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] Why Did Biden’s Approval Crash in August 2021?

Uncensored Video: Odysee NewTube ARCHIVE BitChute

Twelve minute video on why President Biden’s approval ratings crashed in August of 2021.

References:

https://www.pbs.org/newshour/show/older-americans-make-up-a-majority-of-covid-deaths-they-are-falling-behind-on-boosters

https://www.npr.org/2022/02/19/1081948849/elderly-people-make-up-75-of-covid-19-deaths-partially-due-to-loneliness

https://web.archive.org/web/20210731120830/https://covid.cdc.gov/covid-data-tracker/#datatracker-home

https://gis.cdc.gov/grasp/fluview/mortality.html

https://www.cnn.com/2021/07/22/politics/fact-check-biden-cnn-town-hall-july/index.html

Jefferson’s First Inaugural Address: https://avalon.law.yale.edu/19th_century/jefinau1.asp

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(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] How to Analyze Data with a Baseline Linear Model in Python

This article shows Python programming language source code to perform a simple linear model analysis of time series data. Most real world data is not linear but a linear model provides a common baseline starting point for comparison of more advanced, generally non-linear models.

Simulated Nearly Linear Data with Linear Model
"""
Standalone linear model example code.

Generate simulated data and fit model to this simulated data.

LINEAR MODEL FORMULA:

OUTPUT = MULT_T*DATE_TIME + MULT_1*INPUT_1 + MULT_2*INPUT_2 + CONSTANT + NOISE

set MULT_T to 0.0 for simulated data.  Asterisk * means MULTIPLY
from grade school arithmetic.  Python and most programming languages
use * to indicate ordinary multiplication.

(C) 2022 by Mathematical Software Inc.

Point of Contact (POC): John F. McGowan, Ph.D.
E-Mail: ceo@mathematical-software.com

"""

# Python Standard Library
import os
import sys
import time
import datetime
import traceback
import inspect
import glob
# Python add on modules
import numpy as np  # NumPy
import pandas as pd  # Python Data Analysis Library
import matplotlib.pyplot as plt  # MATLAB style plotting
from sklearn.metrics import r2_score  # scikit-learn
import statsmodels.api as sm  # OLS etc.

# STATSMODELS
#
# statsmodels is a Python module that provides classes and functions for
# the estimation of many different statistical models, as well as for
# conducting statistical tests, and statistical data exploration. An
# extensive list of result statistics are available for each
# estimator. The results are tested against existing statistical
# packages to ensure that they are correct. The package is released
# under the open source Modified BSD (3-clause) license.
# The online documentation is hosted at statsmodels.org.
#
# statsmodels supports specifying models using R-style formulas and pandas DataFrames. 


def debug_prefix(stack_index=0):
    """
    return <file_name>:<line_number> (<function_name>)

    REQUIRES: import inspect
    """
    the_stack = inspect.stack()
    lineno = the_stack[stack_index + 1].lineno
    filename = the_stack[stack_index + 1].filename
    function = the_stack[stack_index + 1].function
    return (str(filename) + ":"
            + str(lineno)
            + " (" + str(function) + ") ")  # debug_prefix()


def is_1d(array_np,
          b_trace=False):
    """
    check if array_np is 1-d array

    Such as array_np.shape:  (n,), (1,n), (n,1), (1,1,n) etc.

    RETURNS: True or False

    TESTING: Use DOS> python -c "from standalone_linear import *;test_is_1d()"
    to test this function.

    """
    if not isinstance(array_np, np.ndarray):
        raise TypeError(debug_prefix() + "argument is type "
                        + str(type(array_np))
                        + " Expected np.ndarray")

    if array_np.ndim == 1:
        # array_np.shape == (n,)
        return True
    elif array_np.ndim > 1:
        # (2,3,...)-d array
        # with only one axis with more than one element
        # such as array_np.shape == (n, 1) etc.
        #
        # NOTE: np.array.shape is a tuple (not a np.ndarray)
        # tuple does not have a shape
        #
        if b_trace:
            print("array_np.shape:", array_np.shape)
            print("type(array_np.shape:",
                  type(array_np.shape))
            
        temp = np.array(array_np.shape)  # convert tuple to np.array
        reference = np.ones(temp.shape, dtype=int)

        if b_trace:
            print("reference:", reference)

        mask = np.zeros(temp.shape, dtype=bool)
        for index, value in enumerate(temp):
            if value == 1:
                mask[index] = True

        if b_trace:
            print("mask:", mask)
        
        # number of axes with one element
        axes = temp[mask]
        if isinstance(axes, np.ndarray):
            n_ones = axes.size
        else:
            n_ones = axes
            
        if n_ones >= (array_np.ndim - 1):
            return True
        else:
            return False
    # END is_1d(array_np)


def test_is_1d():
    """
    test is_1d(array_np) function  works
    """

    assert is_1d(np.array([1, 2, 3]))
    assert is_1d(np.array([[10, 20, 33.3]]))
    assert is_1d(np.array([[1.0], [2.2], [3.34]]))
    assert is_1d(np.array([[[1.0], [2.2], [3.3]]]))
    
    assert not is_1d(np.array([[1.1, 2.2], [3.3, 4.4]]))

    print(debug_prefix(), "PASSED")
    # test_is_1d()


def is_time_column(column_np):
    """
    check if column_np is consistent with a time step sequence
    with uniform time steps. e.g. [0.0, 0.1, 0.2, 0.3,...]

    ARGUMENT: column_np -- np.ndarray with sequence

    RETURNS: True or False
    """
    if not isinstance(column_np, np.ndarray):
        raise TypeError(debug_prefix() + "argument is type "
                        + str(type(column_np))
                        + " Expected np.ndarray")

    if is_1d(column_np):
        # verify if time step sequence is nearly uniform
        # sequence of time steps such as (0.0, 0.1, 0.2, ...)
        #
        delta_t = np.zeros(column_np.size-1)
        for index, tval in enumerate(column_np.ravel()):
            if index > 0:
                previous_time = column_np[index-1]
                if tval > previous_time:
                    delta_t[index-1] = tval - previous_time
                else:
                    return False

        # now check that time steps are almost the same
        delta_t = np.median(delta_t)
        delta_range = np.max(delta_t) - np.min(delta_t)
        delta_pct = delta_range / delta_t
        
        print(debug_prefix(),
              "INFO: delta_pct is:", delta_pct, flush=True)
        
        if delta_pct > 1e-6:
            return False
        else:
            return True  # steps are almost the same
    else:
        raise ValueError(debug_prefix() + "argument has more"
                         + " than one (1) dimension.  Expected 1-d")
    # END is_time_column(array_np)


def validate_time_series(time_series):
    """
    validate a time series NumPy array

    Should be a 2-D NumPy array (np.ndarray) of float numbers

    REQUIRES: import numpy as np

    """
    if not isinstance(time_series, np.ndarray):
        raise TypeError(debug_prefix(stack_index=1)
                        + " time_series is type "
                        + str(type(time_series))
                        + " Expected np.ndarray")

    if not time_series.ndim == 2:
        raise TypeError(debug_prefix(stack_index=1)
                        + " time_series.ndim is "
                        + str(time_series.ndim)
                        + " Expected two (2).")

    for row in range(time_series.shape[0]):
        for col in range(time_series.shape[1]):
            value = time_series[row, col]
            if not isinstance(value, np.float64):
                raise TypeError(debug_prefix(stack_index=1)
                                + "time_series[" + str(row)
                                + ", " + str(col) + "] is type "
                                + str(type(value))
                                + " expected float.")

    # check if first column is a sequence of nearly uniform time steps
    #
    if not is_time_column(time_series[:, 0]):
        raise TypeError(debug_prefix(stack_index=1)
                        + "time_series[:, 0] is not a "
                        + "sequence of nearly uniform time steps.")

    return True  # validate_time_series(...)


def fit_linear_to_time_series(new_series):
    """
    Fit multivariate linear model to data.  A wrapper
    for ordinary least squares (OLS).  Include possibility
    of direct linear dependence of the output on the date/time.
    Mathematical formula:

    output = MULT_T*DATE_TIME + MULT_1*INPUT_1 + ... + CONSTANT

    ARGUMENTS: new_series -- np.ndarray with two dimensions
                             with multivariate time series.
                             Each column is a variable.  The
                             first column is the date/time
                             as a float value, usually a
                             fractional year.  Final column
                             is generally the suspected output
                             or dependent variable.

                             (time)(input_1)...(output)

    RETURNS: fitted_series -- np.ndarray with two dimensions
                              and two columns: (date/time) (output
                              of fitted model)

             results --
                 statsmodels.regression.linear_model.RegressionResults

    REQUIRES: import numpy as np
              import pandas as pd
              import statsmodels.api as sm  # OLS etc.

    (C) 2022 by Mathematical Software Inc.

    """
    validate_time_series(new_series)

    #
    # a data frame is a package for a set of numbers
    # that includes key information such as column names,
    # units etc.
    #
    input_data_df = pd.DataFrame(new_series[:, :-1])
    input_data_df = sm.add_constant(input_data_df)

    output_data_df = pd.DataFrame(new_series[:, -1])

    # statsmodels Ordinary Least Squares (OLS)
    model = sm.OLS(output_data_df, input_data_df)
    results = model.fit()  # fit linear model to the data
    print(results.summary())  # print summary of results
                              # with fit parameters, goodness
                              # of fit statistics etc.

    # compute fitted model values for comparison to data
    #
    fitted_values_df = results.predict(input_data_df)

    fitted_series = np.vstack((new_series[:, 0],
                               fitted_values_df.values)).transpose()

    assert fitted_series.shape[1] == 2, \
        str(fitted_series.shape[1]) + " columns, expected two(2)."

    validate_time_series(fitted_series)

    return fitted_series, results  # fit_linear_to_time_series(...)


def test_fit_linear_to_time_series():
    """
    simple test of fitting  a linear model to simple
    simulated data.

    ACTION: Displays plot comparing data to the linear model.

    REQUIRES: import numpy as np
              import matplotlib.pyplot as plt
              from sklearn.metrics impor r2_score (scikit-learn)

    NOTE: In mathematics a function f(x) is linear if:

    f(x + y) = f(x) + f(y)  # function of sum of two inputs
                            # is sum of function of each input value

    f(a*x) = a*f(x)         # function of constant multiplied by
                            # an input is the same constant
                            # multiplied by the function of the
                            # input value

    (C) 2022 by Mathematical Software Inc.
    """

    # simulate monthly data for years 2010 to 2021
    time_steps = np.linspace(2010.0, 2022.0, 120)
    #
    # set random number generator "seed"
    #
    np.random.seed(375123)  # make test reproducible
    # make random walks for the input values
    input_1 = np.cumsum(np.random.normal(size=time_steps.shape))
    input_2 = np.cumsum(np.random.normal(size=time_steps.shape))

    # often awe inspiring Greek letters (alpha, beta,...)
    mult_1 = 1.0  # coefficient or multiplier for input_1
    mult_2 = 2.0   # coefficient or multiplier for input_2
    constant = 3.0  # constant value  (sometimes "pedestal" or "offset")

    # simple linear model
    output = mult_1*input_1 + mult_2*input_2 + constant
    # add some simulated noise
    noise = np.random.normal(loc=0.0,
                             scale=2.0,
                             size=time_steps.shape)

    output = output + noise

    # bundle the series into a single multivariate time series
    data_series = np.vstack((time_steps,
                             input_1,
                             input_2,
                             output)).transpose()

    #
    # np.vstack((array1, array2)) vertically stacks
    # array1 on top of array 2:
    #
    #  (array 1)
    #  (array 2)
    #
    # transpose() to convert rows to vertical columns
    #
    # data_series has rows:
    #    (date_time, input_1, input_2, output)
    #    ...
    #

    # the model fit will estimate the values for the
    # linear model parameters MULT_T, MULT_1, and MULT_2

    fitted_series, \
        fit_results = fit_linear_to_time_series(data_series)

    assert fitted_series.shape[1] == 2, "wrong number of columns"

    model_output = fitted_series[:, 1].flatten()

    #
    # Is the model "good enough" for practical use?
    #
    # Compure R-SQUARED also known as R**2
    # coefficient of determination, a goodness of fit measure
    # roughly percent agreement between data and model
    #
    r2 = r2_score(output,  # ground truth / data
                  model_output  # predicted values
                  )

    #
    # Plot data and model predictions
    #

    model_str = "OUTPUT = MULT_1*INPUT_1 + MULT_2*INPUT_2 + CONSTANT"

    f1 = plt.figure()
    # set light gray background for plot
    # must do this at start after plt.figure() call for some
    # reason
    #
    ax = plt.axes()  # get plot axes
    ax.set_facecolor("lightgray")  # confusingly use set_facecolor(...)
    # plt.ylim((ylow, yhi))  # debug code
    plt.plot(time_steps, output, 'g+', label='DATA')
    plt.plot(time_steps, model_output, 'b-', label='MODEL')
    plt.plot(time_steps, data_series[:, 1], 'cd', label='INPUT 1')
    plt.plot(time_steps, data_series[:, 2], 'md', label='INPUT 2')
    plt.suptitle(model_str)
    plt.title(f"Simple Linear Model (R**2={100*r2:.2f}%)")

    ax.text(1.05, 0.5,
            model_str,
            rotation=90, size=7, weight='bold',
            ha='left', va='center', transform=ax.transAxes)

    ax.text(0.01, 0.01,
            debug_prefix(),
            color='black',
            weight='bold',
            size=6,
            transform=ax.transAxes)

    ax.text(0.01, 0.03,
            time.ctime(),
            color='black',
            weight='bold',
            size=6,
            transform=ax.transAxes)

    plt.xlabel("YEAR FRACTION")
    plt.ylabel("OUTPUT")
    plt.legend(fontsize=8)
    # add major grid lines
    plt.grid()
    plt.show()

    image_file = "test_fit_linear_to_time_series.jpg"
    if os.path.isfile(image_file):
        print("WARNING: removing old image file:",
              image_file)
        os.remove(image_file)

    f1.savefig(image_file,
               dpi=150)

    if os.path.isfile(image_file):
        print("Wrote plot image to:",
              image_file)

    # END test_fit_linear_to_time_series()


if __name__ == "__main__":
    # MAIN PROGRAM

    test_fit_linear_to_time_series()  # test linear model fit

    print(debug_prefix(), time.ctime(), "ALL DONE!")

(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] Ukraine and President Biden’s Approval Rating

Russia invaded Ukraine on February 24, 2022, temporarily moving the COVID-19 pandemic, pandemic response, and the huge number of COVID cases and deaths worldwide attributed to the Omicron variant of SARS-COV-2 despite high levels of vaccinations and masking. So far however, President Biden does not appear to have gotten a boost from the rally around the flag/leader effect that, for example, boosted President George W. Bush’s approval ratings dramatically after the September 11, 2001 mass murder incidents, usually described as attacks on the United States. To be sure, so far there has not been a “New Pearl Harbor” such as 9/11 or a cyberattack or other direct attack on the United States blamed on Russia.

Biden’s approval rating continues to drop (March 27, 2022)

Polling data from Gallup, Rasmussen, and a broad sampling of popular polls all show no clear boost given the few probably few percent error rate of the polls, even a small one, from the Ukraine-Russia crisis so far:

https://news.gallup.com/poll/329384/presidential-approval-ratings-joe-biden.aspx
https://www.rasmussenreports.com/public_content/politics/biden_administration/biden_approval_index_history
https://www.pollingreport.com/biden_job.htm

All of the polls show a marked drop in Biden’s approval ratings in July-August of 2021. One cannot be certain of the reasons, of course, but this is when it became clear that the COVID vaccines worked poorly at best and did not prevent infection or transmission in the vaccinated, contrary to prominent super-confident statements by Biden and his administration that the vaccines would prevent infection in the vaccinated (something obviously untrue from the Pfizer and Moderna clinical trials reports which reported some infections in vaccinated trial subjects).

The approval rating plots above were made with data copied from the referenced web sites on March 27, 2022 and plotted in LibreOffice Calc spreadsheet, a free open-source spreadsheet program similar to Excel, loosely an Excel “clone” although there are some differences.

(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).

[Book Review] A Plague Upon Our House by Scott Atlas

A Plague Upon Our House: My Fight at the Trump White House to Stop COVID from Destroying America

Scott W. Atlas, M.D.

Post Hill Press, New York, 2021

A Plague Upon Our House is Scott Atlas’s personal account of his four months (“end of July” 2020 — December 1, 2020) as a special adviser to the White House on the COVID-19 pandemic and pandemic response. It also discusses some of his interactions with and advice to Florida state governor Ron De Santis. The book paints an alarming picture of key medical advisers Tony Fauci, Deborah Birx, and Robert Redfield as incompetent, highly political, dishonest career bureaucrats in the worst sense of the words and President Trump as a disengaged, indecisive, publicity obsessed leader quite far from his abrasive, tough, “You’re fired!” public image. Although Dr. Atlas avoids using names in several cases, a number of President’s Trump staff come across as young, inexperienced, perhaps well-meaning but clearly out of their depth.

Dr. Atlas and his colleagues had some success influencing the actual policies in Florida, although according to the book Governor DeSantis seems to have largely made up his own mind from reading the direct scientific literature and studying the actual data, using experts like Dr. Atlas, Harvard epidemiologist Martin Kulldorf, Oxford epidemiologist Sunetra Gupta, and his Stanford colleague Jay Battacharya as a sounding board to check his understanding. Although Dr. Atlas describes a few successes at the federal level, he had no real success at the national, federal level or with most states and state governors. While he is highly critical of the medical advisers Fauci/Birx/Redfield and even President Trump who hired him, he performs little analysis of his own failings — a trait most of us share.

Missing References and Data

The book does not have end notes or footnotes with primary references, despite Dr. Atlas’s background as an academic scholar and the highly technical nature of the issues. He presumably has a list of key references that he used as policy adviser to the White House that could have easily been incorporated into the book. He makes the point repeatedly during the book that he spent every day reviewing the latest research papers and COVID data, whereas the Fauci/Birx/Redfield “troika” seemingly did not.

The lack of primary references (or any references at all) is a serious drawback because Google and other online searches, even with the less censored DuckDuckGo search engine, often turn up hysterical mainstream news articles and “Fact Checks” rather than the primary references. Readers who have followed the technical literature and non-mainstream arguments closely will not find anything new technically in the book, but others who rely on the mainstream media likely will find contradicting claims and “information” using a search engine, even DuckDuckGo.

Dr. Atlas’s Wikipedia page (Jan 26, 2022), for example, is highly negative:

Wikipedia entry for Scott Atlas (Jan. 26, 2022)

The ineffectiveness of masks is the only technical issue out of many in the book where Atlas presents actual data in plots on pages 287-294 showing no effect from mask mandates on daily news cases in many different countries and US states (yes, theoretically mask mandates could fail although masks work and Atlas does not address this counter-argument). He does not provide primary references even for these plots.

There was and is a substantial scientific literature showing masks, especially the cloth masks, are largely or completely ineffective or even harmful. Atlas provides no references to this literature. Given the remarkable contradiction on masks and other topics between most mainstream sources such as Wikipedia and highly visible statements on cable and broadcast “news” shows by ostensible experts such as Dr. Fauci and Atlas’s claims in the book, Atlas clearly should provide primary references for incredulous readers.

Although there is much discussion of masks in the book including assertions that SARS-COV-2 is airborne, that is it floats in the air like tiny smoke or dust particles and does not drop to surfaces under gravity as larger droplets of saliva would, Dr. Atlas fails to provide references or adequately describe this key technical issue.

In particular, observers have suspected that plagues, especially respiratory illnesses, were airborne since ancient times. Mask wearing has frequently failed during historical epidemics such as the 1918 influenza epidemic. “Public health” authorities have either ignored the historical failure or rationalized it away.

In the 1950’s and 1960’s a team of researchers led by Richard Riley at Johns Hopkins University conducted an extensive series of experiments at a VA hospital showing that tuberculosis, a bacteria which is much larger and heavier than the influenza virus or the coronaviruses, is airborne. They did this by connecting tuberculosis (TB) wards at the hospital to rooms with guinea pigs via air ducts, otherwise isolating the guinea pigs from possible sources of infection, and placing ultraviolet (UV) sterilizing lights in one duct to one room of guinea pigs. The guinea pigs in the room with the UV lights in the connecting duct did not contract TB whereas the guinea pigs in the other room with no sterilizing UV light in the connecting duct did contract TB.

Although it is now “generally accepted” that TB is airborne, “public health” authorities have continued to claim all other respiratory illnesses are transmitted by large saliva droplets that fall to the ground and other surfaces quickly under gravity. This claim was used to justify the “social distancing,” mask wearing, lockdowns, and other policies that have clearly failed to contain COVID and yet continue.

Magically, perhaps as citizens began to realize that heavy droplet transmission would imply rapid efficient spread through groceries at giant stores such as Safeway, Walmart, and Target that were allowed stay open while smaller competitors serving small local regions were closed, the US CDC flipped and claimed “fomites,” meaning those saliva droplets landing on grocery products, store shelves, etc. did not spread the disease — “just kidding.” More precisely they began to claim the surface transmission was so minor that cleaning with various chemicals was no longer recommended.

Remarkably, the “public health” authorities appear to have never replicated the Johns Hopkins TB study or performed similar studies for influenza or coronaviruses despite multi-billion dollar CDC and NIH budgets and continual publicly stated concern about repeats of the 1918 “flu” pandemic.

The practical consequences of airborne transmission are profound. Masks are not expected to work as the tiny viral particles, about 1/500th the width of a human hair, will flow with the air through even microscopic holes in masks, and with the air around the masks. Confining large numbers of people to apartment complexes with interior hallways or shared ventilation is likely to rapidly spread any respiratory disease. People confined in the same house are all likely to be exposed to the virus. Herding everyone into a few small enclosed giant “Big Box” stores such as Walmart or Safeway is likely to provide an efficient route for rapid spread of the disease. Social distancing is likely to be mostly ineffective especially indoors as the viral particles diffuse through any enclosed space.

Curiously, A Plague Upon Our House, despite correctly pointing out the failure of the masks, does not delve into the key issue of airborne transmission and its implications.

President Donald Trump (Official White House Photo)
President Donald Trump

Trump: Indecisive or Disingenuous?

Although most of Atlas’s criticism is directed at the seemingly incompetent “troika” of Anthony Fauci, Deborah Birx, and Robert Redfield, he is also highly critical of Trump who in fact supported the policies promoted by the troika, despite occasional tweets seemingly to the contrary. Atlas describes Trump as friendly to him and always agreeing with Atlas in private conversations. Atlas is mystified by Trump’s failure to act on his stated beliefs as shared with Atlas, blaming this both on the troika and various Trump advisers rather than Trump personally. Atlas either failed to consider Trump might be pretending to agree with him or chose not to discuss that possibility in his book.

President Trump is a highly successful businessman at one point deeply involved in the rough and tumble casino industry — even taking over Resorts International, a notorious company with a scandalous past. He has a long history of close personal and business “relationships” with murky, rather iffy characters such as the late attorney Roy Cohn, the late singer Michael Jackson, and the late “hedge fund billionaire” without an actual hedge fund Jeffrey Epstein amongst others. That Trump might be something less than straightforward with Atlas or others does not seem improbable.

The actual voting base of Trump is not Pfizer or Bill Gates/Moderna or other giant companies like Walmart or Safeway, it is small business owners, farmers, and large numbers of working class Americans, many employed by small businesses. Atlas briefly notes that Trump’s FDA commissioner Scott Gottlieb took a lucrative position as a member of the board of directors of Pfizer. Atlas does say he disagreed with Gottlieb strongly, but claims he did not attribute Gottlieb’s positions to a conflict of interest with Pfizer. Preteritio?

President Trump had expressed some vaccine skepticism during the 2016 campaign, even suggesting that vaccines might play a causal role in the dramatic increase in autism in the United States over the last three decades. He apparently did reach out to Democrat vaccine skeptic Robert F. Kennedy Jr to pursue a detailed audit of the seemingly confidential public health databases used by CDC and other agencies to clarify the situation.

Robert F. Kennedy Jr. attributes the sudden reversal on this project during the transition period to a large contribution to the Trump inauguration fund by Pfizer. According to Federal Election Commission (FEC) filings, Pfizer donated $1 million dollars to the 58th Presidential Inaugural Committee on December 22, 2016 (see page 163 of the linked 510 page FEC document).

Pfizer has a long history of criminal activities, including an international conspiracy in the 1990s with Archer Daniels Midland and several other companies to fix the price of citric acid (a key ingredient of Coca-Cola amongst other products), lysine, and possibly other food additives.

The lockdown policies have been disastrous for these more ordinary Americans while enriching Amazon and other Big Tech companies, giant retailers such as Walmart and Safeway, and indirectly the vaccine makers like Pfizer. Unrest and opposition to the policies surfaced quickly among President Trump’s base, many of whom, contrary to the Democrat picture of mesmerized cultists, are wary of the flamboyantly sleazy casino magnate turned champion of the common man.

It is not uncommon for politicians to pay lip service to the opinions and policies preferred by their voters while in fact enacting the policies preferred by their campaign contributors and business partners.

Lack of Criticism of Operation Warp Speed

A major weakness of the book is Dr. Atlas’s unreserved enthusiasm and endorsement of Operation Warp Speed and the experimental mRNA based vaccines. Although he does not devote much space to this, it is crystal clear in reading the book. He writes approvingly about the policy to exempt the vaccine makers such as Pfizer and Moderna from liability should the vaccines prove harmful.

Dr. Atlas expresses no concern that vaccines based on the SARS-COV-2 spike protein which bonds to the ACE-2 (angiotensin converting enzyme) receptor and appears to severely disrupt the cardiovascular system, causing the often lethal blood clots reported in some COVID patients, might produce similar cardiovascular problems in vaccinated persons, although he does oppose vaccine mandates and the unscientific ignoring of natural immunity in statements by the “public health” authorities, the troika until recently, and others.

According to the book, Dr. Atlas wrote or completed writing the book in August 2021, arguably just before or as the delta wave was starting to smash through the vaccines, hospitalizing and killing large numbers of Americans if you believe the CDC’s Fluview web site and underlying data as reported.

Vaccine Failure: The COVID-19 “Delta” Wave in August 2021 (about Week 36 of 2021) (US CDC FluView Web Site)

Dr. Atlas expresses no concern about the short circuiting of numerous safety precautions that usually take years to approve a vaccine. Operation Warp Speed met its hyper-aggressive schedule as Atlas proudly claims only by disregarding established safety measures which he does not mention.

In some contexts, usually when making excuses for obvious failures or huge cost and schedule overruns which are common in R&D, scientists such as Dr. Atlas often claim an 80 to 90 percent failure rate for scientific research. For every Manhattan Project that succeeded there are dozens of tokamak fusion power programs, wars on cancer, and so on that have failed. Yet, Dr. Atlas expresses no concerns or fallback plans for the likely failure of Operation Warp Speed if the 80 to 90 percent failure rate is true.

Lack of Self Criticism

Although Dr. Atlas is highly critical of the troika and even President Trump, he does not take himself to task even though he clearly failed to achieve the policies he recommended, citing only a few small successes. This is something of relevance to all of us who seek better policies and to end the irrational hysteria about the COVID pandemic.

Dr. Atlas does claim he was naive about the political process and how politicized the COVID response was. He also is appalled by the censorship, propaganda, and extensive lying or at least false statements by the mainstream media, both “legacy” operations such as the New York Times and social media giants such as YouTube (owned by Google/Alphabet). Other than being outraged, he makes little effort in the book to analyze how and why this is happening and how to successfully combat it.

One may wonder about these claims of naivete given that Dr. Atlas is a fellow at the controversial, mostly conservative Hoover Institution at Stanford University where he has been a health policy analyst for years. The Hoover Institution as a whole is no stranger to bitter partisan political battles.

In any case, Dr. Atlas makes no attempt to understand the crazed “no lie is too big,” “no number of COVID deaths is too many” to GET TRUMP AT ANY COST mentality of the mass media and many others. Indeed this behavior is puzzling and alarming, leading naturally both to psychological explanations such as the “mass formation” theory proposed by Professor Mattias Desmet and grand “conspiracy theories” of varying degrees of seeming plausibility such as those about Klaus Schwab, the World Economic Forum, and the “Great Reset.”

The Collective Fight or Flight Response

Certainly, whether by design (conspiracy) or accident or a mixture of both, we are experiencing a collective fight or flight response on a nearly global scale, a natural response in ancient times when your tribe is attacked by another tribe, in which masks and vaccination are shibboleths to identify friend and foe, like the phrase “lollapalooza” reputedly used by American soldiers in the Pacific during World War II to separate native English speaking US soldiers from Japanese soldiers especially at night.

The collective fight or flight response is the same response that caused massacres of alleged witches, vagabonds, Jews, and others accused of poisoning wells during the Black Death. The fight or flight response is instinctual, overriding higher cognitive functions. High intelligence and formal education does not prevent it or increase one’s ability to shut it off when it is unwarranted — an overreaction for example. It is hard coded into all or nearly all human beings: rich and poor, ignorant and highly educated, dumb and super-smart.

Social conformity, hostility to dissent, censorship, and propaganda all increase markedly during historical episodes of the collective fight or flight response such as during both World Wars. Because of the demonic imagery associated with the Nazis and the Holocaust, it is easier to see the irrationality of the response in World War I than World War II. It is however an irrational, instinctual response rooted in a direct physical attack by a rival tribe in ancient times, not adapted to even modern wars let alone pandemics.

Trump as the American Hitler

Regardless of the motives of any elite conspiracy, Donald Trump’s surprise victory in 2016 caused a collective fight or flight response based on a deep seated belief in and fear of an “American Hitler,” among many Americans, disproportionately liberal Democrats, an insane, evil demagogue who would capture the votes of hillbillies and other stigmatized mostly poor rural whites and lead the US and the World to destruction.

This frightening archetype is common in US popular culture and serious scholarship, dating back at least to the fears of and historical reactions to William Jennings Bryan and most importantly the crusade against Louisiana Senator Huey Long culminating in Long’s assassination in September of 1935, painting Long as the US equivalent of Hitler.

If you believe Trump is the American Hitler, a secular liberal progressive equivalent of the Antichrist figure of the Book of Revelations, then anything is justified to stop him: lying, cheating, stealing, even advocating policies that will in fact increase deaths during a pandemic to undermine him, particularly given the spectre of global thermonuclear war even though the “American Hitler,” Trump, shows a marked antipathy to global thermonuclear war.

Completely irrational behavior such as trying to stop efforts to make deals with Russia and North Korea becomes justifiable if Trump is the American Hitler. Even loopy ideas like provoking a conflict with nuclear armed super-power Russia over Ukraine (currently in the news) to rally the public and prevent Trump or a Trump successor from returning in the mid-term elections may seem reasonable to otherwise intelligent people. Keep in mind if the Trump Republicans were to gain a super-majority in both houses in the 2022 election, they could in principle invalidate the contested 2020 election and restore Trump in 2022 instead of 2024.

Regardless of how unjustified and perhaps unhinged such beliefs about Trump, potential Trump replacements such as Governor De Santis or Senator Rand Paul, or Trump voters actually are, Dr. Atlas, Trump himself, and others have failed to allay them. Once a group of people or a single person is in the instinctual fight or flight response, reason usually fails until they calm down. It is incumbent upon Dr. Atlas to assess why he failed and how to succeed. This is a major weakness of the book.

Conclusion

A Plague Upon Our House paints an alarming portrait of the US government, President Trump and especially the “troika” of Anthony Fauci, Deborah Birx, and Robert Redfield and their many allies — something of continuing concern given Fauci’s current role in the Biden administration. It is filled with anecdotes suggesting Fauci is either incompetent or something worse.

The book would be stronger and more helpful in the continuing crisis if Dr. Atlas carefully evaluated the reasons for his failure to stop the disastrous policies, inability to reach the public and allay the concerns of frightened “never Trumpers,” and how to succeed now in the continuing crisis.

(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] Known Problems with US CDC’s Pneumonia, Influenza, and COVID-19 (PIC) Death Numbers

Introduction

This is a summary (below) of our lengthy (about 13,000 word) paper on the many issues with the CDC’s pneumonia, influenza, and COVID-19 (PIC) death numbers. It is about 1,000 words in length (5-10 minute read) and summarizes most of our key findings.

John F. McGowan, Ph.D., Tam Hunt, Josh Mitteldorf, PhD. Improving CDC Data Practices Recommendations for Improving the United States Centers for Disease Control (CDC) Data Practices for Pneumonia, Influenza, and COVID-19 (v 1.1). Authorea. November 29, 2021. DOI:10.22541/au.163822197.79126460/v1
(https://doi.org/10.22541/au.163822197.79126460/v1)

URL:
https://www.authorea.com/users/425106/articles/547336-improving-cdc-data-practices-recommendations-for-improving-the-united-states-centers-for-disease-control-cdc-data-practices-for-pneumonia-influenza-and-covid-19-v-1-1

Key Points/Summary

A number of CDC data presentation and statistical practices since the start of the COVID-19 pandemic in early 2020 have not followed common scientific and engineering practice. Several problems with data presentation and analyses for pneumonia and influenza death numbers – which have been merged with COVID-19 death numbers in the FluView web site ‒ predate the pandemic.

Before the pandemic (March 2020), the non-standard data presentation and statistical practices appear to increase the number of deaths attributed to the influenza virus and imply the death counts are certain whereas substantial uncertainty exists due to uncertainty in the assignment of the cause of death and other reasons. Since the pandemic, these practices appear to do the same for SARS-COV-2 and COVID-19.

Remarkably, the CDC had at least three (3) different numbers for deaths attributed to pneumonia and influenza before 2020: the leading causes of death report count with about two (2) percent of deaths (about 55,000) per year attributed to influenza and pneumonia, the influenza virus deaths model with about 55,000 deaths per year attributed specifically to the influenza virus, and the FluView web site count with about 6-8 percent of deaths (about 188,000) per year attributed to pneumonia and influenza.

The FluView number differs from the other two death numbers by a factor of OVER THREE. The probable reason for this difference is that — according to the FluView technical notes — FluView counts deaths where pneumonia or influenza is listed as “a cause of death” whereas the leading cause of death report — according to the technical notes — counts only deaths where pneumonia or influenza is listed as “the underlying cause of death.” This probably reflects a large uncertainty in the assignment of the cause of death in respiratory illness cases; indeed the underlying cause of death may be ill-defined in many cases.

The CDC’s excess deaths estimates on their excess deaths web site does not report any standard goodness of fit statistics, notably the coefficient of determination often known as “R squared” and the “chi squared” goodness of fit statistic. Our analysis shows that different models with the same goodness of fit statistics give different estimates of the number of excess deaths, varying by up to 200,000 deaths in 2020. The CDC web site does not report this systematic modeling error.

The CDC appears to have chosen a set of parameters for the Noufaily/Farrington algorithm used to estimate excess deaths by the CDC that gives a lower “R Squared” value for goodness of fit than other choices and a HIGHER ESTIMATE of excess deaths — whereas common scientific and engineering practice would be to use the models with the best goodness of fit statistics, the “R Squared” closest to 1.0.

The Noufailly/Farrington algorithm is an empirical trend detection and extrapolation model theoretically incapable of accurately modeling the aging “baby boom” population which would be expected to produce “excess deaths” in recent years — nor is it able to explain the puzzling near stop of the increase in deaths per year reported in the immediate pre-pandemic years 2017-2019 despite the aging population.

The CDC does not publish (as of Dec 2021) years of life lost (YLL) estimates which include increases in suicides, homicides, and other adverse effects of the lockdowns, nor systematic modeling errors on the YLL estimates. YLL can illustrate the difference between a disease that largely kills those nearing death anyway versus a disease that easily kills the healthy.

The CDC issued a COVID death certificate guidance document in April of 2020 that appears to change the standards for assigning the underlying cause of death (UCOD) from the pre-pandemic practice for assigning the underlying cause of death for pneumonia and influenza, making COVID-19 the underlying cause of death in the many cases where the person who died had serious pre-existing conditions such as chronic bronchitis, emphysema, heart failure etc.. — the deaths counted in FluView but not in the leading causes of death report. There does not appear to have been any public comment on this guidance document to date.

In general the CDC does not report statistical errors, systematic errors, or estimates of biases in pneumonia, influenza, and COVID-19 death numbers. They do not report any monitoring of the effect of their guidance documents or other directives on the assignment of the cause of death by doctors, medical examiners, and others.

These issues are sometimes shared with other government agencies such as the US Social Security Administration (SSA) and US Census Bureau that work closely with the CDC.

Death counts for both individual causes and “all cause” deaths are frequently reported as precise to the last digit without any statistical or systematic errors, despite both known and unknown uncertainties in counting deaths, such as missing persons, unreported deaths due to deceased payee fraud, the ~1,000 living Americans incorrectly added to the government Deaths Master File (DMF), each month, for unknown reasons, and considerable uncertainties in assigning the underlying cause of death (UCOD) by coroners and doctors.

Similarly, raw counts, adjusted counts, and estimates – often based on incompletely documented computer mathematical models – are often not clearly identified as such. The Deaths Master File, with names and dates of death of deceased persons is exempt from the Freedom of Information Act (FOIA) and unavailable to the general public, independent researchers, and even other government agencies such as the IRS. This confidentiality of data makes independent verification of many CDC numbers, such as the excess deaths numbers tracked during the COVID-19 pandemic, all but impossible.

This omission of common scientific and engineering practices raises questions about the accuracy of the CDC’s data, conclusions, and public health policies in a number of important areas, including the COVID-19 pandemic.

The non-standard data presentation and statistical practices appear to increase the number of deaths attributed to the influenza virus and imply the death counts are certain whereas substantial uncertainty exists due to uncertainty in the assignment of the cause of death and other causes. Since the pandemic, these practices appear to do the same for SARS-COV-2 and COVID-19.

END OF SUMMARY

(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 Programmer Shortage: How easy is it to get a programming job?

Other Video Links: BitChute NewTube ARCHIVE

The Programmer Shortage: How easy is it to get a programming job?

Qualified software engineers, managers, marketers and salespeople in Silicon Valley can rack up dozens of high-paying, high-upside job offers any time they want, while national unemployment and underemployment is sky high.

Marc Andreessen (founder of Netscape, cofounder and general partner at the venture capital firm Andreessen Horowitz)

References:

Marc Andreesen Shortage Quote: https://www.brainyquote.com/quotes/marc_andreessen_419343
My Article: http://wordpress.jmcgowan.com/wp/microsoft-layoffs-and-stem-shortage-claims-2009-2017/
Bill Gates Shortage Testimony (2008) https://news.microsoft.com/2008/03/12/bill-gates-testimony-before-the-committee-on-science-and-technology-u-s-house-of-representatives/#f5D7jgGUmeDSBtPe.97
EPI Report on STEM Shortages: https://www.epi.org/publication/pm195-stem-labor-shortages-microsoft-report-distorts/
Cracking the Coding Interview Book: https://www.crackingthecodinginterview.com/
Blog Post on the 10X Programmer: https://www.simplethread.com/the-10x-programmer-myth/
The Leprechauns of Software Engineering: https://leanpub.com/leprechauns
Recent July 2021 ACM Developer Software Shortage Article: https://cacm.acm.org/magazines/2021/7/253461-the-2021-software-developer-shortage-is-coming/fulltext

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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] Quantum Mysticism: Can you make your life better just by wishing hard enough?

Short video on Quantum Mysticism such as the 2004 movie What the Bleep Do We Know and how these compare with the actual theory of Quantum Mechanics in physics.

Other Free Speech Video: BitChute NewTube ARCHIVE Rumble

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Censored Search: https://censored-search.com/
A search engine for censored Internet content. Find the answers to your problems censored by advertisers and other powerful interests!

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BitChute (Video): https://www.bitchute.com/channel/HGgoa2H3WDac/
Brighteon (Video): https://www.brighteon.com/channels/mathsoft
Odysee (Video): https://odysee.com/@MathematicalSoftware:5
NewTube (Video): https://newtube.app/user/mathsoft
Minds (Video): https://www.minds.com/math_methods/
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Archive (Video): https://archive.org/details/@mathsoft

(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] Flat Earth? How to tell the Earth is curved from personal experience

Flat Earth Title Slide

Short video on how to tell the Earth is curved from direct personal experience.

About Us:

Main Web Site: https://mathematical-software.com/
Censored Search: https://censored-search.com/
A search engine for censored Internet content. Find the answers to your problems censored by advertisers and other powerful interests!

Subscribe to our free Weekly Newsletter for articles and videos on practical mathematics, Internet Censorship, ways to fight back against censorship, and other topics by sending an email to: subscribe [at] mathematical-software.com

Avoid Internet Censorship by Subscribing to Our RSS News Feed: http://wordpress.jmcgowan.com/wp/feed/

Legal Disclaimers: http://wordpress.jmcgowan.com/wp/legal/

Support Us:
PATREON: https://www.patreon.com/mathsoft
SubscribeStar: https://www.subscribestar.com/mathsoft

BitChute (Video): https://www.bitchute.com/channel/HGgoa2H3WDac/
Brighteon (Video): https://www.brighteon.com/channels/mathsoft
Odysee (Video): https://odysee.com/@MathematicalSoftware:5
NewTube (Video): https://newtube.app/user/mathsoft
Minds (Video): https://www.minds.com/math_methods/
Locals (Video): https://mathematicalsoftware.locals.com/
Archive (Video): https://archive.org/details/@mathsoft

(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] Improved Way to Find and Evaluate Censored Internet Content

Screenshot of Censored Search Web Site
https://odysee.com/@MathematicalSoftware:5/improved-way-to-find-and-evaluate-censored-internet-content:3

Other Video: BitChute ARCHIVE

A short video announcement for the Censored Search 2.1 web site and service — find the products and services that cost less, work better, and preserve your liberty that Big Tech and Big Pharma are censoring and shadow-banning!

Censored Search 2.1 Announcement Script

We are witnessing unprecedented censorship of competing products and services by Big Pharma and other advertisers that fund Google, Facebook, Twitter and other Internet near monopolies, aided and abetted by politicians in both political parties and across the supposed political spectrum who depend on these giants for campaign contributions and cushy jobs after they leave government service. This unholy alliance is pushing inadequately tested extremely expensive patented drugs and purported vaccines that fail in a matter of months at best, and intrusive surveillance technology products such as vaccine passports beyond the dystopian nightmares of George Orwell in 1984, Aldous Huxley in Brave New World, and Ray Bradbury in Fahrenheight 451.

How do you find the products and services that cost less, work better, and preserve your liberty when Google and other advertising funded search engines censor and shadow ban any products or services competing with this unholy alliance of monopolies, politicians, and the secret police?

Demo searches for “ivermectin,” “vitamin D,” and “air purifier.”

NOTE This is a link to a popular article on the airborne transmission of tuberculosis study at Johns Hopkins that I mentioned:

https://publichealth.jhu.edu/2020/the-experiment-that-proved-airborne-disease-transmission

Our censored search web site and service enables you to search censored and shadow banned web sites for suppressed information. We offer transparency on what the search ranking algorithms are doing and tools to help you separate fact from disinformation. We offer both a free service for everyone and a paid professional service with full access to our tools and the ability to customize the search algorithms for your needs. Our business model is end user funded to avoid either direct control or subconscious bias from advertisers.

Our censored search service is intended as a complement to increasingly censored advertising funded search engines such as Google, Yahoo, Bing, and even DuckDuckGo which appears to be increasingly shadow banning alternative content. We cannot duplicate many useful features of the censored search engines yet, nor is this needed. Use our search engine for censored and shadow-banned content — get the other side or sides of the story. Remember there are often more than two sides to a story!

Inclusion in our search engine is not an endorsement. We include sites based on evidence of censorship or shadow banning in our judgment. We attempt to be neutral and provide tools to our users to evaluate and verify the content without relying on our fallible judgment. There is evidence that powerful interests actively spread disinformation to alternative sites to make identifying suppressed factual information difficult and discredit factual information through guilt by association. We are developing tools to fight these active disinformation tactics.

We have made a number of improvements to our service since our Censored Search 2.0 release last month. We have added the popular libertarian site LewRockwell.com which reports being demonetized, cut off from advertising revenues by Google. We also added Julius Ruechel who has written some detailed analyses of the COVID pandemic and response. The list of supported web sites in now ranked by crawl date, most recent first, to enable users to quickly tell what is new. We have integrated the WordNet dictionary to automatically provide definitions of words and phrases in the dictionary as well as help recognize mispelled search words and phrases.

Bill Gates WORDNET dictionary demo.

What is coming? We make continuous improvements to the service. Our main current goal is improving the search algorithms and user tools to better find and evaluate factual information that has been suppressed in an independently verifiable way. You should not have to trust us or the web sites.

Give us a try at censored-search.com We welcome constructive feedback. How can we serve you better? Bookmark our site as the censorship is growing by leaps and bounds. You may need us more in the future! Let your friends and colleagues know. You can access more advanced features and support development of a transparent, verifiable search engine that works for you and NOT giant advertisers such as Big Pharma by becoming a paid subscriber.
If you want to support our development work, subscribe now!

About Us:

Main Web Site: https://mathematical-software.com/
Legal Disclaimers: http://wordpress.jmcgowan.com/wp/legal/

(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).