COVID: The Psychology of Totalitarianism Book Review

The Psychology of Totalitarianism
Mattias Desmet
Chelsea Green Publishing
White River Junction, Vermont, USA 2022, 231 pages
https://www.chelseagreen.com/product/the-psychology-of-totalitarianism/
(also available on Amazon)

Introduction

The Psychology of Totalitarianism is a new book by Mattias Desmet, a professor of clinical psychology at Ghent University in Belgium, outlining his theory of “mass formation” especially with respect to the response to the COVID-19 pandemic. His theory of “mass formation” was popularized by Dr. Robert Malone, the inventor or one of the inventors of the mRNA vaccine technology, during Malone’s Joe Rogan interview on the COVID pandemic and the COVID vaccines, using the name “mass formation psychosis” which Desmet does not use. Desmet has appeared on several podcasts touting his ideas since then, with several recent appearances to promote the book.

Briefly, I found the case for Desmet’s theory of mass formation with respect to the COVID response unconvincing, although I believe some of the factors such as widespread loneliness and social isolation that he discusses are contributing factors. Some sections of the book are quite interesting and insightful but for other reasons.

Rather, the “groupthink” and grossly irrational behavior during the COVID pandemic can be attributed to a “collective fight or flight response” not specific to totalitarianism, long predating the modern era, and common during wars and war-like episodes such as the aftermath of the September 11 attacks in the United States, World War I and World War II. This collective fight or flight response has been aggravated by pandemic profiteers such as Pfizer and Bill Gates much the same way that “Merchants of Death” selling weapons have aggravated the fight or flight response both before and during wars.

Mass formation is a theory to explain extreme instances of “mass hysteria” or “groupthink” including such episodes as the bloody purges in Stalinist Russia and Nazi Germany. The term and various mass formation theories predates Desmet who has his own variant of the theory which is elaborated in detail in the book. He cites such scholars as Gustave Le Bon and Hannah Arendt.

The book is well written, translated into easily readable English by Els Vanbrabant. A few sections are a bit dry and academic, but overall the English version is clear and interesting with no hint that it is translated other than the frequent references to Belgium and Belgians. It includes an index and references, although a number of critical statements lack footnotes.

The book is clearly marketed toward skeptics of the official COVID narrative or those with significant doubts — hopefully a large and growing group given the evident massive failures of the COVID vaccines since the summer of 2021. Others may be unable to see the case for widespread mass hysteria, groupthink, or other irrationality in the COVID response. The book cover and first pages feature numerous laudatory quotes from Robert Malone MD, Peter McCullough MD, and other prominent critics of the official narrative, policies, and generally the COVID vaccines. These one sided endorsements are likely to limit the reach of the book.

Desmet’s mass formation theory in the book is really two theories that he links together in a whole. The second theory is the mass formation theory that Desmet and Malone have discussed on several occasions. Namely, a general environment of loneliness, social isolation, lack of meaning, and “free floating anxiety” leads to a situation where a large fraction of the population (about thirty percent) fanatically embraces a simplistic, often rapidly changing narrative that provides a powerful sense of both meaning and solidarity with other people, substituting the greater good of the collective for normal social and moral relations. This mass formation is a form of collective hypnosis involving a narrow focus on a single simple goal such as “zero COVID” at any cost, including self-destructive measures and monstrous acts that would normally be rejected as immoral.

A Critique of Scientific “Rationalism”

In the book, Desmet attributes this environment of loneliness, social isolation, lack of meaning, and the associated free floating anxiety to the flaws and limitations of the modern Enlightenment rational materialistic mechanical worldview beloved of many scientists, engineers, and other intellectuals including himself until age thirty-five. Note that the social isolation and associated problems could have another cause than the rational scientific worldview but give rise to the mass formation. Desmet is specific in blaming the “rational” worldview however for the preexisting conditions that make possible the mass formation.

Desmet’s critique of the “rationalist” worldview, perhaps better called “scientism,” is extensive with many good points and insightful discussions of flaws in mainstream science and statistics, making up most of the book, nearly all of the first and third parts. The mass formation theory that many readers may have encountered on podcasts before the book’s recent publication makes up part two which is only about three chapters, sixty pages.

For me Desmet’s extensive criticism of the scientific rationalist materialistic worldview as he calls it was the most interesting part of the book, even though I disagree with his overall thesis. I found his discussion of the practical problems with statistics and graphical data presentation, focusing on the dismal and misleading use of statistics during the COVID pandemic, particularly interesting and insightful.

That said, Desmet’s discussion of quantum mechanics in modern physics is incorrect. The mainstream Copenhagen interpretation of quantum mechanics does not give consciousness any special role in the measurement or observation in quantum mechanics. Some physicists have theorized consciousness in some way is the “measurement” or “observation” that collapses the quantum wave function in the mainstream Copenhagen theory. This is a fringe view.

The Copenhagen interpretation of quantum mechanics is almost certainly “incomplete” and logically flawed as Einstein argued in his 1935 paper with Podolsky and Rosen. The problem is the lack of a clear consistent definition of “measurement” or “observation” in the mainstream theory. Incompleteness does not however mean that consciousness plays a central role in quantum mechanics as Desmet claims in several places. Most non-Copenhagen theories to resolve the incompleteness — for example the many worlds theory of QM — do not use consciousness to resolve the logical flaws in the Copenhagen Quantum Mechanics illustrated by Schrodinger’s Cat and other paradoxes.

David Bohm’s pilot wave theory — derived from the earlier pilot wave ideas of his mentor Einstein as well as deBroglie and Schrodinger — actually removes the need to invoke either a wave function collapse or consciousness by interpreting the quantum system as a pilot wave and a discrete particle somewhat like radar controlled drone guided by a radar signal bouncing and diffracting through a mountain range. The drone always has a specific location and velocity whereas the radar beam is spread out over the landscape, interfering with itself and causing confusing wavelike behavior in the trajectory of the drone.

Although Bohm linked his ideas to mysticism with the pilot wave or “quantum potential” analogized to the World Spirit (Anima Mundi) of western mysticism or the chi of eastern mysticism, the pilot wave theory is entirely mechanistic.

Desmet’s discussion of the supposed scientific revolution during the 17th century, illustrated with the usual stories about Galileo, is what most scientists and intellectuals in the modern world are taught. Yet it is grossly contradicted by the actual historical record which shows a seamless evolution from religion and mysticism, most clearly with the work of Johannes Kepler and Tycho Brahe, both mystics, alchemists, astrologers, and deeply religious men who envisioned God as mathematician dictating mathematical laws obeyed by subsidiary spirits or angels embodied in the Sun and planets.

This notion of a predictable, mathematical universe created by a God or gods is very old, dating back to Pythagoras in ancient Greek and very likely Pythagoras’s teachers in Egypt and Babylonia (modern Iraq). A benevolent God would hardly be the capricious, inscrutable nut case pictured by Carl Sagan and other atheist science popularizers in recent decades, instead providing rational laws of nature for His human creations.

The common textbook notion of a scientific revolution in the 17th century rejecting medieval religion and superstition, epitomized by Galileo and his clash with the Catholic Church, appears to be a projection of atheistic, materialistic views that became dominant in organized, professionalized science during the 19th century and early 20th century.

The Collective Fight or Flight Response

The fight or flight response is a powerful reaction to an immediate perceived threat such as a tiger or other large predator, a car accident, a human antagonist such as a mugger, or other physical dangers. It involves a narrowing of focus to the immediate threat, short term thinking, a strong physical response mediated by adrenaline and other hormones.

An extreme fight or flight response can include loss of pain sensations, the ability to fight and kill with severe, normally disabling or fatal injuries, and other dramatic changes. Many higher cognitive functions are lowered or turned off to handle the immediate threat. Some short term thinking skills and reflexes may be enhanced instead. The immune system is reduced or turned off to focus all energies on the immediate threat.

Human beings and other herd animals also have a collective fight or flight response most evident during wars or public emergencies. Obedience to authority increases. Conformity increases. People and groups that are perceived as different are frequently attacked, isolated (e.g. confinement of American Indians to reservations, internment of Japanese Americans in WW2), driven out (e.g. enslavement and expulsion of most Wampanoag from the Massachusetts colony after King Philips War in 1675) or killed (e.g. massacre of settlers by the Dakota Sioux in Minnesota in 1862). The collective focus narrows to the immediate survival threat. Group members will display flags or other signs to indicate membership in the group (e.g. wearing masks during the COVID pandemic, displaying vaccine cards and certificates) and make differentiating the group from the attackers easier.

These are instinctive, primal responses probably adapted to repelling an attack by a rival tribe or clan in ancient times. As in the individual fight or flight response, higher cognitive function is curtailed or turned off. If your village is being attacked by the tribe across the river, it is not the time for nuanced thought. Language such as “you are either with us or against us” surfaces. The tribe coalesces into a single military unit and fights as one.

The collective fight or flight response does not require preexisting loneliness, social isolation, discontent, a lack of meaning or any negative conditions at all. It simply requires a perceived physical threat to the group.

This is the “mass formation” behavior during the COVID-19 pandemic. Pandemics, even if due to the deliberate release of a biological weapon, are not attacks by a rival tribe in 10,000 BC. The collective fight or flight response can be disastrous in a non-military public emergency, real or imagined.

War profiteers learned a long time ago to provoke and exploit the collective fight or flight response to create and prolong wars, boosting profits often with disastrous consequences for most people. Pandemic profiteers such as Pfizer and Bill Gates can do the same.

Conclusion

The Psychology of Totalitarianism is well worth reading, both because of Desmet’s insights on scientific rationalism and because it will undoubtedly influence the debate and conflict over the COVID pandemic, vaccines, and policies. However, those skeptical of the rapidly changing COVID narrative or major parts of the narrative should not embrace Desmet’s mass formation hypothesis. While it is likely widespread loneliness and lack of meaning has contributed to the overreaction, the main cause is probably the primal collective fight or flight response stoked by a continuing barrage of fear porn from the advertising funded mass media.

Psychoanalyzing people to their face is rarely persuasive. Most people find this condescending and offensive. Desmet eschews the phrase “mass formation psychosis” with good reason and COVID skeptics should particularly avoid telling other people that they are psychotic.

(C) 2022 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] Ukraine COVID and Biden Approval Ratings Deeper Dive

Uncensored Video Links: BitChute Odysee Rumble

Short video discussing results of analyzing President Biden’s declining approval ratings and the possible effect of the COVID pandemic and Ukraine crises on the approval ratings.

A detailed longer explanation of the analysis discussed can be found in the previous video “How to Analyze Simple Data Using Python” available on all of our video channels.

(C) 2022 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] The US CDC’s Broad Legal Disclaimer for the Fluview Interactive COVID Death Numbers

This is the legal disclaimer that appears when starting the US Centers for Disease Control (CDC’s) Fluview Interactive application which purports to report the percentage of deaths per week “due to” pneumonia and influenza (P&I) prior to March 2020 and pneumonia, influenza, and COVID-19 (PIC) since March 2020. (URL: http://gis.cdc.gov/grasp/fluview/mortality.html )

Emphasis is added to key phrases. The NOTES explain the definition and meaning of several technical terms used in the disclaimer.

The disclaimer essentially says, in plain English, the data — the COVID-19 death counts — which is presented with no estimates of statistical or systematic errors is provisional and could be entirely wrong. Two sentences in one paragraph appear to contradict one another.

The CDC had a budget of just over $12 billion in the Fiscal Year 2019. URL: https://www.cdc.gov/budget/documents/fy2019/fy-2019-detail-table.pdf

Disclaimer (Downloaded December 24, 2021)

National Center for Health Statistics Mortality Surveillance System-

NOTE: The National Center for Health Statistics (NCHS) is a division of the US Centers for Disease Control and Prevention (CDC).

The National Center for Health Statistics (NCHS) collects and disseminates the Nation’s official vital statistics. NCHS collects death certificate data from state vital statistics offices for all deaths occurring in the United States. Pneumonia and/or influenza (P&I) deaths and pneumonia, influenza and/or COVID-19 (PIC) deaths are identified based on ICD-10 multiple cause of death codes.

NOTE: ICD-10 is the International Classification of Diseases 10th Edition, a medical classification list by the World Health Organization (WHO). “ICD-10 multiple cause of death codes” refers to multiple “causes of death” listed on death certificates. Many death certificates have many causes of death such as emphysema, a degenerative eventually terminal condition, and pneumonia. One cause of the death is singled out as the “underlying cause of death” or UCOD. One cause of death is singled out as the “immediate cause of death.” The immediate cause of death is often not the underlying cause of death. For example, emphysema may be the underlying cause of death and pneumonia, the influenza virus, or the “common cold” may be the immediate cause of death.

NCHS Mortality Surveillance System data are presented by the week the death occurred at the national, state, and HHS Region levels, based on the state of residence of the decedent. Data on the percentage of deaths due to P&I or PIC are released one week after the week of death to allow for collection of enough data to produce a stable percentage. States and HHS regions with less than 20% of the expected total deaths (average number of total deaths reported by week during 2008-2012) will be marked as having insufficient data. Not all deaths are reported within a week of death therefore data for earlier weeks are continually revised and the proportion of deaths due to P&I or PIC may increase or decrease as new and updated death certificate data are received by NCHS.

NOTE: Notice the conflict between “to allow for collection of enough data to produce a stable percentage” and “the proportion of deaths due to P&I or PIC may increase or decrease as new and updated death certificate data are received by NCHS.”  Percentage is a way of expressing the proportion: for example, fifty percent (a percentage) versus one half (another way of expressing the same percentage).  “Stable” usually means not changing or fluctuating” (Merriam Webster) when used in this way.

The COVID-19 death counts reported by NCHS and presented here are provisional and will not match counts in other sources, such as media reports or numbers from county health departments. COVID-19 deaths may be classified or defined differently in various reporting and surveillance systems. Death counts reported by NCHS include deaths that have COVID-19 listed as a cause of death and may include laboratory confirmed COVID-19 deaths and clinically confirmed COVID-19 deaths. Provisional death counts reported by NCHS track approximately 1-2 weeks behind other published data sources on the number of COVID-19 deaths in the U.S. These reasons may partly account for differences between NCHS reported death counts and death counts reported in other sources.

NOTE:  The language “a cause of death” likely means that COVID-19 (or pneumonia or influenza in pre-2020 figures) is one of the causes of death listed on the death certificate, not necessarily the underlying cause of death (UCOD).  Remember, many death certificates have multiple causes of death, one of which is identified as the underlying cause of death. (UCOD).  Note also that the disclaimer specifically states that NCHS numbers “will not match..numbers from county health departments.” County health departments are presumably official, primary sources of death data with qualified staff — medical examiners and others.

In previous seasons, the NCHS surveillance data were used to calculate the percent of all deaths occurring each week that had pneumonia and/or influenza (P&I) listed as a cause of death. Because of the ongoing COVID-19 pandemic, COVID-19 coded deaths were added to P&I to create the PIC (pneumonia, influenza, and/or COVID-19) classification. PIC includes all deaths with pneumonia, influenza, and/or COVID-19 listed on the death certificate. Because many influenza deaths and many COVID-19 deaths have pneumonia included on the death certificate, P&I no longer measures the impact of influenza in the same way that it has in the past. This is because the proportion of pneumonia deaths associated with influenza is now influenced by COVID-19-related pneumonia. The PIC percentage and the number of influenza and number of COVID-19 deaths will be presented in order to help better understand the impact of these viruses on mortality and the relative contribution of each virus to PIC mortality.

The PIC percentages are compared to a seasonal baseline of P&I deaths that is calculated using a periodic regression model that incorporates a robust regression procedure applied to data from the previous five years. An increase of 1.645 standard deviations above the seasonal baseline of P&I deaths is considered the “epidemic threshold,” i.e., the point at which the observed proportion of deaths is significantly higher than would be expected at that time of the year in the absence of substantial influenza, and now COVID-related mortality. Baselines and thresholds are calculated at the national and regional level and by age groups.

For more information on pneumonia and influenza mortality surveillance please visit: http://www.cdc.gov/flu/weekly/overview.htm#Mortality

* The 10 U.S. Department of Health and Human Services regions include the following jurisdictions. Region 1: Connecticut, Main, Massachusetts, New Hampshire, Rhode Island, and Vermont; Region 2: New Jersey, New York, and New York City; Region 3: Delaware, District of Columbia, Maryland, Pennsylvania, Virginia, and West Virginia; Region 4: Alabama, Florida, Georgia, Kentucky, Mississippi, North Carolina, South Carolina, and Tennessee; Region 5: Illinois, Indiana, Michigan, Minnesota, Ohio, and Wisconsin; Region 6: Arkansas, Louisiana, New Mexico, Oklahoma, and Texas; Region 7: Iowa, Kansas, Missouri, and Nebraska; Region 8: Colorado, Montana, North Dakota, South Dakota, Utah, and Wyoming; Region 9: Arizona, California, Hawaii, and Nevada; Region 10: Alaska, Idaho, Oregon, and Washington.

(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 Great Barrington Declaration

The Great Barrington Declaration: https://gbdeclaration.org/

Scientists, medical doctors, health professionals, and the general public protest the continued lockdowns ostensibly to fight COVID-19.

(C) 2020 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).

Decoupling Trump from COVID-19

Matt Taibbi and Katie Halper on Decoupling Trump from COVID-19

They say it better than I can.

(C) 2020 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).

Corrected Pneumonia and Influenza Weekly Deaths Plot

Weekly Pneumonia and Influenza Death Numbers for 2020 and 2019 Compared

The updated plot is from a LibreOffice spreadsheet which can be downloaded at the link below. LibreOffice is a free, open-source alternative to Microsoft Office. It is available for Microsoft Windows, Mac OS X, and most flavors of Unix. It can be downloaded here.

The original data is from https://www.cdc.gov/flu/weekly/weeklyarchives2019-2020/data/NCHSData14.csv

The companion video for this article is at: https://youtu.be/DcjeKzmLjz8 and https://www.bitchute.com/video/LvKUWJOxcTSq/ The video is about sixteen minutes long. It is usually faster to read the written article than listen to the companion video.

I made a mistake copying the column of pneumonia and influenza deaths from early 2019 (weeks 1-13 of 2019 and weeks 50-52 of 2020) with LibreOffice Calc (the spreadsheet). The spreadsheet copied the macros in the cells instead of the number values. These macros were then applied to the columns to the left of the copied column, giving incorrect values that exaggerated the excess of deaths in comparable weeks last year (2019).

The actual excess is 1,841 more deaths in 2019, not about 6,000.

I was using the spreadsheet to make the results more accessible to a general audience. The copying error was consistent with the results from the more in-depth Python data analysis. In retrospect I should have checked the spreadsheet numbers more carefully.

Discussion

This does not change the conclusion that there is no sign of COVID-19 in the numbers until March 14, 2020 and a weak rise consistent with normal fluctuations in the weekly numbers in the final two weeks (March 14-28, 2020, weeks 12 and 13). It does reduce the size of the discrepancy between the two years. It remains possible that all the about 1,600 COVID-19 deaths reported as of March 28, 2020 could be conventional pneumonia and influenza deaths labeled as COVID-19 due to false positive RT-PCR tests and other misdiagnoses.

As I have discussed, there are strong reasons to doubt the CDC numbers. The most egregious I have found so far is the remarkable difference between the about 55,000 deaths from “influenza and pneumonia” in the leading causes of death tables (Table B, Page Six) and the about 188,000 deaths from “pneumonia and influenza” in the NCHSData14.csv file and other NCHSData<Week Number>.csv files.

An educated guess is that the 55,000 deaths from “influenza and pneumonia” is the output of a model the CDC uses to estimate the number of deaths directly or indirectly caused by “influenza viruses.” In the weekly pneumonia and influenza death numbers, the vast majority of deaths are listed as pneumonia and not the separate “influenza” category. Thus about 130,000 deaths appear to have been assigned to other categories in the final deaths for 2017 report, possibly “chronic lower respiratory diseases” which is the fourth (4th) leading cause of death. This is however a theory and CDC should carefully clarify what they are doing.

Accordingly, it is difficult to know what pre-processing or modeling/estimation may have been applied to the weekly pneumonia and influenza death numbers, although the commentary on the CDC web site implies these numbers are counts of death certificates and the causes of death on death certificates reported to the CDC by state and local authorities.

I am looking through the NCHSData<Week Number>.csv files to see how complete they may actually be. The FluView web page contains a table that seems to imply that all weeks except the very last week in the file are complete or almost complete. They use the label “> 100%” where > is presumably “greater than”. Of course, 100 percent usually means complete.

CDC “Percent Complete” Table (Misleading language at best)

There are many possible reasons for COVID-19 deaths not showing up in the weekly pneumonia and influenza death numbers before March 14, 2020 despite the Chinese coverup in December and early January, the US testing fiasco, the 430,000 visitors to the United States from China since the coronavirus surfaced, and the many asymptomatic carriers now being detected. These different possible reasons have different, even opposite in some cases, implications for public health policy.

Possible reasons include:

Despite the many problems above, the public health authorities have been remarkably successful in identifying nearly all COVID-19 deaths up to March 14, 2020. This seems too good to be true, but cannot be excluded.

The infection fatality rate (aka actual mortality rate) of the COVID-19 coronavirus is much less than early numbers such as 3.4 percent from the World Health Organization (WHO) or the 0.9-1 percent used by various authorities. Iceland, South Korean, Denmark and German data suggest about 0.5 percent mortality rate – which still could be higher than real rate.

Many COVID-19 deaths are due to aggressive treatment of the disease, e.g. intubation, rather than the disease alone.

The weekly pneumonia and influenza death numbers are substantially incomplete, due to normal delays or due to unusual delays associated with the crisis.

There has been a compensating drop in non-COVID pneumonia and influenza deaths due to shelter-in-place and taking it easy. Elderly and susceptible persons may have taken precautions in January and February due to the publicity, even before the shutdown in mid March.

Something else

Some combination of some or all of the above!

Conclusion

There is a remarkable lack of key measurements in the current coronavirus COVID-19 pandemic. These include the actual mortality rate (aka infection fatality rate) broken down by age, sex, race, pre-existing medical conditions, ambient temperature, sunlight levels, pollution levels, and other risk factors. The false positive and false negative rates of the tests for the disease, both the tests for an active infection such as the RT-PCR tests and tests for past infection such as the antibody tests. The methods and rates of transmission for the disease. Aerosol transmission probably occurs at least at a low level and is virtually unstoppable.

It is important to collect this data and measure these key parameters as quickly as possible in an open, “transparent” manner with multiple independent teams, not all funded or controlled by the CDC, as soon as possible to make good decisions based on knowledge and data, rather than fear, ignorance, and the primal fight or flight response.

(C) 2020 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 Controversy Over Continuing Construction in Residential and Mixed-Residential Neighborhoods in Santa Clara County, CA

This is an article on the controversy over continuing construction in residential and mixed-residential neighborhoods in Palo Alto during the COVID-19 coronavirus pandemic:

https://www.paloaltoonline.com/news/2020/04/03/as-palo-alto-halts-major-construction-projects-home-builders-keep-working?utm_source=express-2020-04-06&utm_medium=email&utm_campaign=express

Clearly many people in the region share my concern for the baffling continuation of clearly non-essential construction projects in densely populated residential and mixed residential neighborhoods. The apartment complex across the street from me is clearly a luxury apartment complex aimed at prosperous Apple, Google, and other high tech employees. There are several going up in the neighborhood, in a number of cases having knocked down less expensive apartments to make room. The builders and their allies in the city/county/state may well claim they are “mixed use,” but … seriously?

Given the severity of the claims about COVID-19 one should question the wisdom of continuing even genuine affordable housing projects in highly populated areas.

It is becoming increasingly clear that many people will not be symptomatic during an active infection. Huge numbers are being revealed by the testing. Thus, measures such as body temperature checks, asking workers with coughs to stay home etc., — touted by the construction companies in the article — will not be effective in halting the spread of the disease.

Having said that, the actual mortality rate and ease of transmission of the disease, as well as other key factors, are not known. People should calm down and press hard to get these and other critical numbers evaluated quickly, accurately in an open “transparent” manner.

(C) 2020 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).