Thongchai Thailand


Posted on: April 3, 2020

bandicam 2020-04-03 19-21-58-964







MARCH 2020





As Emeritus of the Johannes-Gutenberg-University in Mainz and long time
director of the Institute for Medical Microbiology, I feel obliged to critically
question the far-reaching restrictions on public life that we are currently taking
on ourselves in order to reduce the spread of the COVID-19 virus. It is expressly not my intention to play down the dangers of the virus or to spread a political message. However, I feel it is my duty to make a scientific contribution to put the current data and facts into perspective. In addition, to ask questions that are in danger of being overlooked in the heat of the debate. My concern is that unforeseeable socioeconomic consequences of the drastic containment measures which are currently being applied in large parts of Europe and which are already being practiced in Germany. My wish is to discuss the advantages and disadvantages of restricting public life in terms of its long term effects.

To this end, I am confronted with five questions which have not been
answered to my satisfaction but which are critically important for a balanced
analysis. I seek your comments on my analysis at your earliest opportunity and, at the same I appeal to the Federal Government to develop strategies that effectively protect risk groups without restricting public life across the board that will likely exacerbate the polarization of society . Sincerly, Professor Emeritus, Dr. Sucharit Bhakdi. I present my analysis below in terms of five items.

ITEM #1: STATISTICS: In infectology, a distinction must be made made between infection and disease and so therefore, only patients with symptoms such as fever or cough should be included in the statistics as new cases. It is not sufficient to test positive for COVID-19 to be counted in the disease statistics.

ITEM #2: DANGER: A number of different coronaviruses have been with us for some time largely unnoticed by the media. If it should turn out that the COVID-19 virus should not be ascribed a significantly higher risk potential than the coronaviruses already circulating, all countermeasures now employed would obviously become unnecessary. The very credible International Journal of Antimicrobial Agents will soon publish a paper that addresses this issue. Preliminary results of the study lead to the conclusion that the new virus is NOT different from the corona viruses of the past in terms of danger. The title of the paper is “SARS-CoV-2: Fear versus Data“.

ITEM #3: DISSEMINATION: According to a report in the Süddeutsche Zeitung, not even the Robert Koch Institute knows exactly how many have tested positive for COVID-19. But there is no doubt that there has been a rapid increase in the number of cases in Germany as the volume of tests increases. It is possible therefore that the virus has already spread unnoticed into the whole of the population. If so, it means that the official death rate of 206 deaths from 37,300 infections by 26 March 2020, at a rate of 0.55%, is too high. This would also mean that it isn’t really possible to prevent the spread of this virus.

ITEM #4: MORTALITY: The fear of a rise in the death rate in Germany (currently 0.55 percent) currently carries an intense media interest. Many people are worried that it could go up to 7% or 10% as it had in Spain and Italy. This fear likely derives from the practice of attributing deaths to the virus only on the basis that patient had tested positive for Covid at the time of his death. This practice is flawed. To attribute death to an agent it must first be determined that the agent played a significant role in the death. The Association of the Scientific Medical Societies of Germany includes this principle in its guidelines saying that to declare a cause of death, the causal chain is more important than the underlying disease. A more critical analysis of medical records should be undertaken to determine how many deaths can be attributed to this virus.



The use of Italy as a reference scenario for evaluating the risk posed to our population by this virus is flawed because the role of the virus in the Italian fatality statistics is unclear. There are external factors at play unique to Italy that made Italy particularly vulnerable. It has not been determined that these factors also apply to Germany. A factor unique to Italy is a high level of air pollution in Northern Italy that would account for more than 8,000 fatalities even without the virus. Air pollution increases the risk of viral lung
diseases in very young and in the very old. A household feature of Italy is the cohabitation of the very young and the very old (27.4% of the population) such that the very young can pass the virus to the very old who are at a high risk of death from the virus. This social feature is also found in Spain at the higher percentage of 33.5%. But it is not found in Germany. Therefore these countries do not serve as a model for understanding the spread and fatality rate of the virus in Germany. Yet another factor that makes it difficult to compare Germany with Italy and Spain is the relatively better equipment in Germany’s health care facility.



The ongoing experience with the SARS-CoV-2 virus and its companion disease, COVID-19, is a matter of balancing health and medical aspects of the associated pandemic against economic costs and consequences of policy responses. Alarmed by estimates from models predicting high morbidity rates (e.g., Edinburgh model), politicians and health bureaucrats imposed lockdowns, mandated social distancing or required citizens to shelter-in-place. As it turned out, worst-case scenarios of healthcare systems and hospitals being overwhelmed were exaggerated with hotspots like Wuhan, Lombardy and NYC bearing the brunt of the wave of illnesses and treatments. The policy responses to this pandemic provide several important lessons.

First, policy makers must understand that estimates derived from models can never be treated as reflecting objective reality of Besides the subjective nature of the assumptions within these models, it is as misguided as it is misleading to treat data sets as if they are devoid of subjectivity. What this means is that claims that public policy is guided by “science and evidence” rings hollow. Science is about testing and questioning what we see around us, not about establishing a consensus.

Second, policy makers should understand and undertake cost-benefit analysis of their decisions before jumping in over their heads. In large measure, the material harm and human costs of the economic collapse are the outcome of what now seems to have been rash judgements based upon faulty modelling.

Third, the goals of policies must be tempered by reality rather than rhetoric or political impulses. For example, Illinois Governor J.B. Pritzker announced economic reopening plan would wait “until we’re able to eradicate” the novel coronavirus.

But to imagine a world that is 100% free of corona or any other virus is an unrealistic goal. As it turns out, moving the number of cases closer to zero will cause the economic and human costs of the collateral damage to rise asymptotically. The way that epidemics or pandemics end is when a large enough proportion of the population acquires immunity either from the antibodies in reaction to infections or the development of a vaccine or treatment.

Vaccines must not be thought of as a magic bullet. On the one hand, vaccines for influenza that have been developed and used for decades are effective in the range of 35% to 45%. On the other hand, the RNA nature of coronaviruses makes them harder to pin down due to more rapid mutations. As it is, there are no vaccines for HIV, Zika, or Ebola despite so much having been invested over such a long period. Indeed, there are no “cures” for these deadly pathogens, only treatments.

Insisting that restrictions should only be lifted only after a vaccine is discovered is also misguided. Discovery of the vaccine is only one part of the puzzle. Even with a miraculous discovery of a vaccine within a few months, it will take considerable time to develop the capacity to produce medically acceptable scales that guarantee purity and safety. Then there is the matter of distributing and inoculating a large enough population. All of this will extend the time horizon into the distant future.

In all events, a question arises concerning the appropriate target ratio is that we should be tracking all the way to zero. Is it the case fatality rate, the infection fatality rate, or the crude mortality rate? None of these ratios are objective ratios determined purely from the data because their interpretation in immunology and virology have a subjective component; but once published these numbers are treated as objective data.

Yet, even the published number for the total number of deaths from COVID-19 is subject to interpretation because the death of persons known to have been infected by the virus creates a strong bias to record the cause of death as COVID-19. In the US, hospitals were guided by a incentive whereby they receive higher financial assistance for such patients that is likely to contribute to an upward bias on reported cases. Similarly, regardless of co-morbidity, instructions were given that all deaths of individuals infected with COV-19 were to be counted as COV-19 fatalities, even if they died with it rather than from it.

Details on the human transmissibility and age-specific impacts of the Coronavirus were reported during an earlier SARS pandemic in 2002-03. Details can be found in this 2009 paper [LINK] by Chris Ka-fai Li and Xiaoning Xu where we learn that “The reemergence of the Coronavirus in humans remains high due to the large animal reservoirs of coronavirus and the genome instability of coronaviruses RNA.”

It is extremely difficult to make accurate estimates of the true risks arising from human encounters with SARS-CoV-2 or any other disease during the initial stages of an epidemic, making it difficult to formulate appropriate policy responses. In all events, pandemics and epidemics first require medical rather than political responses.

In assessing the risk of death from the virus, there are several indicators, i.e., the “case fatality rate”, the “crude mortality rate”, and the “infection fatality rate”.

The Case Fatality Rate (CFR) is the ratio between confirmed deaths and confirmed cases, but it is a poor measure of the overall mortality risk of the disease. As it is, CFR relies on a clear assessment of the number of confirmed cases, something that is very fluid. This is worsened by the fact that the total number of deaths from COVID-19 is subject to interpretation. Anyone testing positive for SARS-CoV-2 will be known to clinical staff, so if they should die, it will be recorded on the death certificate as Covid-19 even if it was not the cause of death.

As such, it is very likely that deaths from COVID-19 are being recorded will tend to give the appearance that Covid-19 is causing an excess number of deaths. Data from the CDC & WHO indicate the CFR for the USA rate is about 5.9%, France 15%, UK 14.4%, Italy 14%, Netherlands 12.75%, Sweden 12.2%, Spain 10%, Mexico 10%, Canada 7.1%, Brazil 6.8%, Ireland 6.3%, & Switzerland 6.1%.

While the negative impact of lockdowns and similar restrictions on movements on economic activity is very clear, it is difficult to know the health impacts. Even so, it is likely that they may also make matters worse for several reasons. First, by simply delaying an inevitable spread of a highly contagious disease from the wide community. Second, keeping people indoors, away from sunshine, fresh(er) air, confined in close quarters with other people, especially if third, they are confined with other people suffering from other communicable diseases like TB that will not be diagnosed so it could have been treated or for infected to be isolated (NB: the “poor” are likely to be hit the hardest, especially in Third World countries). Fourth, extending the timeline of moving towards “herd immunity” might actually lead to a higher overall long-term death rate.

Numbers 1 and 2 will almost surely contribute to a “2nd wave” while number 3 leads to avoidable deaths from otherwise treatable diseases. So, it is not the lifting of a lockdown so much as instituting it in the first place that leads to another round of infections.

In light of the slow progress towards & low probability of discovering, producing and administering an effective vaccine on a large scale, the immediate path towards “herd immunity” is the natural development of antibodies against the SARS-CoV-2 virus. As it is, the evidence from an earlier iteration of the SARS virus suggests that the antibodies formed in that case were effective for up to 12 months & in some cases perhaps as much as 48 months.

For its part, this novel virus does not care about the choice of timing; either accept an initial short, sharp shock or suffer through a series of lockdowns in response to successive waves. Public Choice theory as a subset of economics predicts that politicians and bureaucrats tend to avoid political costs of immediate events inducing them to follow what is in their own best interest, which they believe is to engage in more interventions rather than less. So it is better for them to claim they are being guided by “science and evidence” to issue policy declaration that really reflect their own personal incentives as political agents. In all events, they have no “skin-in-the-game” of the lockdowns since neither they nor most public sector employees will face job losses as have occurred in the private sector.

“Successful” lockdowns will almost certainly be followed by a nasty 2nd wave. In turn, this will almost surely lead to more economic damage while undermining institutional and Constitutional restraints on the democratic process that would impose greater limits on human liberty. Another concern about lockdowns, social distancing mandates, and shelter-in-place orders is if they involve heavy-handed enforcement, there are likely to be heavy impacts on racial minorities, the poor, and those less able to defend themselves.


2 Responses to "DR SUCHARIT BHAKDI"

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  • chaamjamal: Thank you Paul. This is a 50-year study at a decadal time scale. The effective sample size is about 5. There can't be a lot of statistical power in th
  • chaamjamal: Autocorrelation refers to correlations among different time spans of the same time series.
  • chaamjamal: The correlations reported are those between different time series over the same time span.
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