To Observe and to Know

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Good. I have been able to play with my new Python workbooks for two weeks now to find answers to the questions raised by the COVID-19 event, Node said to himself. Questions that are important and that are not clarified, not by officials affiliated with institutions, not by the experts who perform in the talk shows on a daily basis, nor by the administrators and MPs who have to determine and monitor policies. I mention (again, but now a few weeks later) some of the uncertainties that continue to be evoked by a simple picture of COVID infections and deaths (as given in Fig. 1).

Fig.1 COVID-19 numbers for the Netherlands on December 19, 2020 (week 50)

For the first plot, the infection and death rates were initially harmonized to raw numbers per million per day. Fortunately, such infection and death numbers do not run simultaneously. That would mean that everyone infected by the virus would die. The first plot therefore provides an adapted picture from which can be read what the pandemic would look like in the Netherlands if all infections had been measured and 10% of them had ended in death. That seems to be pretty close to what was seen in the first half of the pandemic (the first 26 weeks – no longer thereafter). Yet …

In the second plot, the infection numbers have been harmonized not to lead to a 10% but to a 1% (one percent) death rate. Then suddenly the lines of the second half (the second 26 weeks) fit together (but no longer in the first period).

This phenomenon raises questions that are important but remain inarticulate end unanswered in Dutch debates. They are pushed aside by scientifically trained but out of their boxes operating experts, many of whom pluck the most muddled assumptions from thin air, presumably because they support their favorite hunches. I called it phantom knowledge.

I try not to join the pack here and try to stay as close as possible to what has been observed. Then suspicion arises that actual infections were covered by registration much less complete at the beginning than at the end. But also that we still do not have a complete registry of infections. Only the registry of COVID-deaths an be expected to have some sort of validity and completeness. And that means that this kind of knowledge based on infection-number measurement is garbage-in garbage-out knowledge. In other words: far too little is known to be able to know about anti-COVID measures with any sophistication. But we can hardly accept that. The urge to know is an essential human need and an emotion that brings evolutionary benefits, if balanced by reason and observation.

The preliminary conclusion is that many experts want to act as opinion leaders and do not hesitate to (pace Donald Trump) present fake or phantom knowledge.

Yesterday I heard Mrs. Baarsma on the radio. She is an economist and a bank manager. She stated with conviction that there is only one rational way to get rid of the virus and that is by letting it rage (until we have sufficient organizational skills and sufficient effective drugs / vaccines to domesticate it).

Mrs. Baarsma expects that the availability of adequate organizational capacity, medicines and vaccines will take another year. Muddling through with the measures during all that time (the path that the government has now taken) is too much at the expense of the young, she thinks. Of course the elderly must be protected. By seclusion in what sounded like a plan aimed at permanent quarantine for those over 70.

It reminded me of leper colonies and concentration camps, Node remarked to himself.

Apart from its peculiar ethical nature (where curtailing social expansion of the young outweighs the loss of the senior’s lives), this reasoning rests on at least three rather frail assumptions:

  • first, the assumption that policy aimed at control (as in China) cannot work in a Western culture,
  • second, the assumption that having been infected yields long-term immunity,
  • third, the assumption that letting the virus proceed without strict measures would be less harmful to Dutch society (including the economy) than living under measures that protect health care personnel and instrumentation against becoming overburdened.

What I find most surprising is that these types of assumptions are not further explored and discussed, Node thought. For example, by asking whether Japan, New Zealand and Australia, and in a sense (in terms of open and semi-open economies) also South Korea, Singapore and China, offer counterexamples that deserve to be seriously discussed.

The second assumption is mainly medical-technical and should probably already be judged as untenable.

And the third assumes that Dutch society will benefit less (in terms of social cohesion, rule of law and economy) from another year of the current counter-cyclical approach than from a year in which partying youngsters concurrently learn to morally accept their privileges to be at the cost of a health care system overburdened by the requirement to handle an estimated 175,380 COVID-19 deaths (1% of the population) and the companion multitudes of seriously ill.

In order to form an opinion about all this I made a series of Figures, for which my Python work provided the means. (The Jupyter notebook used is here). The European database:

https://opendata.ecdc.europa.eu/covid19/casedistribution/csv

as available on December 20, 2020 is used as the source for the data.

Elsewhere I paid attention to how such curves should be read. The pictures are kept very small. (They are .svg files that can be enlarged unlimited when viewed separately).

I give three groups of countries. Fig. 2 has seven countries that I expect to put forward counter-cyclical policies, Fig. 3 has a group of seven countries that I expect to have policies aimed at control of the virus and Fig. 4 has seven “divided” jurisdictions that I expect to have fragmented policies.

In the group of countries with counter-cyclical policies, the maximum number of new deaths per million inhabitants varies between 6 and 25, and the accumulated number of deaths in week 50 is between 280 and 1,600 per million inhabitants. The differences are remarkable. Both in terms of dynamics and height. See Germany. At the same time, the development of a second wave in this group is a universal phenomenon.

In the group of countries with a control-oriented policy, the number of new COVID deaths per day per million inhabitants is lower than 1. One or more orders lower than in the other two groups. The total number of deaths in this control-oriented group varies between 1 and 80 deaths per million inhabitants after 50 weeks of pandemic. It is uncertain whether the Philippines belongs in this category (and would not fit better in the group of fragmented countries), but to be able to argue I know too little about the Philippines, Node thinks.

For the group of countries with a (usually institutionally anchored) fragmentary policy, it appears to be characteristic that the cumulative curve tends to a monotonically rising rather straight line. Periods of flattening are characteristic for the other two groups.

The picture for Canada therefore does not fit well in the group of “divided” countries. The country appears to be responding rather like with a counter-cyclical approach.

Finally, a comment that rests on observation also: Belgium is far in front in terms of COVID deaths per million inhabitants over the first 50 weeks of the pandemic: so counter-cyclical policies show a tendency to become problematic. Because of the mere passage of time (stress, habituation, denial)? In Belgium this has apparently gotten considerably worse than, for example, in the US, the UK or the Netherlands. Meanwhile, the exponential growth in the number of new deaths per day in Germany is currently causing worry and wonder (and lock-downs).

And today (December 21, 2020) the UK has been locked out by international traffic being closed. Because a new, possibly even more damaging mutation of the virus has begun to circulate there.