MAILBOX: Global snapshot of Covid-19 data – actuary Dave de Klerk
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MAILBOX: Global snapshot of Covid-19 data – actuary Dave de Klerk

Cape Town actuary Dave de Klerk, by now familiar to BizNews readers, shares some fascinating and detailed Covid-19 global tables, now only possible because of the passage of time, and some resultant half-decent, but unavoidably patchy, data. He also shares his conclusions, which I’m fairly certain will be debated, supported and contested in these columns for several days to come. What emerges, among many other things, is the impact of infrastructure, access, political denialism and often outright stupidity on the progress of the pandemic in various parts of the world. The virus is the virus, and it behaves according to the opportunities we afford it through our actions and inaction, casting a blinding spotlight on what needs to be improved in pre-existing behaviour and set-ups. In many ways, it’s a blessing, exposing our separation from self and others, and calling us unavoidably into Ubuntu. – Chris Bateman

By Dave de Klerk

With a reasonable period now elapsed, different countries’ cumulative experiences can be better compared. The table below covers the 50 countries with the highest numbers of cases in order of cumulative case counts.

To put column ‘B’ into context, the total annual death rate from all causes for the world is roughly seven per thousand.

The Covid-19 rates for the different countries differ considerably (from a low of 0.1 for Japan and Pakistan, to 3.8 for Hungary and SA at 1.5). Remember, these rates are for more than a year, i.e. for the whole period the virus has been around. If we scale down the 5.2 million Covid-19 deaths to an annual figure, the additional mortality caused directly by Covid-19 is around 6%. However indirectly, Covid-19 has probably had other effects; saving some lives during lockdowns (accidents, etc) but also adding some as a result of restricted medical services for other health problems plus some additional deaths owing to economic hardship.

Column ‘A’ shows the percentages infected in the different countries, ranging from a low of 0.6% for Pakistan to a high of 22.1% for Georgia (SA 5.1%)

Column ‘C’ depicts the Covid-19 death rate per 100 people testing positive. This ranges from a low of 0.3% for Indonesia and UAL to a high of 9% for Peru (SA 2.9%).

Column ‘D’ is the current positivity rate. Unfortunately more and more countries are not submitting up to date data iro numbers of tests done.

Of the 70% who do submit that data, SA has the second highest positivity rate at 29.5% exceeded only by Slovakia at 38.6%.

At the previous two peaks, South Africa reached 35%. In only about two weeks we have gone from 1% to nearly 30%.

The coronavirus seems to be mutating in exactly the way a good virus should; more infectious but less lethal. To ensure a long and prosperous life.

Why it is important to use ratios

Almost all Covid-19 figures quoted in the media are absolute figures, down to the last single case or last single death.Without relating these numbers to some form of exposure to risk, they are not very informative.

One hundred thousand deaths in South Africa cannot be compared with a hypothetical 100,000 deaths in China because China has so many more people.

It is imperative to express all Covid-19 figures as percentages of something, to make them meaningful.

A widely publicised, misleading bit of information is the high percentage of people becoming ill, hospitalised or dying, being unvaccinated.

Well, on the day before the first vaccine was administered, this percentage would obviously be 100%.

If, after 20% had been vaccinated, the number of unvaccinated people becoming ill was not at least 80% of the total, the vaccine would be worthless. Yet, such claims continue to be bandied around.

Here somebody should think of comparing the ratios of unvaccinated against vaccinated people becoming infected, hospitalised or dying.

The Omicron mutation

It will be interesting to update these tables in a few months to see the effect of the Omicron variant on these statistics. The purely anecdotal indication at this stage is that the figures in columns A and D may increase significantly but that those in column C may, in fact (hopefully) reduce.

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