beds also reaching a peak

Figure 6: Hospital beds and ICU beds in the Western Cape

The increase in hospital beds and ICU beds occupied in the Western Cape has already slowed down significantly and will start to decrease around the same time or before a peak mortality rate is reached (see figure 6). The Western Cape is highly unlikely to ever need more than 300 ICU beds and 1 700 hospital beds. The original predictions by SACMC of 3 000 to 6 000 ICU beds needed, and 8 500 to 17 500 hospital beds needed in the Western Cape was out by an order of magnitude.

Total mortality predictions for South Africa

My initial prediction on May 14 of the total mortality for South Africa (SA) was 11 000 deaths (Streicher, 2020). This prediction was based on the experience of Italy with adjustments made based on the age demographic differences between the countries. It assumed a similar level of infection and similar risk for similar ages. The adjustment for our younger age was validated by comparing the CFR of each country.

The Western Cape makes up 10% of the population of the country. The above prediction of 11 000 would therefore translate to a prediction of 1 100 deaths in the Western Cape. This prediction is now clearly an underestimate, with 927 deaths on June 11 and the daily deaths at 41 and increasing. This paper predicts a total death toll for the Western Cape of 3 500 to 5000.

Each country has a hotspot with a higher death toll and normally this hotspot reaches a peak mortality rate first. The Western Cape has a CFR of 2.5% while the rest of the country has a CFR of 1.6%. In the Western Cape 0.6% of the total population have already tested positive, while only 0.04% have tested positive in the rest of the country. The low CFR and infection rate for the rest of the country might well indicate an increased resistance to Covid-19. This could potentially be explained by the large number of people in SA living at altitude (> 1 000 m). Gauteng has 12 million people most of which live between 1 300 and 1 700 m above sea level. Gauteng is the largest metro in SA and includes almost half of the urbanised population of SA. Studies have shown that altitude reduces the risk of Covid-19 (Arias-Reyes et al, 2020).

It is therefore plausible that the rest of South Africa could have significantly lower fatalities from Covid-19. At this stage there are no indications that the original prediction of 11 000 deaths for SA (Streicher, 2020) should be significantly adjusted.

  • July 30 update! At this stage the best estimate for total deaths for SA is 19 000 deaths. The best estimate for the Western Cape is 4 000 deaths.


The Western Cape will reach a peak mortality rate between June 23 and July 7 at a level of between 44 and 59 deaths per day on average. This trend is mirrored by hospital beds and ICU beds occupied. Once the Western Cape reaches a peak mortality rate it becomes possible to do an accurate prediction of the total deaths expected in the Western Cape. At this stage the prediction is between 3 500 and 5 000 deaths between April and September.

The total Covid-19 deaths for SA predicted at 11 000 (Streicher, 2020) and at 10 000 by Panda (Davis, 2020) remains a reasonable number based on the low infection rate and low CFR for the rest of the country.

  • July 30 update! At this stage the best estimate for total deaths for SA is 19 000 deaths. The best estimate for the Western Cape is 4 000 deaths.


There are always uncertainties in predicting the future. This analysis is a best attempt and may contain errors. Please notify the author if any error is spotted and it will be corrected. For more regular updates, follow


Thanks to the Western Cape Department of Health in answering all my questions. Thanks to Panda (Pandemic Data and Analysis – for all the input provided to this paper. The views expressed in this paper are my own and are not necessarily supported by these parties.


Arias-Reyes, C., Zubieta-DeUrioste, N., Poma-Machicao, L,,Aliaga-Raduan, F.,Carvajal-Rodriguez, F., Dutschmann, M., Schneider-Gasser, E.M., Zubieta-Calleja, G., Soliz, J., 2020. Does the pathogenesis of SARS-CoV-2 virus decrease at high-altitude? Respiratory Physiology & Neurobiology
Volume 277, June 2020.

Davis, R. 2020. Government projections on SA Covid-19 fatalities questioned by private research group. Daily Maverick 3 June 2020.

Gompertz function – Wikipedia:

Gompertz method in Excel – tutorial:

Levitt M., Scaiewicz A.,1, Zonta F., June 2020. Predicting the Trajectory of Any COVID19 Epidemic From the Best Straight Line. medRxiv preprint doi:

Streicher, P.E. 2020, Covid-19 A call for smart mitigation measures considering the reduced risk for South Africa from Covid-19. Blog article:

Torrealba-Rodriguez O., Conde-Gutiérrez R.A., Hernández-Javier A.L., May 2020. Modeling and prediction of COVID-19 in Mexico applying mathematical and computational models. Chaos, Solitons and Fractals 138 (2020). Elsevier.

Western Cape Covid-19 dashboard

Worldometer – Covid-19 data source:


The rate of increase in Covid-19 cases in the Western Cape is starting to slow down – v.4 31/5

Piet Streicher PhD Engineering


As this post reaches its fourth revision, it is becoming increasingly information-dense, based on criticism received from, amongst others, Dr Harry Moultrie of the National Institute for Communicable Diseases (who remains fiercely critical).

There are strong indications that daily new Covid-19 cases in the Western Cape are not increasing exponentially anymore. Hospitalisations, ICU beds and deaths are all starting to fall behind what used to be a very consistent 8% growth per day. Based on the experience of most countries worldwide this slowdown is to be expected. Within the next few days the certainty of a slowdown is likely to become unquestionable. The implication of a slowdown now would be an irrefutable confirmation that the official Covid-19 models for South Africa grossly overestimated the peak for the Western Cape and for the rest of the country. Decisions based on these models would then need immediate revision.

The analysis and interpretation of the data is complicated by a growing backlog in tests, the cessation of community screening and testing (CST) and constraints on ICU staff available. My analysis is based on the information provided by the government. I make several observations and provide my best attempt to interpret this data. I make five arguments in support of my hypothesis that the rate of increase in Covid-19 cases is slowing down. No single argument is conclusive, however looking at all five arguments as a whole I conclude that there are strong signs that my hypothesis is correct.

By making this analysis available in the public domain, the intention is to improve our collective understanding of the problem. I welcome arguments against my hypothesis.

Most countries slow down earlier than expected

Almost all countries change from exponential growth to a more linear pattern much earlier than originally predicted. Figure 1 shows the worst 25 countries (of 10 million people or more). The blue line is for Belgium which exhibited this change on day 25 at which point 160 new cases per 1 million people were recorded. The level at which this change occurs appears to be independent to the level of lockdown measures employed.

Figure 1: Belgium new confirmed Covid-19 cases per day normalized by population (source:

On a regional basis, this turning point might happen at a higher level, but still well below original predictions used in the official SA Covid-19 models. Figure 2 shows the same information for all states in the USA. The blue line is for New York State which exhibited this change between day 16 and 35 at which point (day 35) 551 new cases per 1 million people were recorded.

Figure 2: New York State new confirmed Covid-19 cases per day normalized by population (source:

The Western Cape is expected to slow down

The Western Cape has a population of 5.8 million people. 921 confirmed new cases as on May 16 equates to 159 new cases per million people per day. This is a level where we can expect a slowdown.

Argument 1 – Confirmed Covid-19 cases by date of test result are not growing exponentially anymore

Figure 3: Western Cape new daily confirmed Covid-19 cases.

Looking at Figure 3 there appears to be a slowdown after May 19. A growth projection of 8% per day which the Western Cape has consistently followed from May 1 to May 18 would have projected an average of 1 836 new cases for the last 3 days of May. The actual average for the last 3 days of May were 1 272.

The counter argument to this observation is that trends in confirmed cases do not always correspond to trends in actual cases and this could be caused by a testing backlog or by the cessation in community screening and tests (CST). A testing backlog is likely to build up gradually and would not explain the change around May 19. This hypothesis is confirmed by my next argument.

Argument 2 – Number of confirmed cases plotted by date of sample taken is not growing exponentially anymore

Figure 4: Western Cape confirmed cases by both sample date and by date of completion.

Figure 4 shows the confirmed cases by date of sample (green and orange lines). The move away from exponential growth on May 15 is clearly evident and the updates to this data received on May 31 did not change the trend already observed on May 27. The data also indicates that the average time from sample taken to test completion is around 5 days. While the green line might move up marginally over the next day or two, the slope of the green line is unlikely to change.

Argument 3 – Daily new tests completed are not increasing exponentially

Figure 5: New tests completed each day.

Another indication of a possible slowdown could include a slowdown in tests done, as this might be driven by the number of people that present symptoms. However, testing capacity constraints would also lead to a slowdown in tests done and this might result in the finding of less active cases. There are testing capacity constraints and this has resulted in a significant reduction in CST tests. Note however that CST testing made up less than 17% of all testing at the peak according to the Western Cape Department of Health. Testing continues to increase, although not at the same rate as new cases. As tests are rationed, we can expect the test positive percentage to increase as tests are limited to only those that clearly show Covid-19 like symptoms. We see this increase countrywide, except in the Western Cape.

Argument 4 – The test positive % over total tests stopped increasing exponentially

Figure 6: Confirmed cases as a percentage of new tests completed each day.

The confirmed cases as a percentage of tests done increased very consistently at 8% per day up until May 19 after which this trend stopped (figure 6). This is another strong indication of a slowdown and this metric can be used even when there is a testing capacity constraint.

Argument 5 – ICU beds occupied and deaths have stopped growing exponentially

Figure 7: ICU beds and deaths

ICU beds occupied stopped growing exponentially on May 22 and has grown marginally at 3-4 beds per day since. There are staff constraints in terms of ICU beds that could explain a portion of this trend.

If this trend is an indication of a reduction in the rate of increase in the demand for ICU beds, we can expect a change in deaths from exponential to linear growth 9 days later. While deaths have tracked the 8% a day projection almost exactly for more than 21 days already, by the 31st where we expected to see 38 deaths, only 16 were recorded. At this stage it is only one data point, but this aspect will be tracked closely in the following days.

SA Covid-19 Modelling Consortium predictions

The South African COVID-19 Modelling Consortium is group of researchers from academic, non-profit, and government institutions across South Africa. The group is coordinated by the National Institute for Communicable Diseases, on behalf of the National Department of Health. This group published a report on 6 May titled: Estimating cases for COVID-19 in South Africa, Long-term provincial projections.

Figure 8: Western Cape projections from the official SA Covid-19 consortium model.

Before discussing the predictions made by the consortium it is important to understand some definitions:

Symptomatic cases: those with Covid-19 that exhibit symptoms whether mild or severe and include those tested positive and those not tested.
Confirmed cases: those that have tested positive for Covid-19.

The consortium stated that all hospitalised and severe cases will be tested but only 1 in 4 mildly symptomatic cases will be tested. Since less than 10% of confirmed cases are hospitalised in SA, we can approximate confirmed cases as 1/4 of symptomatic cases.

Unfortunately the consortium does not show projections for confirmed cases, even though the data released every day by the NICD and by the entire world refers to confirmed cases. This causes unnecessary confusion amongst the general public. It is absolutely critical that projected numbers for confirmed cases are also shown, as then everyone will be able to compare the model to the daily numbers released by the NICD.

The consortium published a model of symptomatic active Covid-19 cases per province over the next 9 months. It peaks at 100 000 – 220 000 symptomatic active Covid-19 cases for the Western Cape (Figure 8). This translates to 25 000 – 55 000 active confirmed cases for the Western Cape. This is slightly over double my prediction of 10 000 – 20 000 active confirmed cases. To put this in perspective, 25 000 – 55 000 active confirmed cases for the Western Cape would be 4 300 – 9 500 active cases per million people. This will be worse than Belgium (2 900), UK (3600) or the USA (3800), which are the three worst countries in the world ito of this number. It will be better than New York State (14000), the worst region in the world ito this number.

The consortium reports that for 100 000 – 220 000 symptomatic active cases (25 000 – 55 000 confirmed active cases), the Western Cape would need 8 000-17 500 hospital beds and 3 000 – 6 000 ICU beds.

This is a ratio of 9 : 4 : 1 for active confirmed : total hospital beds : ICU beds.
Currently the Western Cape exhibits a ratio of 35 : 4 : 1 . (Note that ICU beds occupied today relate to active confirmed cases 1-2 weeks ago).

Is it possible that the consortium made a mistake in conflating symptomatic with confirmed cases? Did they apply a ratio of 35 : 4 : 1 to the 100 000 – 220 000 active symptomatic cases instead of applying this ratio to the 25 000 – 55 000 active confirmed cases?

A more realistic projection for all hospitals in the Western Cape will be the following:

A maximum of 10 000 to 20 000 active confirmed cases in the entire Western Cape (on 31/5 it was at 10 004).

900 – 1500 in hospital 1-2 weeks after reaching peak infection rate

300 – 600 in ICU 2-3 weeks after reaching the peak infection rate

* 900 – 1 300 total deaths from Covid-19 in the Western Cape from April to September.

* 6/6 revision – the estimate is now 2 000 – 4 000 deaths for the Western Cape.
* 12/6 revision – 3500-5000 deaths (see next article).

These predictions are in line with predictions independently made by Pandemic Data and Analytics (Panda), a multidisciplinary initiative co-ordinated by actuary Nick Hudson.


Indications from new daily confirmed Covid-19 cases by test completion date, cases by test sample date, positive tests as a percentage of daily tests completed and ICU beds occupied all indicate that the rate of increase in new Covid-19 infections in the Western Cape is starting to slow down. Confirmed cases by sample date stopped growing exponentially on May 16, ICU beds stopped growing exponentially on May 22 (6 days later) and deaths stopped growing exponentially on May 31 (15 days later). Based on the experience of most countries worldwide this slowdown is to be expected. It is becoming very clear now that the official Covid-19 models for South Africa grossly overestimated the peak for the Western Cape. Decisions based on these models need urgent revision.

For more regular updates on how this data unfolds, follow:


My calculations and assumptions may contain errors. The purpose of the article is to make a contribution in addressing the pandemic. I hope my analysis will aid the various Covid-19 modelling teams, the journalists that track the problem, the politicians that have to make very difficult decisions and the general public. If an error is spotted, please comment on the article and I will correct it. Note that a slowdown in the rate of increase in confirmed Covid-19 cases is not an indication that Covid-19 should not be taken seriously.


I would like to acknowledge the input provided by the following organisations and individuals:

Various members of Pandemic Data and Analytics (Panda) provided invaluable input. Panda is a multidisciplinary initiative co-ordinated by actuary Nick Hudson.

Critical input was provided by Dr. Harry Moultrie NICD (who remains fiercely critical).

The Western Cape Department of Health answered multiple questions around testing processes, testing constraints and ICU staffing constraints.

Acknowledging all these parties is no indication that my analysis is supported by them, in fact the last two remain fiercely critical.


SA Covid-19 Modelling Consortium report from 6 May:
Estimating cases for Covid-19 in South Africa – Long-term provincial projections. Report Update: 6 May 2020. Prepared by MASHA, HE2RO and SACEMA on behalf of the South African COVID-19 Modelling Consortium

Nick Hudson et al, 2020. The mortality and economic effects of Covid-19: Datasets for decision making. Published in Terry Bell, May 5, 2020. Actuaries warn Ramaphosa of a ‘humanitarian disaster to dwarf Covid-19′ if restrictive lockdown is not lifted. Daily Maverick.

Supplementary data:

27/5 update: The R0-number is dropping consistently for the Western Cape and is down to 1.13. R0 is calculated by taking the average confirmed active cases over the last 7 days and dividing this by the average active cases over the last 14 days. If R drops below 1.0, the active cases reached a turning point 7 days previously.

Figure 9: R0-number for Western Cape

Table 1: Western Cape projections and actual data (own analysis).

SA Covid-19 models may grossly overestimate the number of active cases

Piet Streicher PhD Engineering

News24 published a model provided by MediClinic of known active Covid-19 cases per province over the next 9 months. It peaks at 200 000 known active Covid-19 cases for the Western Cape. This scenario is highly unlikely and grossly overestimates Covid-19 cases. 200 000 known active cases in 5.283 million people equates to 34 000 per 1 million people. The worst case countries are the USA at 3 500 / million, UK 3 200 / million and Belgium at 2 800 / million. All three these countries are already well past the peak rate of infections.

Figure 1: Mediclinic model of known active Covid-19 cases by province.

I would like to hear from MediClinic why they expect the Western Cape to peak at a number 10 times higher compared to the worst we have seen in the world? If the MediClinic number of 200 000 only applies to the 17% with health insurance, then I believe they are overestimating the number by a factor of 60+. It is likely that the models do not take account of the number of total infections which could be 10 – 20 times higher than the known infections i.e. the models may have assumed known cases equal all infections. This would be a mistake.

Figure 2: Belgium active cases normalised by population.

Below find the three countries with the highest number of active cases by population:
The USA is at 3 482 active cases / million people.
The UK is at 3 215 active cases / million people.
Belgium is at 2 776 active cases / million people (figure 2).

On a regional basis, New York State has the highest number of active cases at 14 009 / million people. This indicates that the number of active cases by population in a region could significantly exceed the number of active cases by population in a country. The number for New York is 2.5 times lower compared to the number used for the Western Cape in the Mediclinic model.

Figure 3: Belgium new Covid-19 cases by day normalised for population (

Belgium, UK and USA are all well past their peak infection rate. The peak infection rate is important as it determines the peak requirement for hospital and ICU beds.

News 24 reports that Mediclinic expects that for 200 000 active cases, they would need 40 000 hospital beds and 10 000 ICU beds. This is a ratio of 20 to 4 to 1. However, we currently have 6 195 active cases in the Western Cape of which 424 are in hospital and 111 are in ICU. This is a ratio of 56 to 4 to 1.

A more realistic projection of the worse case for all hospitals in the Western Cape will be the following:

20 000 active cases in the entire Western Cape when we reach the peak infection rate

1 429 in hospital when we reach peak infection rate

357 in ICU when we reach peak infection rate

If private hospitals only treat the insured and assuming only 17% are insured, that would equate to 61 people in ICU in private hospitals in the Western Cape at the peak. The 357 ICU number is 28 times lower than 10 000 and the 61 ICU number is 164 times lower.

My analysis and projections above are based on the scenario where our active cases are similar to those of the worst performing countries. The reality is that South Africa has significantly slower infection rates compared to Belgium for instance (see figures 3 and 4).

Figure 4: South Africa new Covid-19 cases by day normalised for population (

In conclusion, the Mediclinic model published by News24 on 20 May grossly overestimates the peak number of active Covid-19 cases in the Western Cape. My analysis assumes that our mitigation measures are no worse than the worst in the world currently. Initial indications are that our mitigation measures are significantly more effective compared to the worst 25 countries worldwide.


My calculations and assumptions may contain errors. This article has not been reviewed by experts. The purpose of the article is to help the media, decision makers and the general public to ask the right questions. If an error is spotted, please comment on the article and I will correct this.


Balancing the health risks and benefits of lockdown measures

Piet Streicher PhD Engineering

South Africa is in a unique position where we must weigh up serious health risks exacerbated by lockdown measures with the protective factors of lockdown measures in terms of Covid-19. We cannot solely rely on WHO guidelines. The WHO guidelines for relaxing lockdown measures do not consider the negative consequences associated with them. Flattening the curve should not be a goal, but a strategy to balance risks. One strategy would be to flatten the curve sufficiently so that the healthcare system is able to cope. The curve should not be flattened any more than this level as it would then unnecessarily prolong the pandemic. A flatter curve implies having lockdown measures for a longer period which would exacerbate the health risks introducted by lockdown measures.

Figure 1: Flattening the curve also prolongs the pandemic. We are at an early stage of the pandemic.

The predicted deaths from Covid-19 need to be compared to other life-threatening factors. South Africa is likely to have approximately 11 000 deaths from Covid-19 from April to September 2020 (own analysis: According to figures from the UNAids website, South Africa lost 71 000 lives to HIV in 2018.

Dr Glenda Gray a member of the Ministerial Advisory Committee (MAC) and chairperson of the South African Medical Research Council (SAMRC) has questioned the lockdown strategy of the government based on risks around malnutrition and missed appointments by HIV patients. Gray told News24 in her personal capacity, “We are seeing children with malnutrition for the first time [at Chris Hani Baragwanath Academic Hospital]. We have not seen malnutrition for decades and so we are seeing it for the first time in the hospital.”

In an interview with News24 late on 16 May 2020, Health Minister Zweli Mkhize defended the government’s lockdown strategy and said no region or district in the country can claim to conform to the World Health Organisations’ six criteria to have restrictions lifted. He called the criticism by Dr Gray “unfair”.

The World Health Organization (WHO) has set out six criteria to help countries decide when to lift lockdowns. WHO said that countries should look at lifting the lockdowns when:

  • transmission is controlled
  • additional capacity in the health system is in place to trace, treat and isolate patients
  • outbreaks risks are minimized in special settings like nursing homes
  • preventive measures are in place like schools and work places
  • importation risks can be managed
  • communities are engaged to adjust to the new norm.

Dr Mkhize is correct in saying that not a single district in SA conforms to all six of these criteria. However, we can also state that not a single item on the WHO list considers the downsides of lockdown measures. If a country is overly strict in its lockdown measures the WHO criteria do not indicate this. For instance, the six WHO criteria do not consider a possibility wherein 10 000 people are in hospital with malnutrition and hundreds are dying from hunger.

Dr Mkhize is correct in stating that we cannot lift the lockdown measures abruptly and must do this in a phased approach. It is counterproductive to attack the government in a personal way when governments worldwide are doing the same. It is much more productive to understand the reasons for the decisions made and then to challenge the goals behind those reasons.

Dr Mkhize is not correct in silencing medical experts. Medical experts have a moral duty to inform the government and the public at large of the health risks related to the disease and the health risks introduced by the lockdown measures. Missed appointments by HIV patients and malnutrition admissions at hospitals as a result of lockdown measures are causes for concern.

It is critical that the government balances both the up and downsides of lockdown measures. These are difficult decisions to make. It is possible to make mistakes either way. It will help the government to clearly state all the downsides of lockdown measures to assure the general public that the government is indeed balancing all the relevant factors. This will go a long way in restoring trust with the general public.


Pegden, W. Chikina, M. 2020. A call to honesty in pandemic modelling.

In South Africa in 2018:

  • 71 000 people died from an AIDS-related illness.

Covid-19: A call for smart mitigation measures considering the reduced risk for South Africa from Covid-19 – version 2


Piet Streicher PhD Engineering

South Africa is likely to have 4 times fewer deaths compared to Italy due to our younger population. In Italy, 22% of all people are over 65 years of age, and this age group represented 91% of all Covid-19 deaths. In South Africa, only 5% of all people are over 65 years of age. This factor alone could result in South Africa having almost 4 times fewer deaths from Covid-19 (assuming all other factors stay the same).

Initially it was feared that HIV and TB would significantly increase the death rate in SA. At this stage there is no evidence that this factor would be nearly as significant as the age factor for the country as a whole.

The initial exponential growth of Covid-19 in most countries has lead to predictions of large numbers of deaths of between 0.5% and 1% of entire populations. It is now clear that this exponential growth stops much earlier than expected for all countries and that the total deaths within entire populations are more likely to be between 0.005% and 0.15%.

This significantly reduced risk requires a re-evaluation of all mitigation measures. Mitigation measures in itself pose a significant risk to lives. There are sufficient mitigation measures available to South Africa that would pose a negligible risk to lives.

Predicting the total deaths expected in Italy

The daily new Covid-19 cases and new deaths in Italy has been dropping consistently and predictably for the last 40 days (since late March). It is possible to predict the total number of deaths from start to finish with some certainty now.

Figure 1: Plotting the daily new Covid-19 cases and new deaths from late March.

An exponential curve was fitted to the daily new deaths (from 27 March onwards) and this curve was used to project future deaths up to 190 days from the start of the first death. The projected numbers were then summed and added to the current total deaths. As on 5 May the total deaths for Italy from start to 190 days was estimated at 39 328.

The only uncertainty is what effect the lifting of the lockdown will have on the above curve. Italy has started to lift several lockdown measures and so far, there is no indication of a resurgence. Countries without strict lockdown measures such as Sweden also has a drop-off as Italy has, although at a slower rate. In Sweden all schools and most businesses are open, only social distancing and hand hygiene is required and mass gatherings are prevented.

Deaths in Italy by age

When looking at the distribution of deaths by age in Italy it is noticeable that there are very few deaths for people aged 0-49 years. Children particularly are not vulnerable at all. However, for people over 60 years of age, the risk goes up significantly.

Figure 2: Deaths by age category in Italy (source: Statista)

In the USA, 8 out of 10 deaths reported have been in adults 65 years old and older (CDC).

Italy and South African demographics

Italy has a much older population compared to South Africa.

Figure 3: Demographics of Italy and South Africa (source: )

Predicting deaths in SA based on data from Italy

If we asume we have the same number of infections in SA as Italy and we assume a South African of a certain age carries the same risk as an Italian of the same age, we can predict the total deaths expected in SA.

Table 1: Calculation hypothetical deaths in SA based on Italy experience and demographic data (own analysis).

Currently my prediction is 10 886 Covid-19 deaths for South Africa for the March-August period. Note that this is 30 times less compared to some initial predictions.

SA had 12 074 known Covid-19 cases and 238 deaths on 14 May. When Italy had 12 462 known cases, they had 827 deaths i.e. a 3.5 times higher death toll. SA had 17 200 known Covid-19 cases and 312 deaths on 19 May. When Italy had 17 660 known cases, they had 1 266 deaths i.e. a 4.1 times higher death toll. It is already clear that our death rate is significantly lower compared to Italy. At this stage there is no indication that HIV and TB will wipe out the age benefit that SA has over Italy.

The exponential growth stops much earlier than initially expected

Most countries see a dramatic change from exponential to linear growth when between 20 and 100 daily new Covid-19 cases for every 1 000 000 people are reached. After this slowdown, daily new cases slowly start to decrease each day.

The implication for South Africa is that we can expect to peak at between 2 000 and 6 000 new Covid-19 cases per day. We could reach this level by mid to late June already and not August/September stated in the media currently. This peak rate is significantly lower compared to the initial models used in SA used to predict hospital beds and ICU beds required. These predictions all need revision, as this was the primary basis for our strict lockdown measures.

Figure 4: Change from exponential to linear growth of top 25 countries (source:

A call for smart mitigation measures

The reduced risk for SA implies that we need to rethink our restrictive lockdown measures. Lockdown measures carry a significant risk to lives as shown by the analysis of a team of actuaries lead by Nick Hudson (Daily Maverick, 5 May). Dr. Glenda Gray reported that malnutrition cases are seen in Baragwanath Hospital for the first time in more than a decade (News24, 16 May). There is little evidence from the 22 April government report “Risk adjusted strategy for economic activity” that the costs of lockdown measures in terms of human lives were modelled.

We need smart and sustainable mitigation measures. People will obey rules that clearly mitigate the disease. People get upset about arbitrary rules that do not mitigate the disease. When there are too many arbitrary rules, people stop following all rules, including those that are needed. It is therefore critical that mitigation measures are seen as necessary. All jobs that feed a family should be regarded as essential. When you prevent a free market, you create a black market. When you introduce too many regulations, people lose respect for all laws.

The measures with the highest benefit and lowest cost include:

  • Rigorous testing regardless of symptoms.
  • Contact tracing and quarantine of those infected.
  • Prevention of mass gatherings: Events where people shout or sing together carry a significant risk.
  • Encourage physical distancing and hand hygiene.
  • Wearing of masks by all in high risk areas such as public transport, shops and certain work environments, but not during solitary activities (to prevent prolonged wear risks).
  • Protection of all vulnerable people such as the elderly.

Measures with a marginal benefit but the highest cost include:

  • Preventing people from working.
  • Preventing people from doing the solitary leisure activity or exercise of their choice.


In conclusion, our Government should carefully reconsider the costs of mitigation measures considering the reduced number of deaths expected in this analysis. The economic impact of lockdown measures also poses a risk to lives. People need money to buy food and if they don’t eat, they also die. We are already starting to see malnutrition cases in our hospitals. These are difficult decisions to make and I wish our Government all the wisdom in this regard.


Nick Hudson et al, 2020. The mortality and economic effects of Covid-19: Datasets for decision making. Published in Terry Bell, May 5, 2020. Actuaries warn Ramaphosa of a ‘humanitarian disaster to dwarf Covid-19′ if restrictive lockdown is not lifted. Daily Maverick.

Covid-19: Reasons for everyone to wear homemade face masks in public and in the care situation amid the Covid-19 pandemic lockdown

Pieter E. Streicher PhD Engineering


The spread of the Covid-19 virus will slow down considerably if everyone wears homemade face masks when going out in public or when caring for a potential Covid-19 positive relative in a household. Homemade face masks will prevent asymptomatic and pre-symptomatic people from spreading droplets containing the virus. When you speak, there are always droplets coming out of your mouth (Cohen, 2020).

This intervention was implemented by the Czech Republic since 18 March 2020 and should be followed closely. This measure should not replace other physical distancing, hand washing, isolation or lockdown measures.

There are several misconceptions concerning face masks amongst the public and the media that might put everyone at higher risk. Some of these misconceptions are well intended in that they aim to maximise the availability of medical masks for medical personnel. Then there is the observation that non-medical people do not know how to wear and handle face masks, putting themselves at higher risk. These factors result in the public being actively discouraged to wear all types of masks. This is a fatal mistake.

By limiting medical face masks to the exclusive use by medical personnel, the availability problem is addressed.

While non-medical people currently do not know how to wear and handle homemade face masks, they can learn to do so. We will soon have a situation where an increasing number of non-medical people would have to care for Covid-19 positive individuals in their households and the skilling up of everyone in the best practices is imperative.

The primary benefit of wearing a homemade facemask is the protection of others. The secondary benefit is the protection of yourself. My mask protects you, your mask protects me.

The virus is spread while people are asymptomatic

We should all consider the possibility that we might already be infected, but are not showing any symptoms, and that we might actively be infecting others (Pueyo, 2020). Not only when you cough or sneeze, but simply when you are talking there are always droplets coming out of your mouth. These droplets then contaminate all surfaces they land on. Advising that masks should only be worn by those with the virus is not helpful, as you might not know if you are infected or not.

The main route of contamination with the Covid-19 virus

People are most likely to contract the virus by touching a contaminated surface and then touching their face. For this reason, people are well-advised to regularly wash their hands. However, each person should consider the likelihood of touching potentially contaminated surfaces when going out in public and then inadvertently touching their faces. Any barrier between your hands and your face will have a benefit according to David Price (2020), ICU Pulmonologist currently actively treating Covid-19 patients. Contracting the virus from the air is possible but unlikely. Normal medical masks would not protect against airborne viruses either, and in hospitals with Covid-19 cases, only personnel performing procedures such as intubation or those who are in close contact with coughing patients are at risk of contracting the virus via the air, and they should wear N95 masks (Price, 2020). Wearing a homemade face mask and keeping a two-meter physical distance from others in public is effective in slowing down contamination.

Countries able to contain the virus

Hong Kong, Singapore, Japan and Taiwan have all been able to contain the virus. In all these countries, people have been wearing face masks for weeks now (Myburgh, 2020). The public Covid-19 posters in Hong Kong prominently display the use of face masks when in public. Taiwan is producing 10 000 000 masks per workday. Taiwan does not have enough cases to appear on the graph below. This measure was not adopted in Italy, USA, Germany, Spain, France or South Africa. In fact, it was actively discouraged in the USA, UK and SA.

FT analysis of John Hopkins University, CSSE, Worldometers; FT research

Czech Republic made masks mandatory in public

The Czech Republic made masks mandatory in public on 18 March 2020 (Euronews), and now more than a week later, they have a high level of compliance. This country needs to be monitored closely. As on 29 March, the known cases were growing at an average of 12% per day from 23-31 March. As on 31 March they had 3002 known Covid-19 cases and 25 deaths (death rate by known cases = 0.83 %).

Czech campaign ( )

Hong Kong

Public posters in Hong Kong – obtained from the James Myburg article “The coming winter flood”.

Shortage of medical masks

The main reason masks have not been advocated for as a method to prevent contamination is that people have stockpiled masks, which has a detrimental effect for medical staff. Masks are critical to protect medical staff when treating infected patients. All members of the public that have access to medical masks should provide this to their nearest hospital. The general public should be encouraged to only wear homemade masks. In this way the supply of medical masks to medical staff will not be affected, while at the same time providing protection to the general public.

Non-medical people do not know how to wear homemade face masks putting them at higher risk

According to a medical expert on SA radio,

“The evidence is that, as masks dampen with prolonged wear, they become quite good at conveying viral particles deposited onto their surfaces (both inside and out). Also, based on observation, most people do not use masks effectively. They also tend to touch the mask more often, to adjust it or for eating and drinking, potentially depositing virus onto the surfaces. Consequently, the authority view is that members of the public should not use masks because they will derive no benefit from them. They may even increase their own risk. Therefore, masks out in public really aren’t that helpful, unfortunately.

You are right to highlight the dangers of transmission in a care setting. The close and prolonged contact that such care necessitates results in significant risk escalation. We estimate that 80% of transmissions occur in the household settings.”

Non-medical people do not currently know how to handle and wear homemade face masks. There is no reason why they will not be able to learn the correct practices. One way to practice to not touch your face would be to wear a homemade face mask at home (Price, 2020). The face mask will make you more aware of when you touch your face. This needs to be practiced long before you wear a homemade facemask in public, or long before you need to care for a Covid-19 patient in your household.

During the lockdown period, the intention is for you to only go out to buy food or medicine. This should be a short mission avoiding the prolonged wear risks. There should be no eating or drinking during this mission. When returning home, it must be assumed that the homemade mask is contaminated, and it must be washed immediately.

Homemade masks

Masks can be made from variety of readily available materials. T-shirt material and pillowcase material have been shown to adequately filter droplets and has good breathability according to the scientific paper by Davies and others (2013). In poorer countries, where physical distancing is more difficult, everyone still has access to T-shirts.


Anyone can wear a T-shirt as a mask. A t-shirt head opening can be worn at eye level, with the sleeves tied behind the head. Make sure that you cover your chin with the mask.

There is a proliferation in DIY face mask solutions on the internet. When you go out, put on a clean homemade mask and focus on not touching your face. When arriving home, take off the mask and wash both your mask and your hands with soap and water.

Misconceptions regarding face masks

Hospitals and medical staff absolutely need medical masks, and they are running out of masks quickly. This has resulted in a misleading message to the public, stating that face masks do not help the general public. While well-intended, this is not true. Below find a table with common misconceptions amongst the public, with suitable corrections.

Comparing the individual benefit to the group benefit

While there is a benefit to the individual wearing a homemade mask in public, one should really consider the benefit of everyone following this practice. If everyone follows this practice and everyone practices rigorous hand hygiene, fewer surfaces will be contaminated and the likelihood of hand to face contamination will be reduced. As more people follow this practice, the benefit will be compounded exponentially. This benefit should be modelled mathematically in the same way that lockdown measures were modelled in the article by Harry Stevens, 2020.


By making it compulsory for all to wear homemade masks in public and by creating awareness of proper mask wearing practices, the growth in Covid-19 infections will be slowed down dramatically, without putting medical staff at risk due to a shortage of medical masks. The Czech Republic introduced this practice on 18 March 2020, and the early signs are positive and need to be monitored closely. To ensure that medical staff have access to medical masks, the public should be encouraged to hand all medical masks over to hospitals.

Homemade face masks are an affordable and effective way to limit the spread of Covid-19. Many people are asymptomatic and are actively spreading the virus simply by speaking. When you speak there are always droplets coming out of your mouth.

Be an example to others during lockdown, by publicising your homemade face masks on social media and encouraging others to do the same. By doing this, this habit could potentially spread faster than the virus itself.

My mask protects you, your mask protects me.


Cohen, J 2020. Not wearing masks to protect against coronavirus is a ‘big mistake,’ top Chinese scientist says. Sciencemag 27 March 2020.

Davies, Anna & Thompson, Katy-Anne & Giri, Karthika & Kafatos, George & Walker, James & Bennett, Allan. (2013). Testing the Efficacy of Homemade Masks: Would They Protect in an Influenza Pandemic?. Disaster medicine and public health preparedness. 7. 413-418. 10.1017/dmp.2013.43.

Euronews 24 March 2020. Coronavirus: Czechs facing up to COVID-19 crisis by making masks mandatory

FT analysis of John Hopkins University, CSSE, Worldometers; FT research.

Country by country: how coronavirus case trajectories compare

#Masks4all campaign

Myburg, James 2020. “The coming winter flood”

Pueyo, Tomas 2020. “Coronavirus: The Hammer and the Dance”.

Price, David 2020. Empowering and protecting your family during the Covid-19 pandemic (video)

Stevens, Harry 2020. Why outbreaks like coronavirus spread exponentially, and how to flatten the curve.


  • Before putting on a mask, clean hands with alcohol-based hand rub or soap and water.
  • Cover mouth and nose with mask and make sure there are no gaps between your face and the mask.
  • Avoid touching the mask while using it; if you do, clean your hands with alcohol-based hand rub or soap and water.
  • Replace the mask with a new one as soon as it is damp and do not re-use single-use masks.
  • To remove the mask: remove it from behind (do not touch the front of mask); discard immediately in a closed bin; clean hands with alcohol-based hand rub or soap and water.


Suspected COVID-19 cases who are medically well, or who are assessed as having only mild disease, may be managed at home while awaiting test results.

Such patients should be instructed to self-isolate at home and be given appropriate advice about reducing possible transmission to others:

  • At home, the patient should stay in a specific room and use his/her own bathroom (if possible). If they live in shared accommodation (university halls of residence or similar) with a communal kitchen, bathroom(s) and living area, they should stay in their room with the door closed, only coming out when necessary, wearing a facemask if they do so.-
  • Patients should avoid unnecessary travel and unnecessary contact with other people.
  • Where contact is unavoidable, the patient should wear a facemask, and maintain a distance of at least 1 metre (preferably 2 metres) from other people
  • Patients should clean their hands with soap and water frequently. Alcohol-based sanitizers may also be used, provided they contain at least 70% alcohol.
  • Make sure that shared spaces in the home have good air flow, such as by an opened window
  • Patients should practice good cough and sneeze hygiene, by using a tissue, and then immediately discarding the tissue in a lined trash can, followed by washing hands immediately.
  • Patients should not have visitors in their home. Only those who live in their home should be allowed to stay.
  • Patients should avoid sharing household items like dishes, cups, eating utensils and towels. After using any of these, the items should be thoroughly washed with soap and hot water.
  • All high-touch surfaces like table tops, counters, toilets, phones, computers, etc. should be appropriately and frequently cleaned.
  • If patients need to wash laundry at home before the results are available, then they should wash all laundry at the highest temperature compatible for the fabric using laundry detergent. This should be above 60° C. If possible, they should tumble dry and iron using the highest setting compatible with the fabric. Disposable gloves and a plastic apron should be used when handling soiled materials if possible and all surfaces and the area around the washing machine should be cleaned. Laundry should not be taken to a laundrette. The patient should wash his/her hands thoroughly with soap and water after handling dirty laundry (remove gloves first if used).
  • Patients should know who to call if they develop any worsening symptoms, so that they can be safely reassessed.