COVID-19 Archives - Louis D. van Niekerk

28 March 20202 April 2020

SA in Lockdown: Day 3 – Will our youth save us or will the Germans?

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“Current estimates of Covid-19 deaths ‘deeply flawed’ – The Wall Street Journal – Linda van Tilburg”

Let’s just remind ourselves of the current confirmed cases because ultimately the estimated deaths are derived from this data.

Below is an animation of the fatalities over time (you must press play in the bottom left hand corner).

This headline immediately had my attention. Since I made some predictions myself I just had to read what she had to say.

It was coronavirus projections estimating that South Africa could have between 87,900 and 351,000 deaths if the country did not take drastic measures to contain Covid-19 that spurred President Cyril Ramaphosa into action with the 21-day lockdown to enforce social exclusion. These are scary statistics, especially in a country where millions of people have compromised immune systems due to HIV/Aids and TB. Two professors in medicine from the University of Stanford Eran Bendavid and Jay Bhattacharya believe that the World Health Organisation’s 2-4% fatality rate of Covid-19 is “deeply flawed” and that current estimates may be too high. Their argument is based on the fact that it is difficult to determine the number of people who are infected because of “selection bias in testing”. To get a better steer of the hidden cases who are infected by the coronavirus, scientists from King’s College and Guy’s and St Thomas hospital in London are now rolling out Covid-19 symptom trackers where Brits self-report their symptoms to get a steer of the hidden cases – “the iceberg that countries sail into”, which would get policy makers better data to work with. They also believe a “universal quarantine may not be worth the costs it imposes on the economy.” Isolating the vulnerable may be a better option. – Linda van Tilburg

The mortality rate is a very important factor to determine our fate. Italy has, as of writing, a mortality rate of 14.2%, while Germany has a mortality rate of 0.9% (up from 0.3% this time last week). The means, in Italy for the same number of infections, 15.7x more people die. Almost SIXTEEN TIMES more!!!

So what is going on? Before we attempt to answer that, let’s just stress the importance of the mortality rate. If we apply that to the 65% of South Africa’s population I used in yesterday’s post we will have either end up with 331 000 or 5.2 million fatalities. That is a vast difference. Currently, the global average is at 4.5%, which for South Africa corresponds to 1,7 million fatalities.

It must be clear that getting the mortality rate accurate is critical to estimate the scale of the calamity that is awaiting our country.

What are the clues?

Between Germany and Italy lies the USA and the hypothesis that the reason why Germany’s mortality rate is so low is due to the nurses per capita being higher than any other nation. If that were to be true, it would have to be in combination with other factors such as a higher ICU, more vigilant lockdown measures, etc. simply because other countries with similar and higher nurses per 1,000 have much higher mortality rates. Clearly, if that was the single biggest factor, Switzerland and Norway should have a much lower mortality rate.

My hypothesis, and the hope for South Africa, lies in the demographical composition of the nations and virus’s propensity to be more fatal the older the host.

Let’s look at the rate of infection per age group as it played out in China (remember they are a good two months before the rest of the world, so 11 Feb is good mature time to measure for them).

You will notice that the overall mortality at that stage was 2.3% where today it is 4.5%, mainly because of the weight and high mortality of Italy. Still, if we recalibrate these figure by assuming a uniform increase across all age segments, mortality for the 60+ age group becomes 11.8% as per below

Apply this to the relative population and you can work out a weighted average mortality rate as I did below

What does the above chart tel us?

To start with, the figures for Germany and Italy are not reflecting reality, but it does show that we should expect a lower mortality rate in Germany than in Italy. Similarly, because of the age demographic of the South Africa population, if and only if the population had a similar health profile, one would expect a mortality rate of only 1.6%.

What role will TB and AIDS play?

I think the final say in South Africa will be how much of a factor will TB and AIDS have in our population. Be all accounts we should expect a higher mortality rate because we have fewer nurses per 1,000 but we have 7.97 million South Africans living with AIDS. That means an immune system that is under severe strain and we know that fighting COVID-19 is all about the immune system.

Did I manage to answer my own question regarding Germany? I think not. I suspect PhD’s will be written on this question as it is puzzling. Did I, in the process of trying to test a hypothesis about the youthful demographic of the South African population learn something about our expected mortality rate? For sure. What I don’t know yet is what impact TB and AIDS will have…

So the question remains, will our youthfulness reduce our mortality rate or will we be able to emulate what the Germans are doing to keep the mortality rate as low as possible. I think neither, yet I hope I am wrong. Time will tell.

Stay safe. Stay at home.

Ed (30/3/20): Perhaps the chart below is the answer to the question. Source Financial Time via London Business School

Statistic: Population in Italy in 2018, by age group | Statista

Statistic: Population of Germany as of December 31, 2018, by age group (in millions) | Statista

28 March 202029 March 2020

SA in Lockdown: Day 2 – We are slowing down our rate of accelerating

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If you want to understand what my assumptions are and what my methodology is, it is described in this post

It’s the end of day 1 and eNCA reported that we now have 1170 infected cases of COVID-19. It is weird how an increase from the 927 of the day before can be good news and yet it is.

Why is this good news?

A quick reminder about day zero. it is the day that the ceiling of infections is reached. In today’s post that is set at 65% based on some harsh criticism from the internet. I stress that I am not saying that 65% (or 100%) will be infected, only how long it will take to infect that many people.

The good news is that because of the slow down, day zero has shifted from 7 May to 11 May. I know that is only 4 days but look at the chart above. It means that by 7 May only 15 million South Africans are infected and not 36.8 million (65%).

Big deal? Yes, it show how dramatic the slowing down can be on the final outcome.

I explained in my first COVID-19 post that even when a race car is still accelerating there is a point at which the acceleration slows down. At first, this is a difficult concept to grasp so let me try and explain.

If a sports car currently travels 10 m per second and accelerates at a 2 meter per second (per second for the engineers). One second later it will be doing 12 m per second. If it continues to accelerate the acceleration to add 1 meter per second every second, it will be accelerating at 3 meters per second faster every second.

The quid pro quo is this. While the vehicle can still be accelerating (going faster) but (and here’s the important bit) not adding so much speed to the existing speed every second. When the rate of acceleration starts to slow and continues to slow it means that car will soon be travelling a constant speed or flatten out.

That is where China is right now, they have almost flattened out i.e. their vehicle has almost reached a stable speed and will soon be starting to lose speed.

China started its lockdown on 23 January (beginning of this graph). Only on 14 February did the curve start to flatten. So slowing down is massively important

China started its lockdown on 23 January (beginning of this graph). Only on 14 February did the curve start to flatten. So slowing down is massively important

For the first time in a week, our rate of acceleration has slowed down. Yes, our car is still picking up speed and yes unless the rate of acceleration slows down, even more, we are still heading for a disaster, but we have slowed down.

I was equally encouraged on the 21st and 22nd when our rate of acceleration slowed way down, but it was weekend and I suspect the admin bit of our testing regime let to a lag in reporting so that it was a false reading. I am hoping like hell that today’s news is a not a false reading too.

The exponential growth in the context

The easiest way to understand this whole exponential growth curve – the curve we are trying to flatten, is to understand that exponential curves are always about doubling the value in a specific time. If the curve is steep, the time to double is short and if the curve is flat, the time to double is long.

As of today, China’s rate of doubling the number of infections 45 days, while South Africa’s is 3 days.

Below is a live-updating chart indicating the rate of doubling. Be sure to select doubling time and log scale to see where South Africa lies.

I suspect that not too long from now, countries will lose track of number of infections as they run out of tests or the number of people required to be tested simply overwhelms the system. From that point onward only the number of deaths will be recorded and we will measure the spread at the rate at which the number of deaths double.

As I write this, it is surreal to think that we are living in a time where we measure the rate at which the number of people that died doubles.

27 March 202027 March 2020

South Africa Lockdown: Day 1 – Young people are not immune and the sadness within me

If you have questions about the calculations, it is explained in yesterday’s post

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As I went to bed last night I received a video, probably viral by now if you’ll excuse the unintended pun, of a man driving down Kayalitsha with hundreds (if not thousands) of South Africa’s youth dancing and having fun in the streets. The disturbing thing was the mocking chanting of “Corona!” as if to challenge this invisible killer to come and get them.

I had to do small inspection late afternoon yesterday in my neighbouring town and driving there I saw even more disturbing scenes: Groups of youth walking and playing in the streets; on a farm a saw roughly 10 workers gathered in a huddle; on the outskirts of town construction workers were going about their business.

Earlier in the day, I stopped at the local Tops! to buy some provisions given that none this will be available at lockdown. I wore a mask and kept a distance of at least 1 metre but ideally more. I was an Alien. Around me, people behaved in their usual daily fashion with little of any cognisance that they, as everybody above mentioned, may be blissfully spreading the disease.

Here the fatalities are just under 1.7M because I set the model to the max at 65% of the population infected.

It left me with a sense of sadness. Here I am crunching the numbers and seeing that every day, probably due to this ignorant behaviour, South Africa’s infection rate is going up. I want to emphasis RATE in that last sentence. I am not talking about the increase of 30%+ in infected cases. I am talking about that % increasing.

Also, don’t underestimate that when we tested 1,000 people and found 34 cases (14 March) it was still manageable. Yesterday the tally is on 20,471 tests with 927 positive cases. We are no doubt missing some cases, which is also the hypothesis about Italy’s high death toll (13.2%) as opposed to Germany’s (0.3%). One explanation is that Italy’s testing is not as good as Germany’s and therefore they simply don’t know about all the infected cases. Guess what – with tests becoming a scarce commodity globally we will soon lose track of the infected cases and only begin the measure the fatalities.

Max infection rate set at 65% of the population. There is a lag between infection and fatalities so the fatalities are still relatively low by the time the max infection is reached. In the subsequent days those infected then either recover or succumb to the virus.

My sense of sadness will remain for as long as our nation keeps its head buried in the sand. When the reality of the situation starts to hit home with grandparents, aunts, uncles, brothers and sisters perished, my sadness will be that I was powerless to prevent the ignorance.

Finally, it is with a bit of a shock that we learned today that the first fatalities are people of 48 and 29 respectively. Let that sink in. No, it is not only old people and no, young people are not immune.

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26 March 202027 March 2020

South Africa COVID-19 Lockdown Pre-amble, assumptions and methodology

Acc = Accumulated [Errata: These slides show the figures associated with the low road i.e. -20% infection rate as described below. The figures in the text are correct]

Ed: I took some flack on the internet because it is statistically impossible for 100% of the population to be infected. While I disagree, my intention with the article was to show a WHAT-IF, not a certainty. Also, it is highly likely that we might indeed reach such high numbers as South African’s see to disregard the lockdown. See my next post on that.

[Errata: These slides show the figures associated with the low road i.e. -20% infection rate as described below. The figures in the text are correct]

Pre-amble

Tonight, at midnight, South Africa enters a window in history never entered before. The country in total lockdown. Never before was a threat to our national security so severe that the whole country was ordered by law to stay at home.

The charts above show, at this rate, we will infect the whole population by 6 May 2020, killing 2.3 million South Africans in the weeks and months to come.

The Corona Virus Disease (Code name COVID-19) caused by the SARS-CoV-2 virus is the driving force behind this harsh intervention.

As an avid strategist, I could no longer sit and wonder how the spread of COVID-19 would occur in South Africa. Over the weekend, 21-22 March 2020, I started to put together a forecasting model to predict how the virus would spread. As the director of the WHO suggested, one’s worst enemy in times like these is perfection. In that spirit, my model is far from perfect or as robust as I would want it to be and yet over the last five days the accuracy with which it predicted our infection rates gave me a degree of comfort that it is good enough to assign some trust.

Methodology

Given that South Africa’s first infection was 5 March, by the 22 there was a substantial amount enough to derive an exponential curve that fitted the rate of infection. In fact, my methodology is actually to establish the CURRENT exponential curve. From there I derive a rate of change (between the previous three day’s of infections or the previous three curves). I then apply this rate of change in perpetuity in three Scenarios:

Current = The current rate of infection less a diminishing factor (ever the optimist that I am)
Current -20% = Simply takes the current rate and only add 80% of the rate of increase to the curve for consecutive days
Current +20% = The same as above except I add 120% of the rate of change

In all three cases, I add a fixed diminishing factor i.e. assuming the rate of infection will slow-down from this point onward. There are two reasons for this. One, I am an optimist and want to believe that all the efforts by the government and every individual will yield some success in slowing the infection rated won. Two, if I did not slow it (in the early versions this week), it was simply too scary. South Africa’s whole population was infected by mid-April with over 2.2Million South Africans dead when all is done. An unthinkable prospect.

Incidentally, some facts, China’s rate of infection only slowed down between 21-23 days after their first lockdown. With our lockdown due to start tonight, we can only expect matters to get worse – much worse before it will get better.

I make some assumptions (in the state of imperfection) and will refine this over time:

The average lag between infection and symptoms is 5-6 days (with the range varying from 1 to 14 days in reality). Source: https://ourworldindata.org/coronavirus
It takes 14 days to recover from the virus (with some cases lasting much longer)
I assume the current global mortality rate will apply to South Africa (and update that daily). This is contentious as Italy has a mortality rate (12%+)much higher than the average (and pushes the average up considerably) while Germany has a mortality rate of below 1%. I will monitor and update this as time goes on.
Death rate and the lag is very contentious and yet the best I could find is that 11 days after symptoms, is the average where patients will die (I will continue to refine this). Here I introduce a bit of randomness with every day’s delay being the chosen 11 + or – 7 days.

What I do is this:

Predict the infection rate (as described above)
Derive New infections by subtracting yesterday’s TOTAL infection from today’s TOTAL infections. I do this for all three scenarios (Current -, Current and Current +)
From New infections, I apply the mortality rate with a delay (from the assumptions above) to establish fatalities (11 days after new infection) as well as cures, which is the reciprocal figure.

From here on, I will attempt to update the figures daily with some commentary where and if necessary. For the record, according to my model, we should expect our first death any day now. Let’s hope my model is wrong

The bad news

The bad news is that since Monday 23 March, my model has consistently underpredicted our infection rate. All that means is that we continue to adapt an every more aggressive exponential curve. Don’t be fooled by the numbers. Unless you truly understand what an exponential curve is and how it works, the numbers deceive you rather easily (even when you do, it does). It is only by looking at the rate of change that one can establish whether we are slowing down or not.

Think of it like this. If you pull away in a Lamborghini the first few seconds is the fastest acceleration you will ever get and yet, you are still only touching 100km/h, which is not that fast. From then on, you still accelerate, but the rate of acceleration slows down until you reach top speed. While the speed continues to climb, the rate of acceleration is diminishing.

Our bad news is that we are still accelerating and for my money, we are still only at about 10-15 km/h heading for an ugly top speed.

I hope sincerely that my predictions are wrong.