[Video] COVID Vaccine Failure in CDC Barnstable, Massachusetts Data

COVID Vaccine Failure in CDC Barnstable, Massachusetts Data Video on Odysee

Alternative Video: NewTube ARCHIVE

The July 30, 2021 CDC report on an outbreak of COVID-19 (delta variant) in Barnstable County, Massachusetts (July 6-25, 2021) used to justify guidance for vaccinated persons to wear masks indoors appears to indicate the COVID vaccines failed to reduce either the rate of infection with COVID in the vaccinated or the hospitalization rate if infected, contrary to widespread statements in the mass media by public health authorities and others. Although there are some possible if improbable explanations, no explanation for this seeming contradiction appears to be present in the report.

Outbreak of SARS-CoV-2 Infections, Including COVID-19 Vaccine Breakthrough Infections, Associated with Large Public Gatherings — Barnstable County, Massachusetts, July 2021
Brown et al
Morbidity and Mortality Weekly Report
Early Release / Vol. 70 July 30, 2021
U.S. Department of Health and Human Services
Centers for Disease Control and Prevention
https://www.cdc.gov/mmwr/volumes/70/wr/pdfs/mm7031e2-H.pdf

Relevant Numbers from Report:

cases of COVID-19 associated with multiple summer events and large public gatherings in a town in Barnstable County, Massachusetts (July 6-25, 2021) 469
vaccination coverage among eligible Massachusetts residents 69.00%
cases in fully vaccinated 346 73.77%
cases in unvaccinated, which may include partially vaccinated and recently vaccinated (less than 14 days since final shot) 123 26.23%
number of vaccinated hospitalized 4
number of unvaccinated hospitalized 1
hospitalization rate of vaccinated infected with COVID-19 1.16%
hospitalization rate of unvaccinated infected with COVID-19 0.81%
Number of cases sequenced 133
Number of cases with B.1.617.2 (Delta) variant 120 90.23%
Number of symptomatic vaccinated cases 274
Number of symptomatic unvaccinated cases ?

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#

(C) 2021 by John F. McGowan, Ph.D.

About Me

John F. McGowan, Ph.D. solves problems using mathematics and mathematical software, including developing gesture recognition for touch devices, video compression and speech recognition technologies. He has extensive experience developing software in C, C++, MATLAB, Python, Visual Basic and many other programming languages. He has been a Visiting Scholar at HP Labs developing computer vision algorithms and software for mobile devices. He has worked as a contractor at NASA Ames Research Center involved in the research and development of image and video processing algorithms and technology. He has published articles on the origin and evolution of life, the exploration of Mars (anticipating the discovery of methane on Mars), and cheap access to space. He has a Ph.D. in physics from the University of Illinois at Urbana-Champaign and a B.S. in physics from the California Institute of Technology (Caltech).

[Article] Improving CDC Data Practices: Recommendations for Improving the United States Centers for Disease Control (CDC) Data Practices for Pneumonia, Influenza, and COVID-19

This is a preprint of a new academic paper written by Tam Hunt, Josh Mitteldorf, Ph.D. and myself on the US Centers for Disease Control (CDC)’s data practices during the COVID-19 pandemic and for pneumonia and influenza prior to the pandemic. I am the corresponding author.

Abstract

During the pandemic, millions of Americans have become acquainted with the CDC because its reports and the data it collects affect their day-today lives. But the methodology used and even some of the data collected by CDC remain opaque to the public and to the community of academic epidemiology. In this paper, we highlight areas in which CDC methodology might be improved and where greater transparency might lead to broad collaboration. (1) “Excess” deaths are routinely reported, but not “years of life lost”, an easily-computed datum that is important for public policy. (2) What counts as an “excess death”? The method for computing the number of excess deaths does not include error bars and we show a substantial range of estimates is possible. (3) Pneumonia and influenza death data on different CDC pages is grossly contradictory. (4) The methodology for computing influenza deaths is not described in sufficient detail that an outside analyst might pursue the source of the discrepancy. (5) Guidelines for filling out death certificates have changed during the COVID-19 pandemic, preventing the comparison of 2020-21 death profiles with any previous year. We conclude with a series of explicit recommendations for greater consistency and transparency, and ultimately to make CDC data more useful to outside epidemiologists.

John F. McGowan, Ph.D., Tam Hunt, Josh Mitteldorf. Improving CDC Data Practices Recommendations for Improving the United States Centers for Disease Control (CDC) Data Practices for Pneumonia, Influenza, and COVID-19. Authorea. July 19, 2021.
DOI: 10.22541/au.162671168.86830026/v1

Here are the key recommendations from the paper:

Recommendations

In light of the previous discussion, we make a number of recommendations to improve CDC’s data practices, including improved observance of common scientific and engineering practice – such as use of significant figures and reporting of statistical and systematic errors. Common scientific and engineering practice is designed to prevent serious errors and should be followed rigorously in a crisis such as the COVID-19 pandemic.

Note that some of these recommendations may require changes in federal or state laws, federal or state regulations, or renegotiation of contracts between the federal government and states. This is probably the case for making the Deaths Master File (DMF), with names and dates of death of persons reported as deceased to the states and federal government, freely available to the public and other government agencies.

  • All CDC numbers, where possible, should be clearly identified as estimates, adjusted counts, or raw counts, with statistical errors and systematic errors given, using consistent clear standard language in all documents. The errors should be provided as both ninety-five percent (95%) confidence level intervals and the standard deviation – at least for the statistical errors.
  • In the case of adjusted counts, the raw count should be explicitly listed immediately following the adjusted count as well as a brief description of the adjustment and a reference for the adjustment methodology. For example, if the adjusted number of deaths in the United States in 2020 is 3.4 million but the raw count of deaths was 3.3 million with 100,000 deaths added to adjust for unreported deaths of undocumented immigrants, the web pages and reports would say:

Total deaths (2020): 3.4 million (adjusted, raw count 3.3 million, unreported deaths of undocumented immigrants, adjustment methodology citation: Smith et al, MMWR Volume X, Number Y)

  • The distinction between the leading causes of death report “pneumonia and influenza” deaths, ~55,000 per year pre-pandemic, and the FluView website “pneumonia and influenza” deaths, ~188,000 per year pre-pandemic, should be clarified in the labels and legends for the graphics and prominently in the table of leading causes of death or immediately adjacent text. Statistical and systematic errors on these numbers should be provided in graphs and tables.
  • In general, where grossly different raw counts, adjusted counts, or estimates are presented in CDC documents and websites with the same name, semantically equivalent or nearly equivalent names such as “pneumonia and influenza” and “influenza and pneumonia,” clearly distinct names should be used instead, or the reasons for the gross difference in the values should be prominently listed in the graphs and tables or immediately adjacent text. It should be easy for the public, busy health professionals, policy makers and others to recognize and understand the differences.
  • CDC should provide results for different models for the same data with similar R2 values – coefficient of determination – to give the audience a quick sense of the systematic modeling errors – since there is no generally accepted methodology for estimating the 95% confidence level for the systematic modeling errors. See Figure 7 above for an example.
  • All mathematical models should be free and open source with associated data provided using commonly used free open-source scientific programming languages such as Python or R, made available on the CDC website, GitHub, and other popular sources. The models and data should be provided in a package form such that anyone with access to a standard MS Windows, Mac OS X, or Linux/Unix computer can easily download and run the analysis – similar to the package structure used by the GNU project, for example.
  • Specifically, the influenza virus deaths model should be provided to the public as code and data. The justification for the increase in the number of deaths attributed to influenza (~6,000 to ~55,000) should be presented in clear language with supporting numbers, such as the false positive and negative rates for the laboratory influenza deaths and general diagnosis of influenza in the absence of a positive lab test as well as in the code and data.
  • With respect to excess deaths tracking, include all major select causes of death, rather than just the thirteen (13) in the cause-specific excess deaths that CDC tracks, which currently account for about 2/3 of all deaths.
  • Include a Years of Lives Lost (YLL) display for COVID-19 deathsi and non-COVID-19 deaths, as well as excess deaths analysis, due to the higher granularity of YLL analysis when compared to excess deaths analysis. Explain the pros and cons of both analytical tools. Do the same for any future pandemics or health crises.
  • Adopt or develop a different algorithm or algorithms for tracking excess deaths which are mostly attributed to non-infectious causes such as heart attacks, cancer, and strokes. The Farrington/Noufaily algorithms were specifically developed as an early warning for often non-lethal infectious disease outbreaks such as salmonella. A medically-based model or models that incorporates population demographics such as the aging “baby boom” and evolving death rates broken down by age, sex, and possibly other factors where known is probably a better practice rather than simple empirical trend models such as the Noufaily algorithm.
  • Eliminate the zeroing procedure in calculating excess deaths, in which negative excess deaths in some categories are set to zero, rather than being added to the full excess deaths sum over all categories.
  • The anonymized data with causes of death as close to the actual data as possible, e.g. the actual death certificates, should be available on the CDC website in a simple accessible widely used format such as CSV (comma separated values) files. The code used to aggregate the data into summary data such as the FluView website data files should also be public.
  • The full Deaths Master File (DMF) including the actual names of the deceased persons and dates of death should be made available to the general public, independent researchers, and others. This is critical to independent verification of many numbers from the CDC, SSA, and US Census.
  • COVID-19-related deaths figures should be tracked based on year-specific age of death, rather than 10-year age ranges, as is currently the case.
  • CDC frequently changes the structure and layout of the CSV files/spreadsheets on their websites. The CDC should either (1) not do this or (2) provide easy conversion between different file formats with each new format so it is trivial for third parties to quickly adapt to the changes without writing additional code. CDC should provide a program or program in a free and open source language like R to convert between the formats.
  • The CDC and other agencies should be required to announce and solicit public comment for changes to case definitions, data collection rules, etc. for key public policy data such as the COVID-19 case definitions, death certification guidelines, and coding rules. Other government agencies have significantly more public participation than CDC, which is appropriate in a modern democracy.
  • Any practices and policies imposed in a public emergency, such as case definitions, definitions of measured quantities, data reporting practices, etc. imposed without public comment and review, should have an expiration date (e.g. sixty days) beyond which they must be subject to public review. Public comment, reviews, and cost/benefit analyses should happen during this emergency period.

Enacting these reforms should reduce the risk of serious errors, increase the quality and accuracy of CDC data and analyses, as well as any policies or CDC guidelines based on the data and analysis, and strengthen public confidence in the CDC and public health policies.

(C) 2021 by John F. McGowan, Ph.D.

About Me

John F. McGowan, Ph.D. solves problems using mathematics and mathematical software, including developing gesture recognition for touch devices, video compression and speech recognition technologies. He has extensive experience developing software in C, C++, MATLAB, Python, Visual Basic and many other programming languages. He has been a Visiting Scholar at HP Labs developing computer vision algorithms and software for mobile devices. He has worked as a contractor at NASA Ames Research Center involved in the research and development of image and video processing algorithms and technology. He has published articles on the origin and evolution of life, the exploration of Mars (anticipating the discovery of methane on Mars), and cheap access to space. He has a Ph.D. in physics from the University of Illinois at Urbana-Champaign and a B.S. in physics from the California Institute of Technology (Caltech).

[Video] How to Sterilize Groceries with a UV Light and Your Refrigerator

Click on title slide below to see video (on free speech friendly Odysee video platform).

https://odysee.com/@MathematicalSoftware:5/how_to_sterilize_groceries_with_a_uv_light_and_your_refrigerator:a

Alternative Video: NewTube Archive BitChute

Censored Video: YouTube

How to Sterilize Groceries with a UV Light and Your Refrigerator

Video shows how to sterilize groceries (or other items) with a UV light and your refrigerator.

NOTE: UV light can be dangerous. UV light can damage your eyes and skin. Prolonged exposure may cause skin cancer. Wear safety goggles, protective clothing, and minimize any exposure to UV light. Hopefully in future videos I will show how to turn on the UV light only when the refrigerator is completely closed. Wear gloves as shown in the video to protect from viruses and bacteria when handling the groceries or other items to be sterilized and to protect hands from the UV light.

A UV light with an ON/OFF switch on the power cord outside the refrigerator can be turned ON/OFF inside the refrigerator without exposure to the UV light. An RF (radio) remote control may be able to turn a UV light ON/OFF inside the closed refrigerator, unlike a standard IR (infrared) remote control.

Items mentioned:

ROHS UV “Corn Lamp”: https://www.amazon.com/Sanitizer-Disinfection-Germicidal-Restaurant-Supermarket/dp/B08LG9TX48/ref=sr_1_20?crid=1IEXUQM2MVD8O&dchild=1&keywords=uv+light+sanitizer&qid=1625589690&sprefix=uv+light+san%2Caps%2C265&sr=8-20

UV Safety Goggles: https://www.amazon.com/Tool-Klean-Safety-Glasses-Protection/dp/B081BHTJT8/ref=sr_1_2?dchild=1&keywords=uv+safety+goggles&qid=1625589772&sr=8-2

We don’t receive any sponsorship or consideration from the makers of the items demonstrated. There are many UV lights and many brands of UV protective eyewear and clothing. Do your own research and choose the best items for you.

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(C) 2021 by John F. McGowan, Ph.D.

About Me

John F. McGowan, Ph.D. solves problems using mathematics and mathematical software, including developing gesture recognition for touch devices, video compression and speech recognition technologies. He has extensive experience developing software in C, C++, MATLAB, Python, Visual Basic and many other programming languages. He has been a Visiting Scholar at HP Labs developing computer vision algorithms and software for mobile devices. He has worked as a contractor at NASA Ames Research Center involved in the research and development of image and video processing algorithms and technology. He has published articles on the origin and evolution of life, the exploration of Mars (anticipating the discovery of methane on Mars), and cheap access to space. He has a Ph.D. in physics from the University of Illinois at Urbana-Champaign and a B.S. in physics from the California Institute of Technology (Caltech).

[Video] Anti-COVID “Space” Helmet Review

Alternative Video: BitChute, Odysee, ARCHIVE

Short review of the Microclimate AIR anti-COVID “Space” Helmet

We do not receive any compensation for this review or use of the product reviewed. We recommend that you talk to your doctor or other health professional regarding use of this product, especially if you have any respiratory or other health problems.

http://www.microclimate.com/

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(C) 2021 by John F. McGowan, Ph.D.

About Me

John F. McGowan, Ph.D. solves problems using mathematics and mathematical software, including developing gesture recognition for touch devices, video compression and speech recognition technologies. He has extensive experience developing software in C, C++, MATLAB, Python, Visual Basic and many other programming languages. He has been a Visiting Scholar at HP Labs developing computer vision algorithms and software for mobile devices. He has worked as a contractor at NASA Ames Research Center involved in the research and development of image and video processing algorithms and technology. He has published articles on the origin and evolution of life, the exploration of Mars (anticipating the discovery of methane on Mars), and cheap access to space. He has a Ph.D. in physics from the University of Illinois at Urbana-Champaign and a B.S. in physics from the California Institute of Technology (Caltech).

[Video] Where did COVID-19 Come From?

Where Did COVID-19 Come From?

Alternative Video Links: BitChute NewTube Archive

Veteran science reporter Nicholas Wade has written a detailed accessible article on the origins of the COVID-19 pandemic and the evidence for and against a laboratory leak at the Wuhan Institute of Virology (WIV), arguing that the lab leak theory is more likely based on the available evidence.

https://thebulletin.org/2021/05/the-origin-of-covid-did-people-or-nature-open-pandoras-box-at-wuhan/

https://nicholaswade.medium.com/origin-of-covid-following-the-clues-6f03564c038

Very little if any of this article is new but Wade presents the information in a clear way with a minimum of technical jargon. He properly acknowledges several prior detailed analyses and reports on the issues.

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(C) 2021 by John F. McGowan, Ph.D.

About Me

John F. McGowan, Ph.D. solves problems using mathematics and mathematical software, including developing gesture recognition for touch devices, video compression and speech recognition technologies. He has extensive experience developing software in C, C++, MATLAB, Python, Visual Basic and many other programming languages. He has been a Visiting Scholar at HP Labs developing computer vision algorithms and software for mobile devices. He has worked as a contractor at NASA Ames Research Center involved in the research and development of image and video processing algorithms and technology. He has published articles on the origin and evolution of life, the exploration of Mars (anticipating the discovery of methane on Mars), and cheap access to space. He has a Ph.D. in physics from the University of Illinois at Urbana-Champaign and a B.S. in physics from the California Institute of Technology (Caltech).

[Link] Over 200 Scientists & Doctors Call For Increased Vitamin D Use To Combat COVID-19

https://vitamind4all.org/letter.html

(C) 2021 by John F. McGowan, Ph.D.

About Me

John F. McGowan, Ph.D. solves problems using mathematics and mathematical software, including developing gesture recognition for touch devices, video compression and speech recognition technologies. He has extensive experience developing software in C, C++, MATLAB, Python, Visual Basic and many other programming languages. He has been a Visiting Scholar at HP Labs developing computer vision algorithms and software for mobile devices. He has worked as a contractor at NASA Ames Research Center involved in the research and development of image and video processing algorithms and technology. He has published articles on the origin and evolution of life, the exploration of Mars (anticipating the discovery of methane on Mars), and cheap access to space. He has a Ph.D. in physics from the University of Illinois at Urbana-Champaign and a B.S. in physics from the California Institute of Technology (Caltech).

Veteran Science Reporter Nicholas Wade Favors Lab Leak Theory of COVID

Veteran science reporter Nicholas Wade has written a detailed accessible article on the origins of the COVID-19 pandemic and the evidence for and against a laboratory leak at the Wuhan Institute of Virology (WIV), arguing that the lab leak theory is more likely based on the available evidence.

https://nicholaswade.medium.com/origin-of-covid-following-the-clues-6f03564c038

Very little if any of this is new but Wade presents the information in a clear way with a minimum of technical jargon. He properly acknowledges several prior detailed analyses and reports on the issues.

(C) 2021 by John F. McGowan, Ph.D.

About Me

John F. McGowan, Ph.D. solves problems using mathematics and mathematical software, including developing gesture recognition for touch devices, video compression and speech recognition technologies. He has extensive experience developing software in C, C++, MATLAB, Python, Visual Basic and many other programming languages. He has been a Visiting Scholar at HP Labs developing computer vision algorithms and software for mobile devices. He has worked as a contractor at NASA Ames Research Center involved in the research and development of image and video processing algorithms and technology. He has published articles on the origin and evolution of life, the exploration of Mars (anticipating the discovery of methane on Mars), and cheap access to space. He has a Ph.D. in physics from the University of Illinois at Urbana-Champaign and a B.S. in physics from the California Institute of Technology (Caltech).

References on Potentially Fatal Antibody Dependent Enhancement (ADE) from COVID-19 Vaccines

Introduction

Antibody Dependent Enhancement (ADE) is a phenomenon that has been observed with a number of vaccines in which the vaccine initially produces no or negligible adverse effects but when the vaccinated person or animal actually encounters the virus or bacteria the vaccine is intended to stop, the immune system overreacts to the infection and harms or kills the vaccinated person. Prior to the COVID-19 pandemic, previous attempts to develop vaccines to prevent coronavirus infections such as SARS (2003, not SARS-COV-2) and MERS failed due to ADE.

How likely is potentially fatal antibody dependent enhancement (ADE) with the COVID-19 vaccines? Simple answer: we don’t know. We are conducting a gigantic uncontrolled experiment on a large fraction of the human race.

The Moderna clinical trials of their RNA COVID-19 vaccine, linked and discussed at the end of this article, suggest that ADE reactions in generally healthy persons similar to the test subjects in their trial are probably (95% confidence level) less than one in sixty-six (66) persons (1.5 percent) using the rule of three in statistics. This is based on a tiny sample of only 196 persons in the trial who actually developed COVID-19 — were actually exposed to the virus. ADE does not appear to have been reported in any of these 196 cases.

UPDATE (April 27, 2021): It is a bit unclear what number — 196, 11, or 185 — to use in the rule of three. The Moderna trial reported only eleven (11) vaccinated subjects with COVID-19 and 185 unvaccinated subjects with COVID-19. Thus, one could use the eleven in which case the rule of three would imply that the risk of ADE is probably less than one in 3.6. However, about 185 vaccinated subjects were exposed to COVID-19 in the vaccinated group as in the un-vaccinated control group. The vaccine worked for all but 11 of these. This would make the correct number 185 instead of 196 and give an estimate that the risk of ADE is probably (95% confidence level) less than one in sixty-one (1:61) or 1.6 percent. Most likely the correct number is about 185 (not the naive 196); the estimated bound on the risk is just about the same for 185 and 196.

References on Historical Failures of Coronavirus Vaccines due to ADE

From petition to European Medicines Agency (EMA) by Michael Yeadon and Wolfgang (https://dryburgh.com/wp-content/uploads/2020/12/Wodarg_Yeadon_EMA_Petition_Pfizer_Trial_FINAL_01DEC2020_signed_with_Exhibits_geschwarzt.pdf)

VIII.For a vaccine to work, our immune system needs to be stimulated to produce a neutralizing antibody, as opposed to a non-neutralizing antibody. A neutralizing antibody is one that can recognize and bind to some region (‘epitope’) of the virus, and that subsequently results in the virus either not entering or replicating in your cells. A non-neutralizing antibody is one that can bind to the virus, but for some reason, the antibody fails to neutralize the infectivity of the virus. In some viruses, if a person harbors a non-neutralizing antibody to the virus, a subsequent infection by the virus can cause that person to elicit a more severe reaction to the virus due to the presence of the non-neutralizing antibody. This is not true for all viruses, only particular ones. This is called Antibody Dependent Enhancement (ADE), and is a common problem with Dengue Virus, Ebola Virus, HIV, RSV, and the family of coronaviruses. In fact, this problem of ADE is a major reason why many previous vaccine trials for other coronaviruses failed. Major safety concerns were observed in animal models. If ADE occurs in an individual, their response to the virus can be worse than their response if they had never developed an antibody in the first place. This can cause a hyperinflammatory response, a cytokine storm, and a generally dysregulation of the immune system that allows the virus to cause more damage to our lungs and other organs of our body. In addition, new cell types throughout our body are now susceptible to viral infection due to the additional viral entry pathway. There are many studies that demonstrate that ADE is a persistent problem with coronaviruses in general, and in particular, with SARS-related viruses. ADE has proven to be a serious challenge with coronavirus vaccines, and this is the primary reason many of such vaccines have failed in early in-vitro or animal trials. For example, rhesus macaques who were vaccinated with the Spike protein of the SARS-CoV virus demonstrated severe acute lung injury when challenged with SARS-CoV, while monkeys who were not vaccinated did not. Similarly, mice who were immunized with one of four different SARS-CoV vaccines showed histopathological changes in the lungs with eosinophil infiltration after being challenged with… (EMPHASIS ADDED)

Specific references for failures of coronavirus vaccines for SARS (2003, not SARS-COV-2) and MERS are given after the following section on ADE and COVID-19/SARS-COV-2

References on ADE and COVID-19/SARS-COV-2

https://pubmed.ncbi.nlm.nih.gov/32908214/

Lee WS, Wheatley AK, Kent SJ, DeKosky BJ. Antibody-dependent enhancement and SARS-CoV-2 vaccines and therapies. Nat Microbiol. 2020 Oct;5(10):1185-1191. doi: 10.1038/s41564-020-00789-5. Epub 2020 Sep 9. PMID: 32908214.

https://pubmed.ncbi.nlm.nih.gov/32659783/

Arvin AM, Fink K, Schmid MA, Cathcart A, Spreafico R, Havenar-Daughton C, Lanzavecchia A, Corti D, Virgin HW. A perspective on potential antibody-dependent enhancement of SARS-CoV-2. Nature. 2020 Aug;584(7821):353-363. doi: 10.1038/s41586-020-2538-8. Epub 2020 Jul 13. PMID: 32659783.

Semi-popular June 22, 2020 Scientific American article by William Haseltine on ADE risk with COVID-19 vaccines:  https://www.scientificamerican.com/article/the-risks-of-rushing-a-covid-19-vaccine/

The Scientist on ADE and COVID:  https://www.the-scientist.com/news-opinion/covid-19-vaccine-researchers-mindful-of-immune-enhancement-67576

This is a March 12, 2018 article from Children’s Health Defense (Robert F. Kennedy Jr’s group) with an overview of several cases of known or suspected antibody dependent enhancement in some vaccines with scientific references.  Does not appear to discuss the coronavirus vaccine failures with ADE.

https://childrenshealthdefense.org/news/worse-than-nothing-how-ineffective-vaccines-enhance-disease/


NOTE: many articles that turn up in searches now appear to omit or not clearly state that previous attempts to develop coronavirus vaccines failed due to ADE in early trials. 

https://www.sciencedirect.com/science/article/pii/S1201971220307311

Jieqi Wen, Yifan Cheng, Rongsong Ling, Yarong Dai, Boxuan Huang, Wenjie Huang, Siyan Zhang, Yizhou Jiang,


Antibody-dependent enhancement of coronavirus,
International Journal of Infectious Diseases,
Volume 100,
2020,
Pages 483-489,
ISSN 1201-9712,
https://doi.org/10.1016/j.ijid.2020.09.015.
(https://www.sciencedirect.com/science/article/pii/S1201971220307311)


Abstract: Antibody-dependent enhancement (ADE) exists in several kinds of virus. It has a negative influence on antibody therapy for viral infection. This effect was first identified in dengue virus and has since also been described for coronavirus. To date, the rapid spread of the newly emerged coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), causing coronavirus disease 2019 (COVID-19), has affected over 3.8 million people across the globe. The novel coronavirus poses a great challenge and has caused a wave of panic. In this review, antibody-dependent enhancements in dengue virus and two kinds of coronavirus are summarized. Possible solutions for the effects are reported. We also speculate that ADE may exist in SARS-CoV-2.


Keywords: Antibody-dependent enhancement (ADE); Coronavirus disease 2019 (COVID-19); Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Severe acute respiratory syndrome coronavirus (SARS-CoV); Middle East respiratory syndrome coronavirus (MERS-CoV)

Highlights

Five mechanisms of antibody-dependent enhancement have been discussed to date, with the most frequent effect being related to FcγR.

Antibody-dependent enhancement has been discovered in both severe acute respiratory syndrome coronavirus and Middle East respiratory syndrome coronavirus, but the mechanism is not completely clear; different studies have led to different opinions.

Many scientist have mentioned the potential existence of antibody-dependent enhancement in the 2019 novel coronavirus – severe acute respiratory syndrome coronavirus.

The most recent studies on both convalescent plasma transmission and the application of inactivated vaccine did not report any case of antibody-dependent enhancement.

References on ADE in SARS and MERS

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4018502/

Yip MS, Leung NH, Cheung CY, et al. Antibody-dependent infection of human macrophages by severe acute respiratory syndrome coronavirus. Virol J. 2014;11:82. Published 2014 May 6. doi:10.1186/1743-422X-11-82 (PDF attached to this email)

Abstract

Background

Public health risks associated to infection by human coronaviruses remain considerable and vaccination is a key option for preventing the resurgence of severe acute respiratory syndrome coronavirus (SARS-CoV). We have previously reported that antibodies elicited by a SARS-CoV vaccine candidate based on recombinant, full-length SARS-CoV Spike-protein trimers, trigger infection of immune cell lines. These observations prompted us to investigate the molecular mechanisms and responses to antibody-mediated infection in human macrophages.

Methods

We have used primary human immune cells to evaluate their susceptibility to infection by SARS-CoV in the presence of anti-Spike antibodies. Fluorescence microscopy and real-time quantitative reverse transcriptase polymerase chain reaction (RT-PCR) were utilized to assess occurrence and consequences of infection. To gain insight into the underlying molecular mechanism, we performed mutational analysis with a series of truncated and chimeric constructs of fragment crystallizable γ receptors (FcγR), which bind antibody-coated pathogens.

Results

We show here that anti-Spike immune serum increased infection of human monocyte-derived macrophages by replication-competent SARS-CoV as well as Spike-pseudotyped lentiviral particles (SARS-CoVpp). Macrophages infected with SARS-CoV, however, did not support productive replication of the virus. Purified anti-viral IgGs, but not other soluble factor(s) from heat-inactivated mouse immune serum, were sufficient to enhance infection. Antibody-mediated infection was dependent on signaling-competent members of the human FcγRII family, which were shown to confer susceptibility to otherwise naïve ST486 cells, as binding of immune complexes to cell surface FcγRII was necessary but not sufficient to trigger antibody-dependent enhancement (ADE) of infection. Furthermore, only FcγRII with intact cytoplasmic signaling domains were competent to sustain ADE of SARS-CoVpp infection, thus providing additional information on the role of downstream signaling by FcγRII.

Conclusions

These results demonstrate that human macrophages can be infected by SARS-CoV as a result of IgG-mediated ADE and indicate that this infection route requires signaling pathways activated downstream of binding to FcγRII receptors.

Keywords: SARS-CoV, Spike, Antibody-dependent enhancement, Macrophage, Fcγ receptor, Antibodies, Pseudotypes

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6478436/

Liu L, Wei Q, Lin Q, et al. Anti-spike IgG causes severe acute lung injury by skewing macrophage responses during acute SARS-CoV infection. JCI Insight. 2019;4(4):e123158. Published 2019 Feb 21. doi:10.1172/jci.insight.123158  (PDF too large to attach to email, available on web site linked above).

Abstract

Newly emerging viruses, such as severe acute respiratory syndrome coronavirus (SARS-CoV), Middle Eastern respiratory syndrome CoVs (MERS-CoV), and H7N9, cause fatal acute lung injury (ALI) by driving hypercytokinemia and aggressive inflammation through mechanisms that remain elusive. In SARS-CoV/macaque models, we determined that anti–spike IgG (S-IgG), in productively infected lungs, causes severe ALI by skewing inflammation-resolving response. Alveolar macrophages underwent functional polarization in acutely infected macaques, demonstrating simultaneously both proinflammatory and wound-healing characteristics. The presence of S-IgG prior to viral clearance, however, abrogated wound-healing responses and promoted MCP1 and IL-8 production and proinflammatory monocyte/macrophage recruitment and accumulation. Critically, patients who eventually died of SARS (hereafter referred to as deceased patients) displayed similarly accumulated pulmonary proinflammatory, absence of wound-healing macrophages, and faster neutralizing antibody responses. Their sera enhanced SARS-CoV–induced MCP1 and IL-8 production by human monocyte–derived wound-healing macrophages, whereas blockade of FcγR reduced such effects. Our findings reveal a mechanism responsible for virus-mediated ALI, define a pathological consequence of viral specific antibody response, and provide a potential target for treatment of SARS-CoV or other virus-mediated lung injury.

Keywords: Infectious disease, Pulmonology

Keywords: Cytokines, Immunoglobulins, Macrophages

ADE in Ferrets with SARS (2003, not SARS-COV-2)

NOTE: Ferrets have a similar respiratory system to humans and are often used for animal studies of vaccines for respiratory illnesses for this reason.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC525089/

Weingartl H, Czub M, Czub S, et al. Immunization with modified vaccinia virus Ankara-based recombinant vaccine against severe acute respiratory syndrome is associated with enhanced hepatitis in ferrets. J Virol. 2004;78(22):12672-12676. doi:10.1128/JVI.78.22.12672-12676.2004

Abstract

Severe acute respiratory syndrome (SARS) caused by a newly identified coronavirus (SARS-CoV) is a serious emerging human infectious disease. In this report, we immunized ferrets (Mustela putorius furo) with recombinant modified vaccinia virus Ankara (rMVA) expressing the SARS-CoV spike (S) protein. Immunized ferrets developed a more rapid and vigorous neutralizing antibody response than control animals after challenge with SARS-CoV; however, they also exhibited strong inflammatory responses in liver tissue. Inflammation in control animals exposed to SARS-CoV was relatively mild. Thus, our data suggest that vaccination with rMVA expressing SARS-CoV S protein is associated with enhanced hepatitis.

ADE in Mice with SARS (2003, not SARS-COV-2)

https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.0030525

Article Source: Vaccine Efficacy in Senescent Mice Challenged with Recombinant SARS-CoV Bearing Epidemic and Zoonotic Spike Variants


Deming D, Sheahan T, Heise M, Yount B, Davis N, et al. (2006) Vaccine Efficacy in Senescent Mice Challenged with Recombinant SARS-CoV Bearing Epidemic and Zoonotic Spike Variants . PLOS Medicine 3(12): e525. https://doi.org/10.1371/journal.pmed.0030525

(PDF attached)

Abstract

Background

In 2003, severe acute respiratory syndrome coronavirus (SARS-CoV) was identified as the etiological agent of severe acute respiratory syndrome, a disease characterized by severe pneumonia that sometimes results in death. SARS-CoV is a zoonotic virus that crossed the species barrier, most likely originating from bats or from other species including civets, raccoon dogs, domestic cats, swine, and rodents. A SARS-CoV vaccine should confer long-term protection, especially in vulnerable senescent populations, against both the 2003 epidemic strains and zoonotic strains that may yet emerge from animal reservoirs. We report the comprehensive investigation of SARS vaccine efficacy in young and senescent mice following homologous and heterologous challenge.

Methods and Findings

Using Venezuelan equine encephalitis virus replicon particles (VRP) expressing the 2003 epidemic Urbani SARS-CoV strain spike (S) glycoprotein (VRP-S) or the nucleocapsid (N) protein from the same strain (VRP-N), we demonstrate that VRP-S, but not VRP-N vaccines provide complete short- and long-term protection against homologous strain challenge in young and senescent mice. To test VRP vaccine efficacy against a heterologous SARS-CoV, we used phylogenetic analyses, synthetic biology, and reverse genetics to construct a chimeric virus (icGDO3-S) encoding a synthetic S glycoprotein gene of the most genetically divergent human strain, GDO3, which clusters among the zoonotic SARS-CoV. icGD03-S replicated efficiently in human airway epithelial cells and in the lungs of young and senescent mice, and was highly resistant to neutralization with antisera directed against the Urbani strain. Although VRP-S vaccines provided complete short-term protection against heterologous icGD03-S challenge in young mice, only limited protection was seen in vaccinated senescent animals. VRP-N vaccines not only failed to protect from homologous or heterologous challenge, but resulted in enhanced immunopathology with eosinophilic infiltrates within the lungs of SARS-CoV–challenged mice. VRP-N–induced pathology presented at day 4, peaked around day 7, and persisted through day 14, and was likely mediated by cellular immune responses.

Conclusions

This study identifies gaps and challenges in vaccine design for controlling future SARS-CoV zoonosis, especially in vulnerable elderly populations. The availability of a SARS-CoV virus bearing heterologous S glycoproteins provides a robust challenge inoculum for evaluating vaccine efficacy against zoonotic strains, the most likely source of future outbreaks.

FINAL COMMENT

How likely is potentially fatal antibody dependent enhancement (ADE) with the COVID-19 vaccines? Simple answer: we don’t know. We are conducting a gigantic uncontrolled experiment on a large fraction of the human race.

These are the trial results reported by Moderna for their now widely used RNA vaccine.

https://www.modernatx.com/covid19vaccine-eua/providers/clini

The emergency use authorization for the Moderna vaccine was based on a study of 14,134 test subjects who actually received the vaccine and not the placebo. ADE requires both being vaccinated and exposure to the virus. The Moderna study did not involve deliberately exposing the roughly 28,000 test subjects (both placebo and actually vaccinated) to SARS-COV-2. Rather the study waited for natural infection of the test subjects — a tiny number:

The median length of follow up for efficacy for participants in the study was 9 weeks post Dose 2. There were 11 COVID‑19 cases in the Moderna COVID‑19 Vaccine group and 185 cases in the placebo group, with a vaccine efficacy of 94.1% (95% confidence interval of 89.3% to 96.8%).

(from Moderna web site on April 21, 2021)

The Emergency Use Authorization (not standard FDA approval which takes years) was based on a total of 196 COVID-19 cases. Presumably these 196 patients did not exhibit ADE. Test subjects in these clinical trials for vaccine approvals — or emergency use authorization in this case — are generally quite healthy and are not a representative sample of the frail elderly at most risk from COVID-19. Thus the risk of ADE in generally healthy vaccinated persons similar to the test subjects used in the Moderna clinical trial is probably (95% confidence level) less than one in sixty-six (66) persons (1.5 percent) using the rule of three in statistics.

UPDATE (April 27, 2021): It is a bit unclear what number — 196, 11, or 185 — to use in the rule of three. The Moderna trial reported only eleven (11) vaccinated subjects with COVID-19 and 185 unvaccinated subjects with COVID-19. Thus, one could use the eleven in which case the rule of three would imply that the risk of ADE is probably less than one in 3.6. However, about 185 vaccinated subjects were exposed to COVID-19 in the vaccinated group as in the un-vaccinated control group. The vaccine worked for all but 11 of these. This would make the correct number 185 instead of 196 and give an estimate that the risk of ADE is probably (95% confidence level) less than one in sixty-one (1:61) or 1.6 percent. Most likely the correct number is about 185 (not the naive 196); the estimated bound on the risk is just about the same for 185 and 196.

(C) 2021 by John F. McGowan, Ph.D.

About Me

John F. McGowan, Ph.D. solves problems using mathematics and mathematical software, including developing gesture recognition for touch devices, video compression and speech recognition technologies. He has extensive experience developing software in C, C++, MATLAB, Python, Visual Basic and many other programming languages. He has been a Visiting Scholar at HP Labs developing computer vision algorithms and software for mobile devices. He has worked as a contractor at NASA Ames Research Center involved in the research and development of image and video processing algorithms and technology. He has published articles on the origin and evolution of life, the exploration of Mars (anticipating the discovery of methane on Mars), and cheap access to space. He has a Ph.D. in physics from the University of Illinois at Urbana-Champaign and a B.S. in physics from the California Institute of Technology (Caltech).

The Uncertain COVID Baseline

US CDC’s Contradictory Pneumonia and Influenza Weekly Death Numbers 2014-2019

The United States Centers for Disease Control (CDC) has at least three different, grossly contradictory historical pneumonia and influenza death numbers. The CDC FluView pneumonia and influenza pre-COVID death number is OVER THREE TIMES the leading causes of death number. Pneumonia and influenza are often conflated in the CDC’s documentation and in the CDC’s influenza death model.

These death numbers are frequently used as the baseline for comparison of the COVID-19 death numbers and assessing the severity of the pandemic relative to previous years and pandemics such as the 1957, 1968, and 2009 influenza pandemics.

The Three Different Sets of Pneumonia and Influenza Death Numbers

Leading Causes of Death Pneumonia and Influenza (P&I) Deaths (About 55,000 per year)

FluView Pneumonia and Influenza (P&I) Deaths (About 167,000 pre-COVID, Over THREE TIMES Leading Causes of Death, About 7,000 Influenza Virus Deaths Per Year)

CDC Model Influenza Virus Deaths (About 55,000 per year, at least THREE TIMES FluView Influenza Deaths)

This is the program and data files used to generate the plot above comparing the CDC’s pneumonia and influenza death numbers from 2014 through 2019. Download and use the 7-Zip or other file archiver for MS Windows or the Unix command tar -xvf cdc_numbers.tar to unpack the program and data files.

This Python 3 program generates a plot comparing the different numbers on a log scale for easy comparison.

The program also plots the weekly deaths for “chronic lower respiratory disease,” mostly chronic bronchitis and emphysema — also referred to as “chronic obstructive pulmonary disease” in the medical literature. It is likely that the THREE TIMES LARGER FluView pneumonia and influenza death numbers are produced by borrowing deaths from chronic lower respiratory disease (mostly chronic bronchitis and emphysema) and adding them to the “pneumonia and influenza” deaths reported in the leading causes of death report.

The article “Are COVID Death Numbers Comparing Apples and Oranges?” at
http://wordpress.jmcgowan.com/wp/are-covid-death-numbers-comparing-apples-and-oranges/


discusses these issues in much more detail and provides references and links.

(C) 2021 by John F. McGowan, Ph.D.

About Me

John F. McGowan, Ph.D. solves problems using mathematics and mathematical software, including developing gesture recognition for touch devices, video compression and speech recognition technologies. He has extensive experience developing software in C, C++, MATLAB, Python, Visual Basic and many other programming languages. He has been a Visiting Scholar at HP Labs developing computer vision algorithms and software for mobile devices. He has worked as a contractor at NASA Ames Research Center involved in the research and development of image and video processing algorithms and technology. He has published articles on the origin and evolution of life, the exploration of Mars (anticipating the discovery of methane on Mars), and cheap access to space. He has a Ph.D. in physics from the University of Illinois at Urbana-Champaign and a B.S. in physics from the California Institute of Technology (Caltech).

[Video] The CDC’s Grossly Contradictory Flu Death Numbers

Graph Showing Contradictory CDC Pneumonia and Influenza Death Numbers
The CDC’s Grossly Contradictory Flu Death Numbers

Alternative Video Links: BitChute NewTube LBRY Archive

The United States Centers for Disease Control (CDC) has at least three different, grossly contradictory historical pneumonia and influenza death numbers. Pneumonia and influenza are often conflated in the CDC’s documentation and influenza death model. These death numbers are frequently used as the baseline for comparison of COVID-19 death numbers and assessing the severity of the pandemic relative to previous years and influenza pandemics.

Leading Causes of Death Pneumonia and Influenza (P&I) Deaths (About 55,000 per year)

FluView Pneumonia and Influenza (P&I) Deaths (About 188,000 pre-COVID, Over THREE TIMES Leading Causes of Death, About 5-15,000 Influenza Deaths Per Year)

CDC Model Influenza Only Deaths (About 55,000 per year, at least THREE TIMES FluView Influenza Deaths)

This video discusses these different contradictory numbers and their implications for the COVID-19 pandemic.

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(C) 2021 by John F. McGowan, Ph.D.

About Me

John F. McGowan, Ph.D. solves problems using mathematics and mathematical software, including developing gesture recognition for touch devices, video compression and speech recognition technologies. He has extensive experience developing software in C, C++, MATLAB, Python, Visual Basic and many other programming languages. He has been a Visiting Scholar at HP Labs developing computer vision algorithms and software for mobile devices. He has worked as a contractor at NASA Ames Research Center involved in the research and development of image and video processing algorithms and technology. He has published articles on the origin and evolution of life, the exploration of Mars (anticipating the discovery of methane on Mars), and cheap access to space. He has a Ph.D. in physics from the University of Illinois at Urbana-Champaign and a B.S. in physics from the California Institute of Technology (Caltech).