In this article I would like to refer to a study that indirectly examined the effectiveness of political measures that are not explicitly medical in nature. But first a few words about the world of science:
The reliability of „science“
We often hear „science says this“ or „scientists say that“ to give a statement a certain relevance and factuality. First of all, it should be said that in science, in most cases, there are at least two views on a topic. Science thrives on the fact that different opinions are brought to bear on each other and that they are checked against each other in its publications.
However, there are considerable differences in the quality of the work of individual researchers. Not every study is equally good and therefore equally reliable.
In the first semester of my master’s program, one of the first courses was to understand the quality criteria of a meaningful scientific paper and to be able to read it out. In the course of this seminar we had to examine and evaluate a total of three different scientific articles and write a so-called „review“. It is quite common practice to have such a „review“ written by another expert for some articles, especially those written by renowned scientists.
One tries to understand the work of the authors, recalculates results, checks the reliability of the data situation, evaluates the comprehensibility of the argumentation and such things more. In fact, it is always necessary to read and understand an article before using it as a basis for argumentation. Unfortunately this happens too seldom, but this is understandable.
The first article we had to review was written by a recognized professor. Normally articles are always reviewed by at least two other scientists before they are published. This probably did not happen with this first article because it was frighteningly miserable. Even headings were misspelled and bullet points were duplicated. Nevertheless, the article has been given to the public in this form. This was, of course, an extreme example of how the quality of scientific work can vary greatly.
In the article „Conspiracy Theory: Part 2“ I have already mentioned how we like to receive information that fits our world view more readily and uncritically than those that contradict our opinion – I myself am no exception. In addition, most people do not have access to scientific articles, because they are usually only available through channels that many people do not know and because they are simply not understandable in terms of content for „non-scientists“ and even for scientists from other fields.
Herein lies the first dilemma of laymen: If I only hear the results of a study, but cannot understand how they came about, then one should always be skeptical about the results. But in between there is often blind trust in the expertise and quality of „science“ – there is, however, an enormous variance.
„The majority of scientists say…“
Another important point is that science does not follow democratic principles. It is not an argument for the conclusiveness of a statement if the alleged „majority“ of scientists adheres to this or that theory. This seems to correspond to human logic, but in fact it is a simple fallacy. Just because the majority believes something, this does not speak for its truthfulness – this is also true, no, even above all, for science.
History has shown us often enough how during their lifetime some clever minds were even politically persecuted by their contemporaries because their statements did not fit into the prevailing world view and contradicted set scientific facts. Socrates and Galilei are only two of the most famous examples.
Even with the increasing number of scientists, the risk of error is no less. Many papers that are published often do not create their own knowledge and new insights, but refer to already researched and argued facts, quote these papers and sometimes blindly rely on the correctness of already written articles. In a dynamic world like today, misconceptions in science can spread just as quickly as „fake news“ in social media.
The scientific world sometimes even resembles an aristocracy. When renowned and recognized researchers make certain statements, they naturally enjoy a certain authority. So if the professor of a university department adheres to a certain theory, then the other academics in his chair have a hard time coming to a completely different opinion. The world of science is sometimes very much characterized by relationships. „Networking“ is enormously important in this respect. If you appear as an unknown name with established scientists, it is like an imaginary order you can pin to your vest.
My professor, for example, enabled me to write an article together with him as a master student. This is a truly outstanding opportunity for me from an academic point of view, as my professor is an absolute authority in the field of linguistics – what he writes and says carries enormous weight.
About the study: The effectiveness of corona measures
The following scientific study was published by the „National Bureau of Economic Research“ (NBER) This research institution was founded in 1920 and has since produced more than twenty Nobel Prize winners.
The NBER „is an American private nonprofit research organization committed to undertaking and disseminating unbiased economic research among public policymakers, business professionals, and the academic community. The NBER is well known for providing start and end dates for recessions in the United States. Many of the Chairmen of the Council of Economic Advisers have been NBER Research Associates, including the former NBER President and Harvard Professor, Martin Feldstein. The NBER’s current President and CEO is Professor James M. Poterba of MIT.“ (Source)
The study „Four stylized facts about covid-19“ was published in August this year and was conducted and written by Andrew Atkeson, Karen Kopecky and Tao Zha. This study is a statistical study in which several different statistical models were used to investigate the extent to which the „non-pharmaceutical interventions (NPIs)“ had an influence on the transmission of „Covid-19“.
There have already been a number of studies this year that have confirmed the effectiveness of the NPIs. However, the authors argue with four facts that have not yet been included in the evaluation of the effectiveness of the NPIs. These facts are as follows:
- In all the regions and countries they examined, the death rates rose rapidly in a short period of time and fell to a level close to 0 after 20 to 30 days.
- After this initial period, the growth rate of death rates around the world was around 0.
- The average growth in death rates in all regions and countries studied dropped very rapidly after 10 days after the first 25 corona deaths were recorded in the respective area and remained very low since then.
- If these three facts are implemented in different epidemiological models (we recall the spherical models), it can be shown for each region of the world that the reproduction number (R-factor) and the transmission rate of Covid-19 are the same regardless of the measures taken.
They also note that no increase in the number of deaths was observed after the measures were relaxed (see p. 2). They also mention three factors that have played a role in the last seven major pandemics but have not yet been investigated for the current pandemic:
- People behave differently when a pandemic is declared.
- The network structure of human societies is more complex than epidemiological models suggest. There, the masses are regarded as a homogeneous cluster in which everyone potentially interacts with everyone else.
- Unexplained and inexplicable natural factors, such as the nature of the respective virus or temperature fluctuations. 7 of 8 major pandemics had a curvilinear course without comparable measures (see p. 3-4).
From this the three authors derive the following hypothesis:
„We argue that failing to account for these four stylized facts may result in overstating the importance of policy mandated NPIs for shaping the progression of this deadly pandemic.“
In short: The previous studies have omitted important things so that they were subject to the misconception that the measures had a proven effect.
The data situation
The authors cite three studies (Dehning et al. 2020, Hsiang et al. 2020 and Flaxman et al. 2020) as examples of a series of studies which, in their view, contain an error in their reasoning because they did not take into account the four facts mentioned above (see p. 2). The three studies mentioned above have been published in the renowned and globally recognized scientific journals „Science“ and „Nature„. The study presented here is therefore a minority opinion in the research community, but in my opinion this is not a valid argument for a lack of conclusiveness.
The following countries and regions were examined in the study:
„Argentina, Belgium, Brazil, Canada, Chile, France, Germany, India, Iran, Ireland, Italy, Japan, Mexico, Netherlands, Panama, Peru, Portugal, Russia, Spain, Sweden, Switzerland, Denmark, Turkey, the United Kingdom, Arizona, California, Colorado, Connecticut, Florida, Georgia, Illinois, Indiana, Louisiana, Maryland, Massachusetts, Michigan, Mississippi, Minnesota, Missouri, New Jersey, New York, North Carolina, Ohio, Pennsylvania, South Carolina, Texas, Virginia, and Washington. The rest of the U.S. is counted as another region.“
The authors have selected the regions that „in total, had more than 1000 deaths from Covid-19 by July 22, 2020“ (see p. 9). The authors obtained their data for the USA from the „New York Times“ and for the other countries from „Johns Hopkins University“ (see ibid.).
For the present study, the authors refer exclusively to death figures, as these are supposed to be „more reliable“, i.e. positive test results (see p. 5). According to their statements, the data were very „noisy“. In other words, the data showed „disproportionate“ fluctuations. On the one hand, they found a „day-of-the-week effect“. And on the other hand, they attribute the fluctuations to the constantly changing and country-specific definitions of when a death is considered a „corona victim“ (see p. 10).
The research method
As already mentioned, this is a statistical study. Statistics is an essential core element of Epidemiology, because epidemiologists try to make statements about a whole group of people, whereas clinical medicine investigates individual cases. In principle, statistics means being able to make statements on the basis of certain data about things for which no data are available. So one tries to consider so many individual data that one can draw conclusions about the big picture from a series of individual cases that should be representative of the whole.
For this study, the population of the respective regions was divided into four groups: Those at risk („susceptible“), those infected („infected“), resistant („resistant“) and dead („dead“) (see p. 6). Further sizes are: time, total population, reproduction factor, recovery rate, lethality rate and transmission rate (see ibid.).
The derived variables, which they define in their formulas, correspond to the data officially adopted by the CDC („center for disease control“) (cf. p. 9). The CDC is in principle the RKI of the USA.
The authors have, in very simplified terms, examined and analyzed many different countries and regions and their individual death rates from the time when a total of 25 corona deaths were recorded in one region until July 22, 2020. They did not consider which measures were taken in which country. They simply put the death figures of the respective country into the same derived statistical formula.
The derivation of such a formula then looks like this in excerpts:
(extract 1: source, S. 11)
I admit openly: Also I cannot follow every step of this derivation mentally, because my statistical knowledge is limited to descriptive statistics. Therefore, all doubters and skeptics should let the statistician and/or math person of their confidence take a look at this article. If you don’t know such a person, then you are probably at the mercy of the layman’s dilemma, which you always encounter when you are presented with the results of a study.
If you then fill in the above formula with the data of an exemplary country, you get the following graphs:
(immage 1: source, S. 17)
For the above graphs, the authors have selected four exemplary countries from four different continents, which show very different shaped curves (left figures): New York, Sweden, Panama and Iran. However, when the data from the left-hand curves are expressed using the derived formula, we obtain very similarly shaped progression curves that represent the growth rate of daily death rates and the effective reproduction rate.
When all the regions studied are plotted together, we obtain the following graphs:
(immage 2: source, S. 18)
The above illustration in Figure 2 shows the effective reproduction rate and the lower illustration shows the growth rate of the death rates. You do not need to be a statistics expert to see how fast the curves fall from a certain level and remain at a low level. For example, if many more people died in countries where no or little action was taken, this would result in strange hills and peaks of so-called „outliers“. However, none of this can be seen here.
And what does that mean?
And this is where the next dilemma of the lay public begins: If you are not able to evaluate the significance of such a study, its derivations and graphs yourself, then you are at the mercy of „experts“. In the course of my studies I have already gained a lot of experience with scientific texts in both German and English. Therefore, I now presume to be able to evaluate a scientific work.
In the case at hand, we are dealing with a very tightly structured and methodically clean article that meets all the requirements I have learned from my studies. I can say less about the flawlessness of the statistical formulas, but since the authors arrived at the same values after deriving their quantities as indicated by the official American „CDC“, I assume that the model is robust.
The validity of their model is also supported by the fact that a number of other already existing epidemiological simulations, taking into account the four mentioned facts, came to a comparable result independent of the country.
Thus, in the figures above we can see how the development of death rates of the corona pandemic has been very similar everywhere, despite the differences in the measures taken or not taken, the different geographical and demographic situation of the regions studied.
Therefore the authors come to the following conclusion:
„Our ﬁnding in Fact 1 that early declines in the transmission rate of COVID-19 were nearly universal worldwide suggest that the role of region-speciﬁc NPI’s implemented in this early phase of the pandemic is likely overstated. This ﬁnding instead suggests that some other factor(s) common across regions drove the early and rapid transmission rate declines. While all three factors mentioned in the introduction, voluntary social distancing, the network structure of human interactions, and the nature of the disease itself, are natural contenders, disentangling their relative roles is diffcult.“
„Our ﬁndings in Fact 2 and Fact 3 further raise doubt about the importance in NPI’s (lockdown policies in particular) in accounting for the evolution of COVID-19 transmission rates over time and across locations. Many of the regions in our sample that instated lockdown policies early on in their local epidemic, removed them later on in our estimation period, or have have not relied on mandated NPI’s much at all. Yet, effective reproduction numbers in all regions have continued to remain low relative to initial levels indicating that the removal of lockdown policies has had little effect on transmission rates.“
The existing literature has concluded that NPI policy and social distancing have been essential to reducing the spread of COVID-19 and the number of deaths due to this deadly pandemic. The stylized facts established in this paper challenge this conclusion. “
The study discussed here has already been published last month. It challenges previous studies on the lethal spread of corona. If you search for „NBER studie corona“ via Google, you will only get two hits on the first page of pages unknown to me that refer to this study: (1), (2). The second link refers to an English page that also covers the article. The page through which the article was brought to me is the alternative medium „journalistenwatch“, which is only listed in the middle of the second page of the Google search. There is nothing to be seen of the „leading media“ with regard to this study on the first five pages.
Therefore, I assume that the leading media in Germany deliberately ignore this study. I personally can hardly imagine how such a controversial study, which on top of that comes from one of the most renowned research institutions in the world, could simply go under. If this article reaches me, it will also reach the editors of the major media houses. All studies that fit into the corona narrative of the leading media, on the other hand, are treated prominently.
Of course I am also prejudiced. Because this article fits into my view of current events, I have elaborated on it here long and broadly. My own observations, my previous analysis and my logical understanding of the official figures are now additionally confirmed by this study. All the questions I asked in the article „The Laws of Logic“ are used here as arguments to doubt the previous investigations in this area.
The fact that this study is not discussed in the leading media („the Lückenpresse„) is for me only a further indication of its validity. I believe that if there were a possibility of somehow ironing out this study, this would have been done immediately. From this I conclude: the study has been conducted very cleanly and offers little room for attack, which is why it is simply dropped under the table.
We will see whether the findings of this study will ever see the light of day for the general public or whether the cloak of silence that the „Lückenpresse“ spreads will prevent this work from being penetrated.
by Marco Lo Voi
This the english translation of the article: