Opinion from a Libertarian ViewPoint

Coronavirus crisis management — what really happened and why it failed

Posted by M. C. on May 18, 2020

Antony Mueller

Shutting down the economy and bringing social life to a standstill did not contain the epidemic. The virus spread and in addition to the harm done by the epidemic itself, additional immense damage has come upon many people because of the lockdown.

Problematic predictions

As an economist, I am trained in making forecasts and am thus familiar with the pitfalls of making predictions. Many processes in economics have similar patterns as epidemics. From the spread of new products to recessions and the contagions that happen in currency crises, processes similar to “pandemics” take place. Widely ignored at its inception, and at first, only slowly growing, crises often remain undetected until it is too late. When the problem finally gets attention, the authorities tend to overreact. Countermeasures are taken that do not contain the problem but acerbate the problems.

Often it would have been better to do nothing and let the things run its course. Yet governments are asked to do something. The population gets hyped-up as the media urges uncritically that something must get done. The same type of error as it often happens in economic policy has also been the case with the lockdown in 2020.

Epidemiological prediction models are not better than those of the economists. Not much different from dynamic developments in economics, it is also almost impossible to foresee in time and with clarity the future shape and dimension of an epidemic. Epidemic processes happen in the form of a “growth curve” which is a well-known quantitative development in economics, finance, and biology.

When a process grows at a constant rate, the overall increase starts out small but gets bigger and bigger over time until its unavoidable collapse. A bacteria culture, for example, will grow slowly at first and gain speed over time until it finishes off in a total collapse when no longer any host is left.

As can be seen in the graph below (figure 1), the coronavirus epidemic follows the typical pattern of a so-called logistic growth curve with the characteristics of almost flat growth at the beginning to be followed by an increasingly steep rise until a peak from where the curve bends down.

Figure 1: World-wide daily reported COVID-19 deaths from January 22 to May 15, 2020

Source: Our World in Data. Report May 15, 2020

One can see that the curve is almost flat from January until early March but took a sharp upward turn in the second half of March 2020, first moderately and at the end of March at an increasing speed. From the middle of March until early May 2020, the number of daily confirmed deaths surged until it peaked at the beginning of May and has been falling since the middle of April 2020.

To better explain such a growth process in more detail and to highlight its features, it helps to stylize the curve and concentrate on its beginning and the exponential part of the growth process (Figure 2).

Figure 2: Exponential growth process

The curves in this graph (Figure 2) point out that at the beginning of the process with its move from a to b along the time axis (t), the existence of an exponential process is still unrecognizable. In the real world, under data uncertainty, an early prognosis could easily have been linear as shown by line P1. During the first half of the process from almost flat at the beginning (a-b) to the stretch at which the curve becomes visibly exponential (b-c), half of the time has already gone by (from a to b) until its endpoint (d) has been reached. Nevertheless, around point b, the projection would still suggest a relatively moderate development (P2) as the movement of the curve is still only slightly bent upwards after reaching stage b and the effects of the process (Q1) are still mild compared to what they will be in the end. At the time between b and c, it may seem as if the process is still manageable. After all, the quantitative effect (vertical axis) has only moved up to Q2 on the vertical axis.

The part from c to d represents the dramatic part of the exponential growth curve. In about one-seventh of the total time span from almost flat to almost vertical (at d), the largest part of the effects takes place within the time span from c to d. In this phase, the speed of change is so fast that in the case of a harmful policy issue the authorities get overburdened by the events and fall into panic mood, which promotes taking wrong decisions.

At their beginning, exponential growth processes typically remain undetected. Yet when they are recognized, they often motivate their discoverers to exaggerate their dimension. When it is a policy matter, and the media take up the issue, public decision-making tends to discard sound judgement.

Disastrous decision making has also taken place in the confrontation with the pandemic of the coronavirus. In an attack of panic, governments around the world implemented harsh measures to block the spread of the coronavirus. The lockdown of the economy included the closing of schools, the suspension of sports and cultural events, the closing of restaurants, and the shutdown of nonessential businesses. Many governments implanted a policy of social distancing.

Failed policy of “flattening the curve”

The campaigners for an lockdown of the economy justified the measures with the claim that the control measures would “flatten the curve”. These advocates in politics and science claimed that without a lockdown, the existing healthcare system would become overburdened by the wave of patients. In many variations swept the model around the world and was reproduced in each corner of the world that showed the curve with a high peak and a small base in contrast to a curve with a low peak and a broader base (Figure 3).

Figure 3: The Concept of “Flattening the Curve”

Source: Everything you need to know about the coronavirus. Wired

As if it were a command from above, the model of “flattening the curve” was rapidly embraced in the echelons of power. “Flattening the curve” became the mantra across Europe with few exceptions. The United States, too, initiated lockdowns across the country. Policymakers discarded the critique that models do not fully represent reality and are necessarily based on assumptions. In the case of the model of flattening the curve, the assumption was that one knows early on that an exponential growth process is in the making. As such, the model of flattening the curve insinuates that the authorities could know from the beginning that a dangerous epidemic is coming. As one can see in the graph (Figure 3), the protective measures are assumed to begin right on from the beginning at a time when the epidemic starts. Yet doing this is impossible in reality.

It takes time before the spread of an infectious disease can be identified as an epidemic. There are considerable time lags between the factual beginning of the process and its recognition. Further lags occur between the recognition and the diagnosis. Additional lags follow from the risk valuation of the occurrence to the decision-making and the implementation of measures. Finally, time goes by until the measures become effective. It rarely happens that public policy measures turn out the way as envisioned and therefore require time-consuming fine-tuning.

The model of flattening the curve (Figure 3) suggests that with protective measures, the spread of the virus would happen more slowly and lead to a lower highpoint than would be the case without protective measures. The policy claims that the peak of the number of patients would be lower as the total number of cases to treat would spread out over a longer period of time.

What really happened was quite different.

As explained above, an exponential growth process (Figure 2) shows up at its inception as nothing peculiar and based on the data available would suggest a linear projection of its course. This was also the case with the coronavirus. In early 2020, the exponential spread of the coronavirus was hardly recognizable. Yet at that time the epidemic was already well underway. When the measures to flatten the curve began, the epidemic was already moving towards its peak. The correct depiction of the curves in the model of flattening the curve must not let the flattened curve begin at the same point in time as the curve that shows the development without protective measures.

A realistic model must take the time lags into account. Then, there is a time lag in place between the development of the epidemic in the real world and the implementation of protective measures. The process of “flattening the curve” begins much later than the epidemic itself (Figure 4).

Figure 4: Real, projected, and policy-intended epidemic growth curves

The graph (Figure 4) shows in curve C3 the de facto development of the epidemic, in curve C1 the epidemic as it was projected by prominent models, and in curve C2 what policymakers intended to achieve by “flattening the curve”.

In this model (Figure 4), the coronavirus was identified as an epidemic at point t1. At that time, the epidemic had already entered its exponential phase. As explained above (Figure 2), exponential growth processes remain undetected in the beginning. Hidden in data garbage, only specialists may have some clue at the early stages that something is in the making. However, even for the expert does it take time to recognize the exponential nature of the process. At the point of its discovery (t1), an epidemic (C3) is already in full swing and its inception lies back in time. When an observer discovers that something unusual is in the making, sufficient data about the origin of the phenomenon under study are hardly available and thus the researcher must use what is at hand which in the case of a presumed epidemic is testing. In the case of epidemics, tests at that time typically discover an exponential growth process.

When the experts make their projections, the true dimension of the process is necessarily still unknown. Yet the more dramatic the projection of a harmful process, the more likely will it be to get attention from the media, the public, and last but not least from the political decision-makers. This is what happened in the case of the coronavirus epidemic.

As it is known by now, the projections of the effects of the coronavirus epidemic in terms of “cases” that would need medical treatment (C1) were massively overblown compared to its real path (C3), which, of course, was not yet known at the time of the prognosis. Yet it was the projection (C1) in their exaggeration that induced the policymakers to initiate (t2) their measures in order to flatten the curve (C2). Only with a considerable lag could a lockdown take effect. As it has turned out by now, the true dimension of the coronavirus epidemic was much smaller than envisioned. The lockdown came too late to have significant effects on the spread of the infections and even more so, shutting down the economy and bringing society to a standstill was also unnecessary. Even if the pandemic would have attained the dimension as projected, the measures to contain it would have come too late.

In Europe and the United States, the coronavirus was recognized as a potential epidemic at a time, when the real epidemic process was already well advanced so that the observations took place when the growth curve was already at the phase of its exponential growth (t1). At that time, the prominent prognosis model predicted an extreme pandemic (C1) which compared to what later happened (C3) suggested a dimension of cases that would widely exceed the full capacity (FC) of the existing health care systems. The political decision-makers opted for rapid implementation of harsh containment measures (C2) in order to flatten the imagined curve C1 and bring down the number of cases that would need treatment in line with the existing capacities.

Germany, for example, is being celebrated for its successful containment of the epidemic but it gets widely unnoticed that the lockdown and the enforcement of social distancing were implemented on March 23, 2020, when the rate of contagion had already fallen below the threshold of 1 (one) which signifies that the spread of the infection was no longer on the rise. (Figure 5)

Figure 5: Reproduction rate (R) of the coronavirus in Germany

Based on data from the Robert-Koch-Institut, Germany (RKI)

Numbers from the official German Center for disease control and prevention, the Robert Koch Institute (RKI), show that the so-called “reproduction rate” of infections with the Coronavirus began to recede from its peak already on March 11 (b) and had fallen below the critical level of one already days before the lockdown was implemented ©. At that time, the spread of infection had already ended. Not different from what had happened in many other countries, dramatic projections were published in Germany at a time when the reproduction rate was in its phase of acceleration (a) yet when the preventive measures were taken, the speed of contagion was already beyond its peak (b) and at the time when the lockdown was imposed ©, such a measure had already lost all its rationale.

Ineffective Lockdown

The lockdown did not help in containing the epidemic but produced immense damage including the loss of life and health. While governments declared saving lives as the objective of closing large parts of the economy, the measures produced the opposite and will continue to cost material wellbeing and the health of many people even after the lockdown will be lifted.

The measures to flatten the curve began when the number of new infections grew at its maximum speeds already approaching their peak. At this point, the measures taken would necessarily take effect only after an implementation lag so that the lockdown became effective at a time when the measures were useless to contain the epidemic because the number of new infections was already beyond its maximum (see figure 6).

Figure 6: Time Lag of Flattening the Curve through Lockdown

As it is shown in the graph, and as available statistics confirm, the lockdown did not flatten the curve. The lockdown came too late for having an important effect. In addition, the epidemic was less dramatic than perceived at the time when the lockdowns were initiated. Only in a few countries, such as most prominently in Italy and Spain (Figure 7), or in specific places, the peaks were for a short time above the capacity of the health care system which also proved to be more flexible than envisioned.

Figure 7: Total reported fatalities for Italy and Spain, 2016-2020 Week 20

Source: EuroMOMO: Report for Week 20, 2020

According to the EuroMOMO Bulletin, which monitors the death rates in Europe, the majority of the countries did not experience excess mortality in the 2019/2020 winter season and the peaks of mortality stayed below or within the range of the earlier phases of flu outbreaks. In those countries where the peak in 2020 exceeded earlier excess death rates, the tops of the peaks were higher, but its base was smaller which means that also in these countries, the overall number of seasonal deaths has not risen.

The score for all ages (Figure 8) for the reported deaths in Europe shows that there was a higher peak in 2020 than in the preceding years. However, as the base in 2020 was smaller than in the years before, the overall number of deaths was not significantly higher.

Figure 8: Total reported fatalities all ages, Europe 2016 to 2020 week 20

Source: EuroMONO: Report for Week 20, 2020


Even after months of its outbreak, the true nature of the coronavirus remains still unclear. In contrast, the damage of the lockdown is already definite. Although it will take time until the costs and benefits can be fully verified, it is evident that to shut down the economy and to bring social life down to a standstill will cost more lives than were saved. In this sense, the lockdown of 2020 represents a democide, defined as murder by one’s own government.

As we are about to enter a New Great Depression, there is a tragic resemblance between what has happened in the Great Depression of the 1930s and what is taking place now with the devastations of the lockdown. In both cases, governments have prolonged and deepened the agony.

The lockdown of 2020 will most likely go down into history as the first democide of the 21st century, as a global war that governments have inflicted upon their own nations without any rational need in a pure panic — out of a combination of hubris, the arrogance of power, and outright foolishness yet with the full support of their brainwashed populations hyped up by the mass media.

When the full misery of the lockdown becomes visible, we should remember that the politicians did not act alone but with the support of the media, some experts, and the vast majority of the population. What has happened and is still going on is nothing but collective suicide.

Be seeing you

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