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- Herausgeber: tredition
- Kategorie: Fachliteratur
- Sprache: Englisch
- Veröffentlichungsjahr: 2024
The book evaluates if some public health measures implemented in Germany such as the mask mandate, the social exclusion of the unvaccinated ("2G") and the vaccination mandate for parts of the population were suitable and necessary to control the spread of SARS-CoV-2 so that the temporary restrictions of some fundamental human rights were justified. In addition, the severity and distribution of the most common viral respiratory infections with pandemic potential are compared with the aim to find out if COVID-19 was indeed much more dangerous compared to other coronavirus or influenza virus infections. All analyses are done based on the official data published by the Robert Koch Institute and published data from scientific journals with the aim to provide a comprehensive and not a selective picture. Finally, the freedom of science during the pandemic is critically evaluated.
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Günter Kampf
Evidence-based pandemic management assessment
Focus: Germany
© 2024 Günter Kampf
Druck und Distribution im Auftrag des Autors:
tredition GmbH, Heinz-Beusen-Stieg 5, 22926 Ahrensburg, Deutschland
ISBN
Paperback978-3-384-19683-5
Hardcover978-3-384-19684-2
e-Book978-3-384-19685-9
Das Werk, einschließlich seiner Teile, ist urheberrechtlich geschützt. Für die Inhalte ist der Autor verantwortlich. Jede Verwertung ist ohne seine Zustimmung unzulässig. Die Publikation und Verbreitung erfolgen im Auftrag des Autors, zu erreichen unter: tredition GmbH, Abteilung "Impressumservice", Heinz-Beusen-Stieg 5, 22926 Ahrensburg, Deutschland.
Preface
A thorough review of the measures taken in the context of the COVID-19 pandemic is repeatedly advocated in Germany, whether in the form of a citizens' council (e.g. by Chancellor Olaf Scholz) or a commission of inquiry (e.g. by the FDP). This is urgently needed, as fundamental human rights have been severely restricted for months in order to contain the spread of SARS-CoV-2. Children have been denied face-to-face classes, residents of nursing homes have been left to die alone, seriously ill patients in hospitals have been denied visitors, and in some cities evening walks outside have been banned after 10 pm. Either “science”, the German National Academy of Sciences Leopoldina or individual publicly known scientists served as justification for the coercive measures adopted by politicians.
The publication of the full minutes of the RKI and Crisis Baton now provides important insights. One example is: “The media are talking about a pandemic of the unvaccinated. Not correct from a scientific point of view. Total population contributes” (5 November 2021). The phrase was not corrected for the public, as the Federal Minister of Health used this incorrect wording at every press conference. Later, an expert assessment was withheld from the public, stating that there was no evidence that vaccination made any difference to the transmission of SARS-CoV-2 (12 October 2022). However, this finding was not in line with the government's political course, so that the official expert recommendations were maintained as long as there was no instruction to the contrary from the Federal Ministry of Health.
Both examples show why a comprehensive scientific review is needed. The public was told a lie by politicians and some scientists (“pandemic of the unvaccinated”). This bold statement had already been scientifically refuted in August 2021, but it remained the official truth for the public.
Some public figures, such as Christian Drosten, the most prominent virologist advising the German government, made some interesting statements in July 2024. He was very annoyed that a reinterpretation of the events was being built up afterwards. He found it simply disingenuous to claim in retrospect that none of this was necessary. He wanted to make it clear once again that “we” had correctly assessed from the beginning what was happening and that “we” in Germany had actually done this very well. He was convinced that this had to be said again. There are groups and people who want to present themselves well and who are abusing the pandemic issue. And he thought that there were extreme groups who scandalised the issue and then distorted the message.
This kind of view is very interesting because it allows us to understand how a virologist who has often been in the media as an expert, if not “the expert”, and who has been very close to politicians, including the Federal Minister of Health and the Chancellor, thinks about a critical review of the suitability and necessity of pandemic measures. It is obvious that any retrospective evaluation of a measure with restrictions on fundamental human rights that contradicts his view is “simply disingenuous”. It is not about science, it is not about facts, it is not about reflection and learning, it is not about the simple possibility that the pandemic may not have been correctly assessed, it is not about the possibility that things were not done very well in Germany, it is only about still being right, even if it turns out to be wrong. What a farce. And those who question the suitability and necessity of the measures just want to present themselves and abuse the pandemic for personal publicity. In my opinion, this view makes him unsuitable for a fair, open-minded and scientifically comprehensive evaluation of measures, because there is not even a hint of critical self-reflection that his view might be wrong.
That is why I can only imagine a serious scientific review by those scientists who have hardly been seen in public and who can therefore take a neutral look at the measures, evaluate their epidemiological (e.g. case numbers from the RKI) and scientific justification and assess their suitability. The assessment should be based on the principles of evidence-based medicine as it allows to get the most stringent evaluation of the scientific evidence (part I of the book).
COVID-19 has often been described as a major threat to human health. The language used to describe the disease has been typically alarmist, suggesting seriousness. But how does COVID-19 compare with other pandemic respiratory infections, such as those caused by influenza viruses or other coronaviruses? All available data were collated here to compare the severity and distribution of pandemic viral respiratory infections and to place COVID-19 in its epidemiologically justified place (part II).
It is still said in the public that these measures have been “very effective” in reducing the number of cases. But is this true, and do the official data or studies support this claim? In parts III to V of the book, I have summarised official data published by the RKI and data from studies to describe whether case numbers were lower after the implementation of the mask mandate, during the social exclusion of the unvaccinated (“2G”) and the sectoral COVID-19 vaccination mandate. I tried to present a critical overall picture and had no intention of demonstrating that any position or wording was right or wrong. It did not matter, I just wanted to give an almost complete picture of the data. That is why the reader will find many tables and figures, all supported by the appropriate references, so that any information can be verified.
The freedom of science was also damaged during the pandemic (part VI). There seems to have been a common denominator among those who criticised political decisions, they were not dependent on the state or its institutions. Some articles were published with major methodological flaws, mostly with content supporting specific measures. Some published articles were retracted for dubious reasons. Curiously, this only happened when the content of the manuscript was critical of selected pandemic measures. A continuing medical education article on the effectiveness of masks in public places was subsequently downgraded to a review article for dubious reasons, probably because it described the lack of effectiveness of face masks in the public to prevent viral respiratory infections. And my personal experience with the freedom of science at the University of Greifswald is also described.
What can we learn for the future (parts VII and VIII)? One key aspect is that the often-used slogan “follow the science” is not acceptable, because science is controversy. No one can ever follow controversy and expect a clear path. The better slogan is “follow the evidence”, which is common practice in evidence-based medicine. But the principles of evidence-based medicine have been ignored in pandemic management in Germany, both by prominent scientists and by the German Academy of Sciences, Leopoldina. In future, every measure should be justified by comprehensive scientific evidence and not by selected study picking, taking into account all study results and considering both the expected health benefits and risks. In addition, any measure that becomes mandatory must be scientifically proven to have a significantly better health benefit for the target population than the same measure if it were voluntary. Politicians and scientists who deceive or mislead the public about the effects of measures should be held legally accountable if the measures are mandatory and restrict fundamental human right. If these principles are followed in the future, we can expect a far more professional and evidence-based pandemic management.
I hope that the readers can learn from the experiences that were made in Germany in the last years. And I hope that the proposals are taken into account in case of similar interventions in the future.
Table of contents
Part I – Evidence-based medicine
1. Evidence-based medicine
1.1. Definition
1.2. Approach
1.2.1. Defining the research question
1.2.2. Systematic literature search
1.2.3. Critical appraisal of evidence
1.2.4. Evaluating the specific situation
1.2.5. Self-critical evaluation
1.3. Categories in evidence-based medicine
1.4. Methods for determining effectiveness
1.4.1. Laboratory and animal studies
1.4.2. Case reports and case series
1.4.3. Observational studies
1.4.4. Randomised controlled trials (RCTs)
1.4.5. Systematic reviews
1.5. Importance of levels of evidence for recommendations
1.6. Relevance for COVID-19 interventions
Part II - Severity and distribution of viral pandemics
2. Viral respiratory infections
2.1. Infections until September 2020 in Germany
2.2. Pandemic years 2020 to 2023
2.3. History of severe influenza epidemics
2.3.1. Italy in 2000
2.3.2. Germany in 2015
2.3.3. Germany in 2017
2.3.4. Mallorca in 2019
2.4. Severe respiratory infections in Germany
2.5. Phases in the spread of a respiratory virus
3. Public statements on COVID-19
3.1. TV speech of 18 March 2020
3.2. Other public statements
3.3. Implications of these statements
4. Severity and distribution
4.1. Background
4.2. Classification by severity
4.2.1. Asymptomatic infection
4.2.2. Mild or moderate infection
4.2.3. Severe infection
4.2.4. Critical infection
4.2.5. Mapping to epidemiological case numbers
3.3. Classification by distribution
5. Asymptomatic cases
5.1. COVID-19
5.1.1. Asymptomatic on the day of examination
5.1.2. Persistently asymptomatic cases
5.1.3. Presymptomatic cases
5.1.4. Proportions by vaccination status
5.1.5. Proportions by age
5.2. Other coronavirus infections
5.3. Influenza virus infections
6. Mild or moderate cases
6.1. COVID-19
6.1.1. Proportions
6.1.2. Proportions by vaccination status
6.2. Other coronavirus infections
6.3. Influenza virus infections
7. Severe cases
7.1. COVID-19
7.1.1. Hospitalised cases (studies)
7.1.2. Hospitalised cases (official data)
7.1.3. "Severe courses" in hospitalised cases
7.2. Other coronavirus infections
7.2.1. Hospitalised cases
7.2.2. Severe courses
7.3. Influenza virus infections
7.3.1. Hospitalised cases (studies)
7.3.2. Hospitalised cases (official data)
7.3.3. Severe cases
8. Critical cases
8.1. COVID-19
8.2. Other coronavirus infections
8.3. Influenza virus infections
9. Fatal cases
9.1. COVID-19
9.1.1. Bergamo deaths
9.1.2. Number of deaths
9.1.3. Cause of death among “COVID-19 deaths”
9.1.4. Case fatality rate
9.1.5. Infection fatality rate
9.2. Other coronavirus infections
9.2.1. Case fatality rate
9.3. Influenza virus infections
9.3.1. Case fatality rate (all cases)
9.3.2. Case fatality rate (hospitalised cases)
9.3.3. Infection fatality rate
10. Overall picture: severity
10.1. Asymptomatic cases
10.2. Mild or moderate cases
10.3. Severe cases
10.4. Critical cases
10.5. Fatal cases
11. Overall picture: distribution
11.1. Worldwide case numbers
11.2. Case numbers in Germany
12. Classification of COVID-19
13. The 2017 pandemic plan
13.1. Weak population immunity
13.2. High morbidity
13.3. High mortality rate
13.4. Extreme strain on healthcare system
Part III – Mask mandates
14. History of the mask mandates
14.1. National Pandemic Plan (2017)
14.2. Robert Koch Institute (January 2020)
14.3. Jena leads the way (April 2020)
14.4. Community masks recommended (April 2020)
14.5. Sectoral mask mandate (April 2020)
14.6. Extension of the mandatory use of masks (May 2020)
14.7. “Never question the mask mandate” (July 2020)
14.8. Community masks are history (January 2021)
14.9. FFP2 respirators partially mandatory (October 2022)
14.10. Gradual abolition of the mask mandate
15. Examples of mask mandates
15.1. Retail sector
15.2. Public transport
15.3. Long-distance transport
15.4. Air transport
15.5. Medical practices and clinics
15.6. In the open air
15.7. Restaurants
15.8. Schools
15.9. Brothels
16. Public statements on masks and efficacy
16.1. Politicians and officials
16.1.1. "Masks can reduce the risk of infection".
16.1.2. "Masks are especially effective".
16.1.3. "Masks are very effective".
16.1.4. "The benefits of masks are very great and undisputed".
16.1.5. "Masks keep infection rates down"
16.1.6. "Masks protect against long COVID".
16.1.7. "Masks prevent needless deaths".
16.1.8. "Masks prevent stricter measures".
16.1.9. "Masks prevent a major lockdown"
16.1.10. "Overwhelming evidence for the use of mandatory masks”
16.1.11. "Infection of the mask wearer is reduced”
16.2. Professional societies
16.2.1. German Society for Hospital Hygiene
16.2.2. Germany Society for Hygiene and Microbiology
16.2.3. Paediatric societies
16.2.4. Commission for Hospital Hygiene and Infection Prevention
16.3. WHO
16.3.1. Influenza pandemic
16.3.2. COVID-19 pandemic
16.4. The pandemic plan of Germany
17. What does "effective" mean?
17.1. Effective filtration in the laboratory
17.2. Effective filtration on the face
17.3. Effectiveness through reduced transmission
18. Types of masks
18.1. Community masks
18.2. Medical and surgical masks
18.3. FFP2 respirators
18.4. Tight fit of FFP respirators
19. Laboratory studies
19.1. Cotton masks
19.2. Polyester masks
19.3. Silk masks
19.4. Medical and surgical masks
19.5. FFP2 or N95 respirators
19.6. Comparative studies
19.6.1. Particle reduction
19.6.2. Protection against SARS-CoV-2 in the face
19.7. Virus detection on masks
19.8. Importance of laboratory studies
20. Observational studies
20.1. Definition
20.2. Disadvantage: Confounding
20.3. Results of individual studies
20.3.1. Self-protection
20.3.2. Studies in specific population groups
20.3.3. Schools
20.4. Results of reviews
20.5. Review article in the German Medical Journal
21. Randomised Controlled Trials
21.1. Definition
21.2. Benefit: Causality
21.3. Disadvantage: variable adherence to the intervention
21.4. Disadvantage: variable prevalence of infection
21.5. Trials in the healthcare setting
21.5.1. Self-protection with community masks
21.5.2. Self-protection with surgical masks
21.5.3. Self-protection with N95 respirators
21.5.4. Assessment of prevalence and compliance
21.6. Studies outside the health sector
21.6.1. Self-protection with surgical masks
21.6.2. Protection of contact persons with surgical masks
21.6.3. Self-protection and protection of others with surgical masks
21.6.4. Assessment of prevalence and compliance
21.7. Meta-analyses of RCTs
21.7.1. Relative risk
21.7.2. Community mask versus no mask
21.7.3. Community mask versus surgical mask
21.7.4. Surgical mask versus no mask
21.7.5. N95 respirator versus no mask
21.7.6. N95 respirator versus surgical mask
21.7.7. Assessment of prevalence and compliance
22. The 2023 Cochrane meta-analysis
22.1. Comment in the German Medical Journal
22.1.1. The headline
22.1.2. Critical appraisal
22.2. Cochrane editor's statement
23. Effect according to official data
23.1. Impact of the first mask mandate in 2020
23.2. Hotspot regulation in March 2022
23.2.1. 7-day incidence in Hamburg
23.2.2. 7-day incidence in Mecklenburg-Western Pomerania
23.3. Case numbers, variants and vaccination coverage
23.4. Hospitalisations, variants and vaccination coverage
23.5. Germany, United Kingdom and Denmark
24. Side effects of mask use
24.1. At least one adverse health effect
24.2. General discomfort
24.3. Breathing difficulties
24.4. Headaches
24.5. Itching
24.6. Skin reactions
24.7. Impaired communication
24.8. Allergic skin reaction to formaldehyde
24.9. Blood oxygen and carbon dioxide
24.10. Other side effects
24.11. Adverse reactions in children
24.11.1. Results from Germany
24.11.2. Effects on the heart and circulation
25. Fundamental human rights affected
25.1. General right of personality
25.2. Physical integrity
25.3. Freedom to choose a profession
25.4. General freedom of action
25.5. Freedom of assembly
25.6. Freedom of religion or belief
26. Legitimation of a mask mandate
27. Suitability
27.1. Suitability criteria
27.2. Suitability according to study design
27.3. Community masks
27.4. Surgical masks
27.4.1. Wearing masks correctly
27.5. FFP2 respirators
27.5.1. Correct respirator fit
28. Necessity
28.1. Recommendation or mandate?
28.2. The immunity of recovered people
28.3. Appropriateness
29. Proportionality
29.1. Community masks
29.1.1. Physical integrity (health hazards)
29.1.2. Overall conclusion
29.2. Surgical masks
29.2.1. Physical integrity (health hazards)
29.2.2. Environmental impact
29.2.3. Overall conclusion
29.3. FFP2 respirators
29.3.1. Physical integrity (health hazards)
29.3.2. Ecological risks
29.3.3. Overall conclusion
30. Was there political pressure?
30.1. Example: Denmark
30.2. Example: Germany
30.3. Example: Great Britain
30.3.1. Mandatory masks in schools
30.3.2. Delayed publication of Cochrane review
30.3.3. Scientists are dangerous actors
31. Evaluation of the governmental expert committee
32. Attempts of an explanation
32.1. Masks only marginally effective at best?
32.2. Poor-fitting masks?
32.3. Masks in the wrong places?
32.4. Are FFP2 respirators useless?
32.5. Is a mask mandate necessary at all?
Part IV - Social exclusion of the unvaccinated (“2G”)
33. History of “2G”
34. Developments around “2G”
34.1. SARS-CoV-2 variants
34.1.1. Increased transmissibility of the Omicron variant
34.1.2. Milder course with the Omicron variant
34.1.3. More asymptomatic cases due to Omicron variant
34.1.4. Weaker vaccine efficacy against the Omicron variant
34.2. Full vaccination and booster
34.3. Hospitalised COVID-19 cases with acute respiratory infection
35. Public statements
35.1. Politicians and officials
35.1.1. "Unvaccinated people are playing with the lives of others".
35.1.2. "Unvaccinated people are more likely to transmit the virus".
35.1.3. "91.5% of new infections among the unvaccinated"
35.1.4. "Unvaccinated persons are involved in most infections"
36. Goal: Reduction of COVID-19 case numbers
36.1. "Break the dynamics of infection".
36.2. Hypothesis for declining case numbers
36.3. Case numbers in vaccinated and unvaccinated persons
36.3.1. Findings from vaccine trials - symptomatic cases
36.3.2. Data from Germany - symptomatic cases
36.3.3. Findings from vaccine trials - asymptomatic cases
36.3.4. Data from the UK - all cases
36.4. Has the number of cases been reduced by 2G?
37. Goal: Prevention of transmission
37.1. "Reduce the likelihood of infection".
37.2. Reduced transmission hypothesis
37.3. Viral load according to vaccination status
37.4. Virus persistence in relation to vaccination status
37.5. Source of outbreak: fully vaccinated patient
37.6. Secondary attack rates by vaccination status of the index case
37.7. Hot spots for high-risk contacts
37.8. Outbreaks in vaccinated and recovered persons
37.8.1. 2G in bars: 10% - 60% infected
37.8.2. 2G in clubs: 1% - 22% infected
37.8.3. 2G at choir rehearsals and concerts: 12% - 40% infected
37.8.4. Christmas party at "almost 2G": 73% infected
37.8.5. 2G Nursing home: 39% of residents infected
37.9. How should the outbreaks be interpreted?
37.10. Were transmissions reduced by 2G?
38. Goal: Reduction of severe case numbers
38.1. "Avoid overburdening the health system"
38.2. Were severe cases prevented?
38.2.1. Hospitalised COVID-19 cases
38.2.2. COVID-19 cases in intensive care units
39. Goal: Increased pressure on the unvaccinated
40. Fundamental human rights affected
41. Legal frame of “2G”
42. Suitability
42.1. Limiting the incidence of infection
42.2. Preventing health system overload
42.3. Achieving higher vaccination coverage
43. Necessity
43.1. The "least restrictive means" principle
43.2. Impact of 2G
43.3. Targeted mandatory testing likely to be more effective
43.4. Is 2G needed at all with a high vaccination rate?
43.5. National pandemic plan and contact restrictions
44. Proportionality
44.1. Simulation study: expected effect of 2G very small
44.2. Decision on retail trade (Lower Saxony)
44.3. Decision on outdoor sports (Lower Saxony)
Part V – COVID-19 vaccination mandates
45. Mandatory COVID-19 vaccination
45.1. Germany
45.1.1. Health and care sector
45.1.2. German Armed Forces
45.1.3. The general population
45.2. Austria
45.3. Switzerland
45.4. France
45.5. Great Britain
45.6. USA
45.6.1. Universities
45.6.2. The military
46. Public statements
46.1. Politicians: "Vaccinated people are not dangerous"
46.1.1. Janosch Dahmen, Green Party
46.1.2. Markus Söder, CSU
46.2. Politicians about a vaccination mandate
46.2.1. Christian Lindner, FDP
46.2.2. Karl Lauterbach, SPD
46.2.3. Robert Habeck, Green Party
46.2.4. Michael Kretschmer, CDU
46.2.5. Olaf Scholz, SPD
46.3. Politicians about vaccine safety
46.3.1. Karl Lauterbach, SPD
46.3.2. Michael Theurer, FDP
47. Fundamental human rights affected
47.1. Physical integrity
47.2. Human dignity
47.3. Principle of equal treatment
47.4. Free choice of occupation or job
47.5. Coercive vaccination was considered
48. Legal frame of a vaccination mandate
48.1. Is the objective legitimate?
48.1.1. Protection of the vaccinated person
48.1.2. Protecting the health of others
48.1.3. Non-vaccination as a criminal offence?
49. Suitability
49.1. Statements made by the Scientific Service
49.2. "High efficacy of vaccines"
49.3. "Risk of virus transmission greatly reduced"
49.3.1. Further data by 22 December 2021
49.3.2. Further data up to 7 April 2022
49.4. “Protect persons without adequate self-protection”
49.5. "Lower viral load, lower risk of infection"
49.5.1. Data up to 22 December 2021
49.5.2. Additional data by 7 April 2022
49.5.3. Further data until 31 December 2022
49.5.4. Detection of infectious virus (cell culture)
49.6. Duration of viral shedding
49.7. "Vaccinated people are less likely to be infected"
49.7.1. Case numbers from registration studies
49.7.2. Case numbers in Germany - symptomatic cases
49.7.3. Case numbers in the United Kingdom - all cases
49.7.4. Breakthrough infections in Germany
50. Necessity
50.1. Health and care sector
50.1.1. Healthcare sector
50.1.2. Nursing homes
50.2. The German Armed Forces
50.3. General population
50.3.1. Dominance of the Omicron variant
50.3.2. One consequence: milder courses of disease
50.3.3. Another consequence: lower vaccination efficacy
51. Proportionality
51.1. Vaccine adverse reactions
51.1.1. Justification of low risks
51.1.2. Early warning of serious adverse reactions
51.1.3. Disadvantages of passive monitoring systems
51.1.4. Data from the Paul-Ehrlich-Institute
51.1.5. Data from occupational health insurance funds
51.1.6. Data from the KBV
51.1.7. Data from the InEK
51.1.8. IgG4: example of an unexpected effect
51.1.9. Overall picture
51.1.10. The legal mandate
51.1.11. Recent benefit-risk-assessments
51.1.12. Opinions of the Federal Constitutional Court
51.2. Non-specific effects of vaccines
51.2.1. Beneficial non-specific effects
51.2.2. Harmful non-specific effects
51.3. Expected severe cases and deaths
51.4. Protection of contact persons
51.5. Relief for the health system
51.6. All cause and non-COVID-19 excess deaths
51.6.1. Excess deaths in Germany during the pandemic
51.6.2. Excess deaths in other countries
51.6.3. Non-COVID-19 mortality rates according to vaccine doses
51.6.4. Cancer excess deaths in Japan during the pandemic
51.6.5. mRNA vaccines cause heart dysfunctions
51.6.6. Excess mortality wrongly counted as COVID-19 mortality
51.6.7. The political ignorance
52. Motivation for COVID-19 vaccination
52.1. Health and care sector
52.2. Military
52.3. General population
53. Reasons against COVID-19 vaccination
53.1. Health and care sector
53.1.1. Questionable safety of vaccines
53.1.2. Lack of confidence
53.1.3. Other risk perceptions
53.1.4. Different sense of responsibility
53.2. Military
53.3. The general population
54. Confidence in vaccine manufacturers
54.1. Information on efficacy
54.2. Offer of vaccination to the control group
54.3. Shortcomings in the approval study
55. Confidence in regulators
55.1. Vaccine or drug?
55.1.1. Preclinical studies
55.1.2. Laboratory studies
55.1.3. Pharmacovigilance
55.2. Active substance content
55.3. Active substance purity of mRNA vaccines
55.4. Impurities
55.4.1. Host cell protein
55.4.2. Steel particles
55.4.3. Excessive DNA amounts
55.4.4. National test parameters for batches
55.5. Rotashield: late discovery of side effect
55.6. Pandemrix: Side effect concealed?
56. Confidence in the Paul Ehrlich Institute
56.1. "Adverse reaction rates are not batch dependent".
57. Confidence in the Robert Koch Institute
57.1. "The vaccine degrades after a short time".
57.2. Vaccine in the blood
57.3. Vaccine in lymph nodes
57.4. Vaccine in leg muscle
57.5. Vaccine in heart muscle
57.6. Overall picture
58. Confidence in the Standing Committee on Vaccination
58.1. First vaccination recommendation for children and adolescents
58.2. Statements by politicians
58.3. Statements from STIKO members
58.4. The STIKO's concession
58.5. Current recommendations for children and adolescents
59. How can confidence be rebuilt?
59.1. Confidence in effectiveness
59.1.1. Publication of raw data
59.1.2. Efficacy by severity and risk profile
59.2. Confidence in safety
59.2.1. Low threshold system for safety signals
59.2.2. Take affected patients seriously
59.2.3. Reporting of suspected cases
59.2.4. Clarification of post-vaccination deaths
59.3. Confidence in government institutions
59.3.1. Historical example: smallpox vaccination
59.3.2. Treatment of vaccine injuries
59.3.3. The state has become the victim's adversary
59.3.4. The state must support victims
59.4. Confidence in “science”
59.4.1. Containment, severity and mandatory vaccination
59.4.2. The role of science: critical evaluation of evidence
59.4.3. National Academy of Sciences
59.4.4. Science is controversy
Part VI – Freedom of science during the pandemic
60. Science is free
60.1. German Commission for UNESCO
60.2. The Bonn Declaration on Freedom of Research
60.3. German Research Foundation
61. Continuing medical education
62. High-impact publications
62.1. Masks: "most effective measure"
62.2. Masks: "Outbreaks safely prevented"
63. Face masks and the German Medical Journal
63.1. Review paper on efficacy
63.2. News on mask studies
64. The Leopoldina
64.1. The Leopoldina's self-image
64.2. The 7th ad hoc statement
64.2.1. Comparison with Ireland
64.2.2. The scenario without a hard lockdown
64.2.3. The Working Group
64.2.4. Real experts and pseudo-experts
64.2.5. Independence of the Working Group?
64.2.6. Pretence of professionalism?
64.3. Reactions to the statement
64.3.1. Open letter by Michael Esfeld
64.3.2. Statement by Jörg Friedrich, Die Welt
64.3.3. Resignation of Stephan Luckhaus
64.3.4. Resignation of Thomas Aigner
64.4. The boundary between science and politics
64.5. Freedom of science?
65. Public service as a conflict of interest?
65.1. Conflict of interest "Industry”
65.2. "State employment" as a conflict of interest?
65.3. Academic Freedom Network
65.4. Who criticised the pandemic policy?
65.4.1. Matthias Schrappe
65.4.2. Klaus Püschel
65.4.3. Sucharit Bhakdi
65.4.4. Stefan Hockertz
65.4.5. René Gottschalk and Ursel Heudorf
65.4.6. Ines Kappstein
65.4.7. Wolfgang Wodarg
65.4.8. Franz Allerberger
65.4.9. Alexander Kekulé
65.4.10. The common denominator
65.4.11. The Gap
65.5. The role of science marketing
65.6. Clarifying suspect serious adverse events
65.7. Freedom of science?
66. Overt political pressure
66.1. COVID-19 Vaccination of 12-17 year olds
66.1.1. The Standing Committee on Vaccination (STIKO)
66.1.2. Appeal by a professional society
66.1.3. The 124th German Medical Congress
66.1.4. Jens Spahn
66.1.5. Markus Söder
66.1.6. Karl Lauterbach
66.1.7. Saskia Esken
66.1.8. Manfred Lucha
66.2. Mask mandate in public places
66.3. Freedom of science?
67. Fact checkers and science
67.1. Example: Facebook
67.2. Example: Twitter
67.3. What is "harmful content"?
67.4. Right to freedom of expression
68. Retracted publications
68.1. Letter to the Editor option
68.2. Correction option
68.3. The possibility of retraction
68.3.1. Reasons for retraction
68.3.2. Ethical guidelines
68.4. Publications on COVID-19
68.5. Example: Carbon dioxide under masks
68.5.1. Suitability of the measuring device
68.5.2. Was inhaled air measured?
68.5.3. The conclusions
68.5.4. Other expert comments
68.5.5. Are the criteria for retraction met?
69. Personal experience
Part VII – Parameters for pandemic management
70. Characteristics of influenza waves
70.1. Evidence from sentinel practices
70.2. Number of cases of notifiable diseases
70.3. Parameters in the national pandemic plan
71. Objectives of interventions
71.1. Reducing the number of COVID-19 cases
71.2. Reducing the number of severe cases
71.3. Avoid overburdening the health care system
72. Effect on transmission dynamics
72.1. Parameter: R-value
72.2. A senseless wish of the Minister of Health
72.3. The R-values for the years 2020 to 2022
72.4. Outlook for the future
73. Effect on case numbers
73.1. Case definitions
73.1.1. WHO COVID-19 case definition
73.1.2. ECDC COVID-19 case definition
73.1.3. Robert Koch Institute COVID-19 case definition
73.1.4. Influenza case definitions
73.2. Early 2020: exaggerated presentation of case numbers
73.3. Incidence values in 2020 and 2021
73.3.1. Incidence values in the Infection Protection Act
73.3.2. Politicians insisted on the incidence value
73.4. Importance of PCR in case definition
73.4.1. Cases with low or no infectivity
73.4.2. False positive results
73.5. Meaningfulness of counting cases
73.5.1. Objective: overview of infection dynamics
73.5.2. Objective: Identify potential sources
73.5.3. Goal: Limiting the dynamics of transmission
74. Effect on the number of severe cases
74.1. Relevant parameter: Severe cases
74.2. Risk profile for severe courses
74.3. Targeted protection for those most at risk
Part VIII – Perspectives for the future
75. Society and the mask
75.1. "Universal masking"?
75.2. The mask as symbol
75.2.1. Expression of civic responsibility
75.2.2. A symbol of freedom and respect
75.2.3. Symbol of panic
75.2.4. Fear signal and symptom of obedience
75.3. Voluntary rather than mandatory
75.4. There is no zero risk
76. Society and “2G”
76.1. Public statements
76.1.1. "Unvaccinated people do not belong in the centre of society".
76.1.2. "Now we need a sharp wedge to divide society"
76.1.3. "2G is not an injustice"
76.1.4. "2G is not discrimination"
76.1.5. "2G privileges poison the social climate"
76.1.6. "2G is a targeted exclusion of citizens"
76.1.7. "Bringing society back together"
76.2. Vaccinated people think more discriminately
76.3. Dignity, human rights and fundamental freedoms
76.4. Outlook
77. Society and vaccination mandate
77.1. Health and care
77.2. German armed forces
77.3. Society
77.4. Critical self-reflection
77.5. Focus on vulnerable groups
77.6. Outlook
78. Perspectives for the future pandemic management
78.1. Case definition: only with typical symptoms
78.2. Focus on severe and critical cases
78.3. Multidimensional set of indicators
78.4. Testing only to guide treatment
78.5. Ongoing review of the target
78.6. Personal outlook
Glossary
References
About the author
Part I – Evidence-based medicine
1. Evidence-based medicine
For centuries, the medicine has been based on the knowledge and experience of healers. However, as research progressed, individual approaches to treatment were increasingly complemented by scientifically based treatment concepts. In 1990, the term "evidence-based medicine" was coined in the Anglo-American world. This approach found its way into the German-speaking world in the mid-1990s.
1.1. Definition
Evidence-based medicine is the conscientious, explicit and judicious use of the best current external scientific evidence to make decisions about the medical care of individual patients. The practice of evidence-based medicine involves the integration of individual clinical expertise with the best available external evidence from systematic research.
Evidence-based medicine therefore aims to analyse and evaluate scientific knowledge and evidence from different sources according to clear methodological principles that are internationally and nationally accepted. The focus is on the results of clinical research. The result is, for example, high-quality guidelines from which clinically active doctors can extract the best possible "knowledge ingredients" for a clearly justified decision in individual cases.
The German Network for Evidence-based Medicine (www.ebm-netzwerk.de) has been in existence since 1998, and Cochrane Germany was founded in 1999 (www.cochrane.de). Cochrane is considered to be the first and most important global network for solving the problem of the growing flood of information in medicine. It aims to make the transfer of knowledge from clinical research to clinical practice more transparent and easier by systematically searching for, evaluating and presenting the results of trials in an easily accessible way.
The principles of evidence-based medicine serve as the authoritative basis of assessment for the Joint Federal Committee, which decides on the eligibility of statutory health insurance members in Germany. The principles of evidence-based medicine have also become the standard for decision-making in infection prevention. The Commission for Hospital Hygiene and Infection Prevention at the Robert Koch Institute develops recommendations for hospitals, nursing homes and doctors' surgeries on various types of infection, such as post-operative wound infections, pathogens such as C. difficile or other issues such as hand hygiene. In doing so, it fulfils the task assigned to it by Section 23 of the German Infection Protection Act to develop recommendations for the prevention of nosocomial infections in hospitals and other medical facilities based on the latest medical knowledge.
1.2. Approach
In addition to individual clinical experience and the values and wishes of the patient, the current state of clinical research is an essential pillar for the optimal treatment of patients. The process of evidence-based medicine is divided into five steps.
1.2.1. Defining the research question
In this step, the clinical problem is translated into a question to be answered by scientific research. In the context of COVID-19, the questions can be formulated in very different ways, thus influencing the answer. The wearing of masks is an example of this.
"Do masks protect?
This very general question is often asked in the media. It is a yes-no question that is inappropriate from a scientific point of view. This is because a protective effect is usually variable and can be very low or very high. In addition, it does not distinguish which masks are meant, whether the mask protects the wearer or the other person, what the protection should be against (e.g. droplets, specific pathogens or infections) and the environment in which the protective effect is expected. If you are talking only about droplet filtration, you may even be able to attribute a certain level of protection to certain community masks.
"Do surgical masks protect against SARS-CoV-2 viruses?
This question is more nuanced because it defines a type of mask and the protective effect is related to a specific virus. To answer this question, it was possible to analyse all the studies that examined the filtration performance of surgical masks against SARS-CoV-2. However, it remains unclear whether the protective effect relates to the person trying to protect themselves or to a person who is a SARS-CoV-2 carrier trying to protect others in their immediate environment. It is also unclear whether the surgical masks are intended to protect against actual infection or against COVID-19 without knowing the associated symptoms, which are not the same thing.
"Does wearing surgical masks in shops protect against COVID-19?
This question may sound similar to the previous one, but it refers to the disease COVID-19 rather than the virus itself. As a result, the question can only be answered by using studies that have investigated a health benefit from wearing surgical masks (fewer COVID-19 cases). In addition, the setting is clearly defined (shop). Trials in which the health benefit of wearing surgical masks was investigated in a COVID-19 ward in a hospital are therefore irrelevant for this question. This is because the exposure risk in a shop is completely different (few virus carriers, short contact times, little face-to-face contact, little talking) from that in a COVID-19 ward. However, this question still leaves open the question of whether the mask wearer or other people should be protected.
"Will wearing a surgical mask in a shop protect me from COVID-19?
In addition to the previous question, this question also narrows down the person for whom a protective effect is defined.
"To what extent will wearing a surgical mask in a shop protect me from COVID-19?"
Finally, the question may include a quantification of the protective effect to describe the magnitude of the expected health benefit. At this point, the answer can no longer be a simple "yes" or "no". The result can now range from 100% (complete protective effect) to 0% (no protective effect).
1.2.2. Systematic literature search
In this step, the scientific literature is searched using a comprehensible strategy and then systematically analysed. The aim is not only to consider literature that supports a particular hypothesis, but also to obtain an undistorted, unbiased overview of the current state of knowledge on specific issues.
Firstly, search terms (e.g. 'face mask' and 'protection') and databases of scientific literature are defined (e.g. 'PubMed'; the largest publicly available database of scientific medical literature). The definition of the search terms is very important as the results of the search depend heavily on the choice of terms used. You will then receive a list of results. This may include hundreds or thousands of publications, and in science only published results count. The search terms may have been too general and need to be refined. As part of the literature search, you may be able to reduce the size of the publication list by refining your search terms. However, no studies are excluded at this stage on the basis of defined formal or content criteria.
1.2.3. Critical appraisal of evidence
The next step is to define the criteria for including or excluding studies from the analysis. If only studies with SARS-CoV-2 are searched for and some studies with SARS-CoV-1 are displayed in the publication list obtained in the first step, the latter do not fulfil the inclusion criteria and are therefore not included. To make the search comprehensible and reproducible for all other researchers, the total number of hits and the number of excluded studies, including the reasons, are usually reported. This is the only way to ensure that other researchers can perform the same search if they have doubts about the results.
All trials that meet the inclusion criteria are then analysed, ideally by two independent scientists. This is often done using a standardised procedure that all reviewers use to assess the strengths and weaknesses of each study's design. Once the review is complete, the results are summarised and graded according to their strengths and weaknesses, for example using the internationally recognised GRADE criteria.
The evaluation of the study design is particularly important when considering the causality of a risk factor or intervention. Observational studies (cohort studies or case-control studies) are often conducted in which the benefit of an intervention is determined retrospectively for a specific time or in a specific setting, such as a hospital. The same data are collected on a suitable control group for comparison. However, observational studies are only suitable for proving causality of an intervention under certain conditions [1]. Therefore, if fewer SARS-CoV-2 transmissions were measured when masks were worn in one period compared with another, it is not certain that this effect can be explained by the use of masks.
Prospective randomised controlled trials (RCTs) are generally better at establishing the causal relationship between an intervention and its effect.
The quality of the evidence therefore depends mainly on the study design. Systematic reviews, in which as many randomised controlled trials as possible have been analysed (e.g. Cochrane reviews), provide the greatest certainty about a topic.
1.2.4. Evaluating the specific situation
Since medicine is often about the most promising treatment option, the treating physician should then weigh the knowledge gained against the specific clinical situation of his or her patient and assess which treatment or prevention concept is likely to provide the best benefit for this patient.
1.2.5. Self-critical evaluation
The self-critical evaluation of the treating physician is part of the procedure and may lead to an adaptation of the previous procedure.
1.3. Categories in evidence-based medicine
In evidence-based medicine (EBM), each recommendation is assigned to a category that indicates the degree of certainty with which a health benefit can be expected from the recommended intervention. The evaluation of the study design is therefore particularly important with regard to the causality of a risk factor or intervention (Figure 1).
1.4. Methods for determining effectiveness
1.4.1. Laboratory and animal studies
The lowest category includes laboratory studies or animal studies. This includes, for example, determining the filtration performance of masks under standardised conditions. These studies do not allow an assessment of effectiveness in humans in real life, but can provide important and fundamental information about a possible effectiveness in humans.
1.4.2. Case reports and case series
Case reports and case series are considered to be of higher quality because they provide evidence from observations in humans, but lack a control group.
Figure 1: Levels of evidence; results from systematic reviews are the most informative; cohort studies and case-control studies are observational studies; *e.g. Cochrane reviews.
1.4.3. Observational studies
A better way to measure the health benefits of masks is through a reduction in respiratory infections. Case-control and cohort studies are observational studies. In this design, the benefits of an intervention are determined for a specific period of time or in a specific setting, such as a hospital. The same data are collected on a suitable control group for comparison. However, observational studies are only suitable for proving the causality of an intervention under certain conditions [1]. Therefore, if fewer transmissions of SARS-CoV-2 were measurable with mask use in one period compared with another, it is not certain that this effect can be explained by mask use or by other confounding.
1.4.4. Randomised controlled trials (RCTs)
Prospective randomised controlled trials (RCTs) are the most reliable way of establishing the causal relationship between an intervention and its effect. For this reason, RCTs have a higher level of evidence than observational studies (Figure 1). In all other designs, the observed change in the endpoint (e.g. viral respiratory infection) may be explained by the mask or other confounding factors, and causality is less clear.
1.4.5. Systematic reviews
Systematic reviews and meta-analyses, in which as many randomised controlled trials as possible are analysed (e.g. Cochrane reviews), provide the greatest certainty about a question. For this purpose, all available trials on a specific research question are evaluated according to a predefined procedure. The assessment is carried out by at least two authors who are independent of each other. In addition, the quality of the studies is assessed to determine how error-prone the method may have been in relation to the research question.
1.5. Importance of levels of evidence for recommendations
The Commission for Hospital Hygiene and Infection Prevention at the Robert Koch Institute uses these levels of evidence to develop recommendations for interventions aimed at preventing nosocomial infections through optimal and scientifically proven treatment.
Each recommendation is assigned to a category, which indicates how certain it is that the recommended intervention will have an infection-preventing effect. The study design has the greatest influence on the classification. The categories used are as follows (Table 1). Category IA and IB recommendations are strongly recommended for all hospitals. Category II recommendations should be introduced or implemented in many hospitals. Category III measures are not recommended or are considered unresolved. Category IV includes legal requirements that do not require scientific justification [2].
1.6. Relevance for COVID-19 interventions
It is quite possible to conduct a systematic literature review on defined questions within a short period of time using publicly available databases and search engines.
Category
Relevance
IA
This recommendation is based on well-designed systematic reviews or single high-quality randomised controlled trials.
IB
This recommendation is based on clinical or high-quality epidemiological studies and rigorous, plausible, and reproducible theoretical inferences.
II
This recommendation is based on indicative studies or investigations and rigorous, plausible and reproducible theoretical deductions.
III
Interventions for which there is insufficient or conflicting evidence of effectiveness and therefore no recommendation is possible.
IV
Requirements, measures and procedures to be observed by generally applicable legislation.
Table 1: Categories for recommendations of the Commission for Hospital Hygiene and Infection Prevention at the Robert Koch Institute [2].
For example, in 2020, Chu et al. were able to analyse a large number of observational studies on the benefits of masks in health care and public spaces [3]. In my opinion, it would also have been possible for Germany to carry out an open and systematic evaluation of the literature on the following questions
Can surgical masks or FFP2 respirators worn in public places reduce the number of COVID-19 cases, severe COVID-19 cases or deaths?
Can lockdowns imposed in addition to other measures reduce the number of COVID-19 cases, severe COVID-19 cases or deaths?
I have deliberately included the number of severe COVID-19 cases or deaths here because asymptomatic or mild cases represent the vast majority of COVID-19 cases and do not result in hospitalisation or intensive care for the patients concerned, although these people can transmit SARS-CoV-2 with mild symptoms [4].
Part II - Severity and distribution of viral pandemics
2. Viral respiratory infections
2.1. Infections until September 2020 in Germany
Viral respiratory infections occur more frequently each year during the cold season. The most common viruses include rhinoviruses, coronaviruses, influenza and parainfluenza viruses, RS viruses, adenoviruses and human metapneumoviruses. Their incidence varies from year to year. Since 2008, patients with acute respiratory infections have been tested for the different viruses in selected doctors' practices in Germany in order to determine which viruses or virus variants lead to particularly frequent visits to the doctor. This makes it possible to roughly estimate the actual burden of disease in the outpatient sector per virus for a season.
The figures for a season are published by the Influenza Working Group at the Robert Koch Institute as so-called ARE reports (ARE: acute respiratory diseases). Since 2016, the number of positive samples analysed has been reported. For this population, it is therefore possible to determine which respiratory viruses were particularly common in a winter season. Figure 2 shows the relative frequency of viruses analysed per season, but excluding seasonal coronaviruses. From 2015 to the end of the 2019/2020 season, about half of the viral respiratory infections were caused by influenza viruses, the other half by other viruses such as rhinoviruses (22.1% to 36.1%), RS viruses (7.1% to 16.4%), human metapneumoviruses (2.7% to 10.6%) and adenoviruses (6.0% to 8.3%).
2.2. Pandemic years 2020 to 2023
At the beginning of the COVID-19 pandemic, the virus panel was expanded to include SARS-CoV-2, seasonal coronaviruses and parainfluenza viruses. Respiratory viruses such as rhinoviruses (24.1% to 55.8%), parainfluenza viruses (7.7% to 16.9%), RS viruses (5.2% to 15.7%) or human metapneumoviruses (0.4% to 10.4%) were still frequently detected in doctors' practices during the three winters up to spring 2023. Influenza viruses were much rarer in the first two years, ranging from 0.3% to 7.5%, and almost returned to pre-pandemic levels in the 2022/2023 season, with a total of 36.0%. SARS-CoV-2 was the most frequently detected virus only in the 2021/2022 season with 22.1%. In the 2020/2021 season, seasonal coronaviruses (17.2%) and parainfluenza viruses (16.9%) were more frequent than SARS-CoV-2 (11.0%). In the 2022/2023 season, influenza A viruses (27.8%) and RS viruses (12.8%) were more common than SARS-CoV-2 (11.4%). Thus, in the selected medical practices, there was no outstanding dominance of SARS-CoV-2 in patients with acute respiratory infections in any pandemic year (Figure 2). The other respiratory viruses remained clinically relevant, although their frequency varied considerably.
Figure 2: Relative frequency of respiratory viruses in Germany detected in positive specimens from patients with acute respiratory infections in selected medical practices; sources: ARE weekly reports of week 39 of a year with data from week 40 of the previous year.
2.3. History of severe influenza epidemics
Severe influenza epidemics have occurred repeatedly in recent decades. They have sometimes led to local bottlenecks in hospital care for a short time, with individual patients having to be transferred to neighbouring hospitals. But never before has a severe flu epidemic led to lockdowns, to nursing home residents being denied visits from relatives for months or even being left to die alone, to FFP2 respirator mandates on buses, to pregnant women being isolated from their newborn babies after giving birth, to night-time curfews, to schools and universities being closed, to an entire block of flats being sealed off with a construction fence for seven days, as in Göttingen, or to the state stipulating how many people from how many households may meet at private gatherings. Above all, dealing with the suffering and dying was a taboo in 2020. The Israeli sociologist Eva Illouz said [5]:
"We are experiencing an anthropological break in the way we deal with suffering, dying and death. I am concerned about how easily we accept the injunction to leave the suffering and the dying alone. Suddenly we have deprived them of the comfort, companionship and support of their neighbours. To the point where the dead are buried in isolation. [...] This is a turning point, as is the fact that we simply accept that this is happening. It seems to me that our societies are experiencing a lasting trauma.“
These unparalleled state encroachments on fundamental human rights are historically unprecedented. So here are a few more examples of severe flu epidemics to underline that they have happened again and again.
2.3.1. Italy in 2000
In the winter of 1999/2000, Italy was hit by a severe flu epidemic. Hospitals in Milan, Florence and Venice were overcrowded. In many hospitals, patients lay on cots in the corridors. In Milan it was impossible to call an ambulance for hours because patients who could not be admitted to hospital blocked the calls. The number of sick people in Italy at the time was estimated at 8 million [6].
2.3.2. Germany in 2015
In the winter of 2015, the flu epidemic placed a heavy burden on hospitals in Germany. The emergency departments in Bavaria were completely full at the time, said Eduard Fuchshuber, spokesman for the state hospital association. "I have never experienced anything as extreme as this year." In Offenbach, admissions to the internal medicine department were not possible for a while due to overcrowding [7].
2.3.3. Germany in 2017
In February 2017, some emergency departments in Germany were temporarily closed due to overcrowding during the severe flu epidemic. The bottleneck was exacerbated by the fact that some nursing staff were absent due to the flu. The spokesperson for the integrated rescue control centre in Nuremberg said: "It's extremely difficult at the moment. We are not aware of any comparable situation on this scale from previous years" [8].
2.3.4. Mallorca in 2019
In the winter of 2019, Mallorca experienced a severe flu epidemic, which meant that the emergency department of a hospital in Palma de Mallorca was completely overloaded. At one point, 114 people were waiting for treatment at the same time, and in some cases, patients had to wait up to 24 hours for a hospital bed. A flu epidemic regularly pushes hospitals in Mallorca to their limits [9].
2.4. Severe respiratory infections in Germany
The Robert Koch Institute provides data on the frequency of patients treated in hospitals for severe respiratory infections. This includes all types of respiratory infections, whether caused by bacteria, viruses or other pathogens. The frequency of these infections is given per 100,000 population and is calculated on a weekly basis. It can be seen that significantly fewer patients with severe respiratory infections were hospitalised in the summer months of each year.
With an incidence value of 40, the 2017/18 season shows a relative peak of the severe flu epidemic this winter. A similarly strong wave of severe respiratory infections was not observed in the following years until 2022 (Figure 3). With the start of the COVID-19 pandemic, the first season of 2020/21 did not show any significant change compared to previous years. The 2021/22 season also showed no significant change in hospitalisation incidence. However, in the subsequent 2022/23 season, the rate of severe respiratory infections treated in hospital was almost as high as in the severe 2017/18 influenza season. Although the relatively mild Omicron variant of SARS-CoV-2 dominated the 2022/23 season and a large proportion of the population had received at least basic immunisation against COVID-19, the incidence of severe symptomatic respiratory infections was still comparable to the severe influenza wave in 2017/18. A significantly increased incidence of severe symptomatic respiratory infections compared to previous years was not observed at the beginning of the COVID-19 pandemic.
2016 2017 2018 2019 2020 2021 2022 2023
Figure 3: Incidence of severe symptomatic respiratory infections per 100,000 population hospitalised for treatment [10]; *beginning of.
2.5. Phases in the spread of a respiratory virus
Using the spread of influenza viruses as an example, WHO has defined phases that can be used to estimate the extent of virus spread [11]. These are shown in Table 2. It is important to note that this 2009 classification does not take into account the severity of the infection, only its spread [12]. Peter Doshi, associate editor of the British Medical Journal, therefore made the following request to the WHO in 2011 [12]: “The 'pandemic' label must of necessity carry a notion of severity, for otherwise the rationale behind the original policy of having 'pandemic plans' distinct from ongoing public health programmes would be called into question”.
Phase
Characteristics
1 - 3
Predominantly animal infections, few human infections (sporadic cases and small clusters)
4
Continuous local human-to-human transmission
5
Continuous human-to-human transmission in at least two countries in one WHO region
6
Continuous human-to-human transmission in countries in at least two WHO regions
Table 2: Characteristics of the phases described by the WHO for classifying the spread of viral respiratory infections using the example of influenza viruses [11].
Others have identified a shift in mortality towards younger age groups as a key feature of a pandemic [13]. However, this important feature was not taken into account by officials when managing the pandemic on the basis of COVID-19 case numbers. The supposed severity of the infection or pandemic was then described in unusual terms in almost all media channels in Germany from March 2020. Chapter 3 gives some examples of this.
3. Public statements on COVID-19
3.1. TV speech of 18 March 2020
On 18 March 2020, Angela Merkel spoke about the COVID-19 pandemic in a TV speech. She appealed to the common sense and discipline of citizens to deal with what she saw as a serious pandemic situation. Here are two quotes from the speech.
"The coronavirus is dramatically changing life in our country. Our idea of normality, of public life, of social interaction - all this is being put to the test as never before".
"This is serious. Take it seriously. Not since German reunification, no, not since the Second World War, has there been a challenge to our country that depends so much on us acting together in solidarity".
(18 March 2020; TV speech)
Even before the first lockdown, the German Chancellor was very concerned about the many severe cases of COVID-19. She said [14]: "But our hospitals would also be completely overstretched if too many patients with a severe course of corona infection were to be admitted in the shortest possible time". According to the Robert Koch Institute, there had been a total of 8,198 confirmed cases in Germany at that time, although the proportion of severe cases was not described. How did this extraordinary concern arise?
A group of eight scientists submitted a scenario paper to the Federal Ministry of the Interior on 20 March 2020. Five of these scientists came from the field of economics, another from sociology, another from philosophy and another from the press office of a British university [15]. This means that no representative of infectious disease epidemiology was involved in this assessment. Among other things, this paper presented a worst-case scenario. According to this scenario, a total of 57.4 million people in Germany would be infected with SARS-CoV-2 by 23 May 2020 if no measures were taken, corresponding to 69.3% of the population. During the same period, approximately 1.2 million people would die from COVID-19, representing 1.4% of the population. The impact for patients has been illustrated by calculating the capacity for hospital treatment (Table 3).
This means that the intensive care units would be at full capacity on 9 April 2020 and would have to turn away patients for more than six weeks, meaning that 84% of the COVID-19 patients requiring ICU care would have to be turned away. The situation was not much better for patients requiring mechanical ventilation. The ventilation beds would be fully occupied on 14 April 2020 and would then not be available for more than five weeks, meaning that a total of 74% of COVID-19 patients requiring ventilation would have to be turned away.
Indicator
Beginning of rationing
Duration of rationing
Proportion of rejected patients
Hospital
9 May 2020
9 days
5%
Intensive care unit
9 April 2020
45 days
84%
Ventilation
14 April 2020
39 days
74%
Table 3: Extrapolation of case numbers (worst case scenario without any measures) in the first wave of COVID-19 in Germany; extract from the originally confidential scenario paper for the Federal Ministry of the Interior [16].
But things turned out differently. By 23 May 2020, the Robert Koch Institute had registered a total of 177,850 COVID-19 cases. This corresponds to about 0.3% of the total number of cases described in the worst-case scenario for this period and about 0.21% of the population. During the same period, 8216 COVID-19 deaths were recorded. This represents about 0.7% of the total number of deaths described in the worst-case scenario for this period and about 0.01% of the population. One might be tempted to judge the actual case numbers described above as a success of the measures in spring 2020 compared to the worst-case scenario. However, this view does not stand up to a comparison with countries without restrictive measures, such as Sweden. By May 2020, there were 36,417 confirmed cases (0.35% of the population) and 4537 COVID-19 deaths (0.04% of the population). Although these rates were significantly higher than in Germany, they were still far from the magnitude of the worst-case scenario simulated for Germany.
In 2021, there were still fears that the health system could be overburdened. On 17 August 2021, the Scientific Service of the German Bundestag assessed the access restrictions for the unvaccinated as an infection control measure with the legitimate purpose of preventing overburdening of the healthcare system [17]. This primarily referred to the capacity utilisation of hospitals. A similar aim was formulated in the context of the COVID-19 vaccination mandate, namely to avoid overburdening the health care system in order to continue to guarantee the treatment of seriously ill patients [18].
Conclusion
It is quite conceivable that the German government was already aware of this worst-case scenario in mid-March 2020 and used it as a basis for political decisions to avoid overburdening the healthcare system.
3.2. Other public statements
In public, the situation regarding the COVID-19 pandemic has mostly been portrayed as serious and threatening. Here are some examples.
Karl Lauterbach, SPD
"Covid-19 is an insidious, disgusting disease that affects the lungs, kidneys, vascular system and heart, and in severe cases will probably also leave behind cognitive impairments due to prolonged artificial respiration, and later even dementia. If the number of infected people in Germany were to rise sharply again, our system would also be overstretched".
(25 April 2020; taz)
Tedros Adhanom Ghebreyesus, WHO
"We are deeply concerned about both the alarming spread and severity of the disease, and the alarming level of inaction."
(11 March 2020; Tagesschau)
Christian Drosten, Charité
"The peak of infections will be in African countries this summer. I can't even imagine the pictures we will see. We will see people dying in the streets in Africa. The situation will be bad, very bad."
(22 March 2020; Stern)
Angela Merkel, CDU
"We are in a dramatic situation at the beginning of the cold season."
(29 October 2020; ZDF)
"The coronavirus pandemic is a historic crisis."
(31 December 2020; Zeit online)
Conclusion
A viral respiratory infection and its spread, which is repeatedly described as "dramatic", "very bad" and "historically unique", only gives the impression that there must not only have been a large number of infected people, but also a large number of seriously ill people and deaths, which must have occurred much more frequently than with other viral respiratory infections.
3.3. Implications of these statements
Forensic pathologist Michael Tsokos of the Charité university hospital in Berlin called for more level-headed reporting as early as May 2020. He said [19]:
"Exaggerated scaremongering, including by virologists and politicians, can make people who are mentally unstable or suffering from depression feel that the world is heading for the abyss. Communication about the pandemic needs to be more balanced and reassuring. Otherwise, we will see excess mortality at the end of the year, not from COVID diseases, but from suicide and alcohol-related deaths."
In October 2020, he reported that there are many people who are suffering from the crisis, even though they are not infected with the virus themselves. "In the last week alone, we carried out several autopsies on people who had not left their homes since the lockdown," he said, referring to the lockdown in March and April 2020. The people had been in their homes for a long time, some of them in untidy homes, "with gas masks and prepared astronaut food that nobody had missed", the forensic pathologist explained. Many people had also not gone to hospital because "they were afraid to go out because of all the threatening scenarios that had been put forward" [20].
Conclusion
This raises the important question of whether COVID-19 was really so much more dangerous than other viral respiratory infections that occur every year during the winter months and have repeatedly led to temporary hospital overcrowding.
4. Severity and distribution
4.1. Background
To assess whether COVID-19 can indeed be classified as "dramatic" and "historically unique", it is useful to compare COVID-19 with other viral respiratory infections with pandemic potential. To this end, Peter Doshi suggested in 2009 that distribution and severity should be used as key parameters (Figure 4).
Figure 4: Assessment of global viral respiratory infections according to their contagiousness (distribution) and severity of infection; adapted from [21].
Infections with a rather mild course are classified as less dangerous to health, such as the influenza A virus H1N2 since 1988 with a rather low spread, or the pandemics in 1957 and 1968 with high infection rates. The situation is different for viral respiratory infections, which are more likely to be severe. An example of this is the H5N1 bird flu since 1997, which had a comparatively low spread. This is in contrast to the Spanish flu, which led to many transmissions and severe cases. In the following, I will try to classify COVID-19 according to this pattern.
