Testing

In winter times we rely more frequently on test to find out about sickness. Covid-19 testing has proven pretty effective in this respect. Often it is preferable to test more persons positive if at the risk of having many false positives. False negative tests can have many fatal consequences. Hence we have to weigh the risks of both kinds of false tests. This applies to many other diseases and diagnoses as well. There is no certainty but only a probability of each of the test results. The true result may deviate from the observed or tested result. FF FT TF TT are the possible outcomes of (1) the underlying true or false outcomes, which can have test scores of true or false as well. Not much new here the rest is statistics or probabilities to be more precise.

The overall outcome of testing or true sickness for some only God may know. Cancellations of events may be the result of one or the other reasoning.

On Noise

The 3 authors Daniel Kahneman, Olivier Sibony, Cass R. Sunstein have published in 2021 the impressive attempt to sell statistics to non-statisticians. The grip on the topic: “Noise. A Flaw in Human Judgment” is a bit misleading. Even the German translation (“Was unsere Entscheidungen verzerrt”), in my opinion, is grossly misleading. The work deals with judgment, or arriving at a sensible judgment. Decision-making is only the next step with a lot of other intervening processes. The German philosophical term since the enlightenment period has been “Urteilskraft“. We are all more or less familiar with the notion “bias” in judgment. Me, originating from the Moselle, will always be biased in favor of a Riesling compared to other vines. In addition to this naive bias I may apply a more professional judgment on wine. Testing several wines even from the same small area from the Moselle valley and then repeating the tasting I might make a noisy judgment.  “When wine experts at a major US wine competition tasted the same wines twice, they scored only 18% of the wines identically (usually, the very worst ones).” (p. 80). In addition to the previously defined form of “level noise, pattern noise and system noise” (p.77), we have occasion noise, when judgments vary from an overall statistical perspective.
Having received a second dose of a vaccination yesterday and having spent an unpleasant night my judgment for this review might be biased, because of impatience. So in order to reduce bias and variants of noise I shall repeat the review at a later stage. Let’s see what this returns. But for today, the Epilogue “A less noisy world” (p.377) appears rather odd to me. It is probably an illusion to believe that we can create a less noisy world, even with the best of wishes. The authors abstract from any strategic use of noise to influence judgments. The political form of choosing judges for Constitutional Courts in the U.S. needs to be dealt with. Noise in judgments is an important element, but strategic use of bias might be more influential to impact outcomes. Noise, when faced with a judge who has a reputation to be very tough in sentences might be overturned in an appeal court decision. There are plenty of procedural ways to overcome noise in judgments. I agree with the authors that you better know about the noise in judgments than ignore it. Awareness of random errors and noise involved in grading exams and recruitment decisions have determined many excellent “failures” to leave historic contributions to our world. In music, maths or literature some splendid talents probably have been impeeded at earlier stages of their life to make average or normal careers. Some of them left us with fantastic pieces thanks to the noise in judgment of others.
There seems to be an age bias in the tolerance of noise in the acoustic sense. Noise in the statistical sense has left a strong mark on me when I learned about white noise as error or stochastic process.
Image Kahneman, Sibony, Sunstein 2021. p3.

Covid-19 USA

With almost 6 months into COVID-19 in the USA since the first official case, the public health situation is still scary. Data and figures from the official U.S. Department of Health & Human Services, particularly the recent data release from the Centers for Disease Control and Prevention show around 50.000 new cases of Covid-19 every day again (see figure).

By using the data and calculating a simple linear trend shows the following evolution in case no policy change occurs.

Using a seasonality in the calculation of about 3 weeks (=20days) the evolution looks much different with huge margins of error.

Hence, all is possible by chart analysis or the language of investors. Public health analysts would rather look at the disaggregated state by state or even county be county detailed analysis. A seasonality of 3 weeks could make sense. With a high level Covid-19 cases in one state people move (also with Covid-19) to other states for one week exporting the virus. After 2 more weeks incubation, case numbers rise in this receiving area, which will make visitors (and the virus) move back again. A oscilating pattern of the spread of the virus will result, making a nationwide lockdown much more likely. In natural sciences (Video-Link)we call this coupled oscillation, here of Covid-19 between several US-states. (take Florida and New York for example).
Now, let us apply the same simple statistical modelling to the international level. As we enjoy Summer in Europe , in the southern hemisphere of the world Winter, Covid-19 and the flu are spreading there now. When we shall move from Autumn to Winter, we might receive another wave of cases from the other part of the world.
Conclusion: Get prepared for another wave as of now. Helping the global South now, saves lives in Autumn and Winter also in Europe. Simple isn’t it. Let’s just act accordingly (the not so simple part). Train health care professionals (95.000 of them got infected in the US and 500 !!! died).