Why Listen to this video? There are many heavily promoted dangerous misconceptions about modern “science,” many of which I once shared. These misconceptions generally lead to an excessive and dangerous confidence in scientists and claims labeled as science. These can even cost you your life as happened to many arthritis sufferers who trusted scientific claims about the blockbuster painkiller Vioxx. Many other examples exist, some discussed briefly in the following video. I will discuss over a dozen common misconceptions. The discussion reflects my personal experience and research.
Why me? I have a B.S. in Physics from Caltech, a Ph.D. in experimental particle physics from the University of Illinois at Urbana-Champaign, worked for a successful video compression startup in the Silicon Valley, NASA, HP Labs, and Apple.
TOPICS COVERED
Scientists are people too. Rarely the altruistic truth-seekers depicted in fiction and popular science writing. Egos, glory, greed. Comparable to less revered and even actively distrusted professions such as attorneys. Many examples of error and gross misconduct up to the present day: “Tuskegee Study of Untreated Syphilis the Negro Male” by US Public Health Service and US Centers for Disease Control (1932-1972), Eugenics, Vioxx scandal.
In her 2009 article “Drug Companies & Doctors: A Story of Corruption”, published in The New York Review of Books magazine, (former NEJM Editor-in-Chief Marcia) Angell wrote :[7]
…Similar conflicts of interest and biases exist in virtually every field of medicine, particularly those that rely heavily on drugs or devices. It is simply no longer possible to believe much of the clinical research that is published, or to rely on the judgment of trusted physicians or authoritative medical guidelines. I take no pleasure in this conclusion, which I reached slowly and reluctantly over my two decades as an editor of The New England Journal of Medicine.
Moral character and intelligence (IQ, general intelligence) are uncorrelated.
Since World War II most modern science is funded by the government, by giant bureaucratic funding agencies such as the National Institutes of Health, the National Science Foundation, the Department of Energy, and the DoD in the USA. There was a large transformation of science during and after World War II from small scale, often more independent research to huge government programs.
(Video segment from Eisenhower’s Farewell Address on the danger of the scientific technological elite)
The success of the wartime Manhattan Project which developed the first nuclear reactors and atomic bombs appears to have been a fluke. Most New Manhattan Projects have largely or completely failed including several in physics involving the same people or their students.
There is an illusion of independence in scientists because so many are directly employed by universities such as Harvard, Stanford, Caltech and others, but those universities depend mostly on government funding. High profile academic dissidents such as linguist Noam Chomsky usually stay well away from truly taboo topics often labeled as “conspiracy theories,” e.g. the Kennedy Assassination, “pseudoscience,” or both.
The federally funded academic research system is a pyramid scheme with many, many more Ph.D.’s produced than long term faculty or staff positions, typically 5-20 times more Ph.D.s. Remarkably, leading scientists and scientific institutions continue to claim terrible shortages of scientists despite this. A never ending supply of young, cheap, often starry-eyed workers — graduate students and post-docs.
A well-paid but precarious elite of tenured faculty, principal investigators, senior scientists at government labs who can easily be replaced by a tiny fraction of the younger Ph.D’s if they rock the boat.
Brilliant, well-educated, hard working people sometimes do dumb things, both individually and collectively.
Knowledge of cognitive biases such as “confirmation bias” or “cognitive dissonance” does not immunize people from the biases.
Brilliant, well-educated, hard working people are often better at rationalizing away obviously contradictory evidence or logic and convincing others to accept their rationalizations. Paradoxically knowledge of cognitive biases provides an arsenal of excuses to rationalize away the evidence or logic.
The heavily promoted popular concept of “falsifiability,” usually attributed to Karl Popper, does not work in practice. Scientists can usually (not always) find technically plausible, sophisticated “explanations” for supposedly falsifying evidence. A double standard that sets an impossible obstacle for deprecated views.
The scientific uncertainty excuse. Scientists often make confident statements claiming or implying no or negligible uncertainty. When the statement proves wrong, they will ridicule critics by claiming science is tentative, an ever evolving process, there is an 80-90% failure rate in science, there is uncertainty they never mentioned and by implication everyone should know that. Once the criticism is beaten back often by this ridicule they revert to more confident statements, sometimes grossly contradicting the previous statement.
Modern scientists make heavy use of complex, error-prone, usually computerized mathematical models and advanced statistical methods that are difficult to reproduce or criticize. These methods are prone to finding small signals that rarely exceed the normal variation of the data when small mistakes are made, whether innocently, due to subconscious bias, or intentionally.
The error rate of top science students in school, college, university, academic settings is very low, possibly zero percent for some top students (800 on SAT, a few top students at Caltech, MIT etc.). BUT this does not translate to real world R&D where failure rates are clearly much higher. Scientists selectively cite a failure rate of 80-90 percent when confronted about obvious falures (cost and schedule overruns, failed cancer breakthroughs etc.)
Prodigies/highly successful scientists (tenured faculty etc.) frequently have unusual family backgrounds such as extremely wealthy, politically connected families or an often prominent academic family. Parents know calculus which is a significant hurdle for most “nerds.” Not like Good Will Hunting or The Big Bang Theory where prodigies are portrayed as working class, poor etc. Purely genetic fluke implied.
“Science” (in scare quotes) is promoted by scientists as a religion or substitute for religion, a comprehensive “rational” worldview demanding fealty and paradoxically irrational “rational” obeisance. Extreme examples include the use of the term “God Particle” for the Higgs particle in particle physics, promoted by the late Nobel Laureate Leon Lederman and others. Carl Sagan’s inaccurate account of the destruction of the Library of Alexandria and murder of Hypatia in Cosmos. Often closely tied to militant atheism and materialism despite the strong use of religious and mystical terms and ideas at the same time. Organized skeptics such as CSI/CSICOP, Michael Shermer and others. Dissenting or differing points of view are labeled as anti-science, conspiracy theories, pseudoscience, denialism and other labels.
Carl Sagan, Neil deGrasse Tyson and Hypatia (Debunked):
Conclusion: I’ve discussed over a dozen major heavily promoted, dangerous misconceptions about “science.” If you find some of these hard to accept, perform your own research. I have numerous articles on the false scientist shortage claims, also known as STEM shortage claims, on my web site. I also have articles on the Manhattan Project as a fluke and the Myth of Falsifiability. I will likely post more supporting information on the other misconceptions in the future. Most importantly, true science requires thinking carefully and critically for yourself and not treating something labeled “science” as a religion or substitute for religion, either consciously or subconsciously.
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
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).
Banish computers, smartphones and other devices from your bedroom.
Turn off screens at least an hour before going to sleep.
Wait at least one half hour before turning on screens when wake up.
Avoid smartphone use.
Avoid/minimize other mobile device use such as laptops.
Take social media apps and other non-essential apps off smartphone. Turn off all notifications except messages from live friends, family, and colleagues.
Don’t watch fear/outrage cable news, television, and other video sources such as Fox News, CNN, and MSNBC. If something makes you fear, hate, scorn or ridicule some other group, stop watching.
Completely unplug from computers and Internet one day (or more) per week. Exercise, read a physical book, spend time with friends or family. (If possible)
Set aside limited time periods for ‘bad news’ or other difficult topics. Don’t allow them to be 24/7.
Diversify away from centralized advertising funded sources such as Google and Facebook that use opaque algorithms and teams to manipulate search results or social media feeds.
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/
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).
It is rational to suspect a conspiracy in some situations. Conspiracies are difficult to prove and difficult to disprove. Proving a conspiracy usually requires a confession by someone in the conspiracy or evidence such as surveillance audio, video, or documents such as a duplicate set of books in an accounting fraud or a diary. People involved in criminal conspiracies who confess are, almost by definition, unreliable witnesses and can often be called into question because of their admitted moral character.
Disproving a conspiracy is usually very difficult, simply because we generally lack reliable 24/7 surveillance of the alleged conspirators. Specific conspiracy scenarios can be ruled out but it is often impossible to comprehensively rule out all plausible scenarios. Thus, it is often necessary to accept a high level of uncertainty in situations where a conspiracy may be involved; even with a high degree of suspicion, certainty beyond a reasonable doubt is often not possible.
What does historical data show about the frequency of conspiracies and how frequently do they elude discovery? These are some numbers from the US Federal Bureau of Investigation (FBI).
US FBI Homicides by Victim/Offender Situations for 2017 (Screenshot on Feb. 10, 2021)
The FBI reported a total of 15,129 homicides in 2017, the most recent year for which I was able to locate statistics. Of these, 1,781 involved a single victim and multiple offenders — a conspiracy. Three-hundred (300) involved multiple victims and multiple offenders — a conspiracy. Thus, a total of 2,081 or 13.76 percent involved a conspiracy.
Note that the table lists 5,174 (461 + 4,713) or 34.2 percent with an unknown offender or offenders, presumably unsolved. Thus, a substantial number of murder conspiracies avoid detection and prosecution.
The FBI also gives clearance data for 2017 indicating about 38.4 percent of homicides are unsolved. Note that this seems inconsistent with the 34.2 percent number implied by the Homicides by Victim/Offender Situation table above. The meaning of “clearance” is the subject of some controversy and has different definitions in different crime statistics.
US FBI Report on Crime Clearance Rates in 2017 (Screenshot on Feb. 10, 2021)
The history of investigations into organized crime where conspiracies have frequently been proven in a court of law includes numerous unsolved murders and disappearances including such famous cases as the murder of reputed mob boss Arnold Rothstein in 1928, the disappearance of Jimmy Hoffa in 1975, the murder of alleged Chicago mob boss “Sam” Giancana in 1975, and many others.
Thus conspiracies do happen and it is rational to suspect them in some situations. The FBI data indicates conspiracies are involved in about 13.76 percent of murders in the US.
(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).
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).
This article argues that the US Centers for Disease Control (CDC)’s April 2020 guidance for filling out death certificates for possible COVID-19 related deaths strongly encourages, if not requires, assigning COVID-19 as the underlying cause of death (UCOD) in any death where COVID-19 or the SARS-COV-2 virus may be present, which appears to differ from common historical practice for pneumonia and influenza deaths where pneumonia was frequently treated as a “complication,” a cause of death but not the underlying cause of death.
This means the number of COVID deaths should be compared to a count of death certificates where pneumonia and influenza were listed as a cause of death or even a lesser contributing factor, a historical number which appears to have been at least 188,000 per year based on the CDC FluView web site. The proper comparison number may be even larger if deaths that historically were listed as heart attacks, cancer or other causes than pneumonia or influenza are also being reassigned due to the April 2020 guidance.
The CDC has at least three different historical pneumonia and influenza death numbers. These are the leading causes of death report numbers of about 55,000 deaths per year which appears based on death certificates, a poorly documented mathematical/computer model which attributes about 55,000 deaths per year with a large error to the influenza virus, and the FluView web site which attributed about 188,000 deaths per year to pneumonia and influenza.
The leading causes of death historical numbers appear based on the underlying cause of death listed on the death certificate whereas the FluView historical numbers appear based on death certificates that list pneumonia or influenza as a cause of death, in most cases not the underlying cause of death. The historical FluView death numbers appear to be the proper baseline for comparison to COVID-19, although an even larger number is possible if there has been practically significant reassignment of heart attacks and other deaths as well.
This would mean that COVID-19 is less deadly than popular perceptions based frequently on comparisons to “flu death numbers” of about 55,000 per year apparently derived either from the CDC’s leading causes of death report or the influenza virus model.
Note that this is not a claim that COVID-19 or SARS-COV-2 plays no causal role in the deaths: a death “with” COVID rather than a death “due to” COVID in popular debates. Rather, the proper interpretation is probably that COVID-19 acts predominantly as an opportunistic infection instead of an inherently deadly infection able to easily kill healthy young people with a strong immune system. Vaccines are likely to have small or no ability to prevent death from an opportunistic infection since the infected vaccinated person will have a weak or failed immune system with little or no ability to produce the immune response learned from the vaccination.
The language and numbers on the CDC web sites and official reports and documents are remarkably confusing and grossly contradictory in some cases — such as the historical number of deaths attributed to pneumonia and influenza which differs by over a factor of THREE. Error bars or confidence levels on most numbers such as the death numbers are not reported as required by common scientific and engineering practice. These practices have been harshly criticized for years by scientists and medical experts such as Peter Doshi. Consequently it is impossible to make definite statements about the meaning of the numbers and the definitions of measured quantities discussed below.
This is a complex life-and-death subject with many nuances. Each section below expands each key point in detail, discussing the nuances and unknowns. Some facts and arguments are repeated in different sections for clarity.
Three Different Historical Pneumonia and Influenza Death Numbers
The United States Centers for Disease Control (CDC) documents and web site present at least THREE different historical (pre-2020) estimates of deaths from “influenza and pneumonia,” “pneumonia and influenza,” and/or “influenza” or the “flu” presumably meaning the influenza category of viruses. Which of these three death numbers, one of which differs by a factor of OVER THREE from the other two, should be compared to COVID-19 deaths? Indeed, it could well be incorrect to compare any of them to the COVID-19 deaths.
These three pneumonia and influenza death numbers are the value in the annual leading causes of death report — about 55,000 deaths each year, the tables of “pneumonia and influenza” (abbreviated as P&I) from the National Center for Health Statistics (NCSH) used on the FluView web site — about 188,000 deaths per year, OVER THREE TIMES the leading causes of death number, and the output of a poorly documented model of deaths attributed to the influenza category of viruses, a broad range centered at about 55,000 deaths per year.
Deaths attributed to COVID-19 in 2020 have frequently been compared to an estimate of about 55,000, either to the date of the report or for the entire year. The language used is often unclear but appears to refer to either the CDC’s influenza mathematical model or the leading causes of death number, which are similar numbers but technically not the same.
This article argues that the current COVID-19 death numbers are best compared to the larger FluView numbers, although an even larger different number may be appropriate if deaths that would have been attributed to heart attacks, strokes, or other blood coagulation related disorders in the absence of a positive SARS-COV-2 test or diagnosis are included in the current COVID-19 death counts.
The CDC FluView Web Site
The CDC FluView web site shows that six to ten percent of deaths, varying seasonally, are due to pneumonia and influenza (P&I) according to the vertical axis label on the FluViewPneumonia & Influenza Mortality plot. The underlying data files from the National Center for Health Statistics (NCHS) list about 188,000 deaths per year attributed to pneumonia and influenza.
US Centers for Disease Control (CDC) FluView Pneumonia & Influenza Mortality Plot (Dec. 4, 2020)
Note also that deaths attributed to “pneumonia and influenza” on the FluView web site are highly seasonal. A substantial increase over the summer is expected during the winter (or rainy season in Northern California and similar regions), peaking in December and January each year.
Also note that the seasonal variation is sinusoidal — like the oscillation of a pendulum or a mechanical spring. Contrary to popular culture, there is no clear step up when schools open in the fall or step down when schools close for the summer. It looks very much like something driven by the Sun, directly or indirectly by some mechanism or mechanisms. Possible mechanisms include Vitamin D production from sunlight, destruction of viruses and bacteria in the air or on surfaces by ultraviolet light in sunlight, general health benefits of a warm environment, or some other Sun-driven phenomenon.
The Leading Causes of Death Report
In contrast, the CDC’s leading causes of death report Table C, Deaths and percentage of total deaths for the 10 leading causes of death: United States, 2016 and 2017 on Page Nine (see screenshot below) attributes only two percent of annual deaths (about 55,000 in 2017) to “influenza and pneumonia.”
The difference between the CDC FluView and leading causes of death report numbers is probably due to the requirement that pneumonia or influenza be listed as “the underlying cause of death” in the leading causes of death report and only “a cause of death” in the FluView data. This is not clear. Many deaths have multiple “causes of death.” The assignment of an “underlying cause of death” may be quite arbitrary in some cases. Despite this, none of these official numbers either in the leading causes of death report or the FluView web site are reported with error bars or error estimates as required by common scientific and engineering practice when numbers are uncertain.
A longer, more detailed discussion of the FluView, leading causes of death, and the CDC’s influenza virus death model death numbers (yet another number) may be found below. It appears likely the FluView death numbers are defined similarly to current COVID-19 death counts; the reasons for this are explained in some detail.
Which Pneumonia and Influenza Death Numbers Should Be Compared to the COVID-19 Death Numbers?
The FluView and Leading Causes of Death numbers for “pneumonia and influenza” differ by a factor of OVER THREE. Note that both the FluView and Leading Causes of Death numbers have no error bars or error estimates given, implying exact numbers in common scientific and engineering practice. Should the number of COVID-19 deaths be compared to the FluView number or the leading causes of death number or some other number?
The CDC also uses a poorly documented mathematical model that attributes roughly 55,000 deaths from pneumonia and influenza to the influenza virus as the underlying cause, a number roughly comparable to the total pneumonia and influenza deaths in the leading causes of death. The influenza virus is confirmed by laboratory tests in only a small fraction of pneumonia and influenza deaths, about 6,000 per year.
Although the language is often unclear in the CDC documents and web site, the CDC appears to claim that an initial influenza infection which disappears or becomes undetectable in laboratory tests leads to the subsequent pneumonia, presumably a bacterial pneumonia although other viruses would be consistent with some lab tests. Based on this argument, the CDC appears to attribute most pneumonia deaths where historically pneumonia was listed as the “underlying cause of death” to the influenza virus for which there is a flu vaccine that the CDC promotes heavily — even though laboratory tests frequently fail to confirm influenza or even detect other viruses or bacteria instead. The “underlying cause of death” is discussed in more detail below.
As shown in the graphic above, the CDC web site Disease Burden of Influenza (Figure 1) appears to give a range from 12,000 to 61,000 influenza deaths from this model. The graphic does not indicate if this range is a 95 percent confidence interval — a common scientific and engineering practice — or some other error estimate. The range in the graphic does not appear to match any of the 95 percent confidence levels for estimated deaths attributed to influenza in Table 1.
The History of Serious Criticism of the CDC’s “Flu” Death Numbers
There is a long history of serious criticism of the CDC’s “flu” death numbers by medical scientists and others. The most prominent critic is Peter Doshi, currently a professor at the University of Maryland and an associate editor at the British Medical Journal.
Citing the results of actual laboratory tests of deceased patients, critics of the CDC’s flu death numbers such as University of Maryland Professor Peter Doshi have argued that pneumonia deaths are due to a range of different viruses, bacteria, other pathogens, and even toxins, rather than predominantly influenza as implied by the CDC’s influenza deaths model. The output of this model appears to be the basis of the baseline “flu” deaths numbers used in most popular and public policy discussions of COVID-19 deaths — although the leading causes of death report number may also be used.
The Reason for the Large Difference Between the FluView and Leading Causes of Death Numbers
The US CDC documents and web site are frequently unclear and even contradictory as in the case of these grossly contradictory totals of deaths from pneumonia and influenza. This makes it difficult to be certain of the cause for the difference. Nonetheless, the technical notes for each document — FluView and the leading causes of death — give a highly probable reason.
Leading Causes of Death Report for 2017 with “the underlying cause of death” language circled in red. (Dec. 18, 2020)
Death certificates frequently have multiple causes of death. One of these is assigned as the underlying cause of death. This may be quite arbitrary in some cases. Indeed the concept of “underlying cause of death” may not be well defined for some deaths because the aged may develop multiple health problems in parallel that are fatal in combination.
FluView Mortality Surveillance notes with “A Cause of Death” Language Circled in Red (Dec. 18, 2020)
The Rules for Assigning the Underlying Cause of Death Before COVID-19
Prior to 2020 and COVID-19, most pneumonia deaths did not list pneumonia or the pneumonia-causing pathogen if known as the underlying cause of death. This will be discussed in detail below. The only common partial exception was HIV/AIDS where pneumocystis carinii pneumonia (a common fungus) was often the immediate cause of death and the Human Immunodeficiency Virus (HIV) is almost always listed as the underlying cause of death. However, HIV is not the pneumonia-causing pathogen which is the pneumocystis fungus. Instead, most pneumonia deaths, those included in the FluView numbers but not included in the leading causes of death numbers, were attributed to a cause such as a chronic lower respiratory disease, heart disease, cancer, even accidents, and other usually pre-existing conditions as the underlying cause of death.
The CDC follows the World Health Organization (WHO)’s definition of the underlying cause of death. WHO defines the underlying cause of death as “the disease or injury which initiated the train of morbid events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury” in accordance with the rules of the International Classification of Diseases (ICD).
In the United States, the underlying cause of death is listed last in the list of causes of death in part I of the death certificate. The immediate cause of death is listed first. Part 2 lists other conditions that are considered contributing factors but somehow not causes. Pneumonia is often the immediate cause of death in part 1 of the death certificate.
In principle, death certificates and the assignment of causes of death including the underlying cause of death is governed or at least guided by the CDC’s Medical Examiners’ and Coroners’ Handbook on Death Registration and Fetal Death Reporting (2003 Revision). This one-hundred and thirty-eight (138) page manual actually provides very limited guidance on how to assign the underlying cause of death in cases where pneumonia is present. Page twenty-five (25) has the only detailed discussion of deaths involving pneumonia:
US CDC Medical Examiners’ Handbook 2003 suggests attributing a pneumonia death to either chronic ischemic heart disease or diabetes mellitus as the underlying cause but not the pneumonia or pneumonia-causingpathogen. No clear direction to the physician.
Pneumonia is often a nonspecific process that occurs as the terminal event in someone who dies of a more specific underlying cause of death, such as congestive heart failure resulting from ischemic heart disease. In such cases, the specific underlying cause of death should be included in the cause-of-death statement.
Pneumonia is often designated as either community acquired or hospital or institution acquired (nosocomial). If the community- or institution-acquired nature of the pneumonia is known, the cause-of-death statement should include an indication of which one applies.
The specific bacterial, viral, or other infectious agent, if known, should be cited in the cause-of-death statement.
Relevant risk factors should also be cited in the cause-of-death statement, as might occur in an alcoholic who develops tuberculous pneumonia. Only in those instances where pneumonia has caused death and there is no known underlying cause or risk factor should the underlying cause of death be stated as “Pneumonia,” being sure to specify the infectious agent, if known, or specifying that a specific etiology is unknown, if such is the case.
Emphasis Added
And on page 113 of Cause of Death and the Death Certificate by Randy Hanzlick, dementia, cerebrovascular disease, cardiac disease, and lung disease are all listed as common underlying causes of death in cases of deaths due to pneumonia:
Thus, traditionally, pre-2020 and COVID-19, pneumonia deaths were frequently assigned a non-pneumonia underlying cause of death, usually a pre-existing condition and not the pneumonia-causing pathogen such as the influenza virus or SARS-COV-2, in common medical practice. Based on the technical notes these pneumonia and influenza deaths would be included in the FluView death numbers but not in the leading causes of death report.
Comparing COVID-19 Death Numbers to the Pneumonia and Influenza Death Numbers and Estimates from Previous Years
As shown above, the CDC has at least three (3) different pneumonia and influenza death numbers and estimates: the Leading Causes of Death Report (about 55,000 deaths per year, about two percent of annual deaths from all causes), the FluView graph and underlying data from the NCHS (about 188,000 deaths per year, six to ten percent of annual deaths from all causes, before 2020), and the influenza death model estimates that range from 12,000 to 61,000 deaths per year with the best estimate close to the number of pneumonia and influenza deaths in the leading causes of death report. Are any of these the proper baseline for comparing COVID-19 deaths to prior years or should some other number or estimate be used?
In the absence of the RT-PCR and antibody tests for the SARS-COV-2 virus, most COVID-19 deaths would have been unexplained pneumonia deaths lacking a laboratory test confirming influenza or other known pathogen. Possibly, some COVID-19 deaths would have been listed as heart attacks or strokes, those COVID-19 deaths attributed to the blood clots and other blood-related anomalies currently blamed on COVID-19, or even some other causes. The rest of this article will focus on the pneumonia deaths which would probably comprise most of the COVID-19 deaths in the absence of laboratory tests.
The US CDC’s April 2020 guidelines for reporting COVID-19 deaths clearly direct physicians and others not to list chronic obstructive pulmonary disease (COPD) as the underlying cause of death in COVID-19 cases, moving it to Part 2 of the death certificate reserved for “non-cause” contributing factors, which differs dramatically from medical practice prior to 2020 as described in Randy Hanzlick’s book and implicit in the FluView pneumonia and influenza deaths data.
In some cases, survival from COVID–19 can be complicated by pre-existing chronic conditions, especially those that result in diminished lung capacity, such as chronic obstructive pulmonary disease (COPD) or asthma. These medical conditions do not cause COVID–19, but can increase the risk of contracting a respiratory infection and death, so these conditions should be reported in Part II and not in Part I.
This guidance also gives a specific example of a COVID-19 death with COPD relegated to Part 2:
COVID-19 Death Guidance Example with COPD as Contributing Factor Only
Although other causes of death that are often given as the underlying cause of death in pneumonia cases on pre-2020 death certificates are not explicitly identified in the April 2020 guidance document, it seems probable most physicians would move these pre-existing conditions to Part 2 and not list them as the underlying cause of death for COVID-19 based on the April 2020 CDC guidance document. Note that COPD would fall under the category “lung disease” in the list below from Randy Hanzlick’s Causes of Death and the Death Certificate:
Thus, COVID-19 deaths since the April 2020 guidance are probably roughly comparable to the FluView deaths, the larger number, the 188,000 pneumonia and influenza deaths per year. The language “roughly” is used because the April 2020 guidance appears to strongly encourage physicians and others to assign COVID-19 as the underlying cause of death in any death where COVID-19 is detected by tests or perhaps even just suspected, raising the possibility that heart attack and stroke deaths might be wrongly classified as COVID-19 deaths as well as the traditional pneumonia and influenza deaths that would be listed in the FluView data. These would presumably be misclassified as the COVID-19 deaths exhibiting the mysterious blood clots and other blood-related problems reported in some COVID-19 cases and deaths. Thus, the FluView death numbers may represent a lower bound on COVID-19 deaths rather than an exact baseline — unfortunately.
Ealy et al have raised the question whether the CDC complied with the Paperwork Reduction Act (PRA) and Information Quality Act (IQA) requirements in issuing the April and earlier March COVID-19 death certification guidelines, apparently without submitting these for public comment through the Federal Register as Ealy and co-authors claim is required by these federal laws.
Conclusion
Thus, due to the guidance on the death certificates from the CDC in April 2020, COVID-19 deaths on death certificates appear comparable to the larger FluView death numbers — or even larger numbers if heart attacks, strokes or other blood coagulation related deaths with a positive test or clinical diagnosis are being classified as COVID-19 deaths.
The CDC’s documents and web site are remarkably unclear, contradictory, and confusing for public health and scientific information presented to the general public, busy doctors and other medical professionals, or even research scientists — as previously noted by Peter Doshi and others.
(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).
There is almost certainly no smartphone privacy app that you can install on your phone to block monitoring and spying by the “government,” meaning the Total Information Awareness system constructed by the NSA and other security/intelligence agencies in the United States. There are almost certainly low level hardware or firmware backdoors in all popular smartphones such as Apple’s iPhone and the many Android phones that enable the “government” to access the phones at will. These backdoors operate at a low level below any installed app.
The Total Information Awareness (TIA) Program’s Original Controversial Logo
In the case of the iPhone the backdoors are probably in the Apple multitouch firmware that customers use to enter their password and interact with the iPhone; it is a little known but not secret fact that the US Central Intelligence Agency (CIA) funded the development of this multitouch technology at Fingerworks which was acquired by Apple both for the software and the Fingerworks development team.
“More directly, academics at the University of Delaware, funded by a National Science Foundation and CIA fellowship program, developed multi-touch scrolling and gestures for screens; they created a company called FingerWorks that was bought by Apple in 2005, two years before the iPhone was launched.”
These low level smartphone backdoors are very risky. With a large fraction of the human race using smartphones, they are tempting for abuse and almost certainly are being badly abused by the small cadre of intelligence officials with access to the system. The most likely abuse is mass/global scale blackmail and psychological manipulation for the acquisition of money and power. As the case of former New York governor Elliot Spitzer illustrates, many powerful people are remarkably careless with their electronic communications and would be subject to manipulation or blackmail by anyone with access to this system.
These backdoors are a high value target for the intelligence services of various nations such as Russia and China as well as organized criminal gangs, terrorists, and other groups. They are vulnerable to technological surprise by other as yet unknown actors who may develop the technology to take over the system or somehow already have the capability.
What Can You Do to Protect Your Privacy?
The reality is there is no app you can install to protect yourself and continue to use a smartphone — or many other devices — in the modern world of nearly ubiquitous high speed wireless networks. The practical options at present are either simply to stop using smartphones entirely or keep them in a sealed radio proof case, a “Faraday bag,” except for emergencies and special situations.
Such cases are currently available for purchase at Amazon and other sources. One can also use hermetically sealed metal food containers that are still widely available. Some games and other products are also marketed in such containers. It is possible to make secure cases for smartphones using metal foil (aluminum foil), painters tape or other tapes, and a ruler and pair of scissors — all common inexpensive household items.
Commercially Available Secure Case for a SmartphoneHermetically Sealed Food Container that Can Isolate Smartphone from the Radio NetworksDIY Secure Case Made with Painter’s Tape and Aluminum Foil
More detailed information on these options can be found here:
How to Stop Your Phone from Spying on You? Demonstrates methods to isolate your phone from cellular and other radio networks while keeping the phone easily accessible for emergencies or other special cases.
We do not receive any compensation for using or demonstrating these products.
(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).
Many Seeming Routes for Efficient Spread of COVID-19 in Santa Clara County, California (USA)
Twenty minute video on many seeming routes for efficient spread of COVID-19 in Santa Clara County, California, USA (Silicon Valley). Discusses herding of residents into Big Box retail stores such as Safeway, Target, and Walmart, large apartment complexes, the VTA bus system, and construction projects with many shared spaces and surfaces.
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
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 United States Centers for Disease Control (CDC) documents and web site present at least THREE different historical (pre-2020) estimates of deaths from “influenza and pneumonia,” “pneumonia and influenza,” and/or “influenza” or the “flu” presumably meaning the influenza category of viruses. Which of these three death numbers, one of which differs by a factor of OVER THREE from the other two, should be compared to COVID-19 deaths? Indeed, it could well be incorrect to compare any of them to the COVID-19 deaths.
These three pneumonia and influenza death numbers are the value in the annual leading causes of death report — about 55,000 deaths each year, the tables of “pneumonia and influenza” (abbreviated as P&I) from the National Center for Health Statistics (NCSH) used on the FluView web site — about 188,000 deaths per year, OVER THREE TIMES the leading causes of death number, and the output of a mysterious model of deaths attributed to the influenza category of viruses, a broad range centered at about 55,000 deaths per year.
Deaths attributed to COVID-19 in 2020 have frequently been compared to an estimate of about 55,000, either to the date of the report or for the entire year. The language used is often unclear but appears to refer to either the CDC’s influenza mathematical model or the leading causes of death number, which are similar numbers but technically not the same.
This article argues that the current COVID-19 death numbers are best compared to the larger FluView numbers, although an even larger different number may be appropriate if deaths that would have been attributed to heart attacks, strokes, or other blood coagulation related disorders in the absence of a positive SARS-COV-2 test or diagnosis are included in the current COVID-19 death counts.
The CDC FluView Web Site
The CDC FluView web site shows that six to ten percent of deaths, varying seasonally, are due to pneumonia and influenza (P&I) according to the vertical axis label on the FluView Pneumonia & Influenza Mortality plot. The underlying data files from the National Center for Health Statistics (NCHS) list about 188,000 deaths per year attributed to pneumonia and influenza.
US Centers for Disease Control (CDC) FluView Pneumonia & Influenza Mortality Plot (Dec. 4, 2020)
Note also that deaths attributed to “pneumonia and influenza” on the FluView web site are highly seasonal. A substantial increase over the summer is expected during the winter (or rainy season in Northern California and similar regions), peaking in December and January each year.
Also note that the seasonal variation is sinusoidal — like the oscillation of a pendulum or a mechanical spring. Contrary to popular culture, there is no clear step up when schools open in the fall or step down when schools close for the summer. It looks very much like something driven by the Sun, directly or indirectly by some mechanism or mechanisms. Possible mechanisms include Vitamin D production from sunlight, destruction of viruses and bacteria in the air or on surfaces by ultraviolet light in sunlight, general health benefits of a warm environment, or some other Sun-driven phenomenon.
The Leading Causes of Death Report
In contrast, the CDC’s leading causes of death report Table C, Deaths and percentage of total deaths for the 10 leading causes of death: United States, 2016 and 2017 on Page Nine (see screenshot below) attributes only two percent of annual deaths (about 55,000 in 2017) to “influenza and pneumonia.”
The difference between the CDC FluView and leading causes of death report numbers is probably due to the requirement that pneumonia or influenza be listed as “the underlying cause of death” in the leading causes of death report and only “a cause of death” in the FluView data. This is not clear. Many deaths have multiple “causes of death.” The assignment of an “underlying cause of death” may be quite arbitrary in some cases. Despite this, none of these official numbers either in the leading causes of death report or the FluView web site are reported with error bars or error estimates as required by common scientific and engineering practice when numbers are uncertain.
A longer, more detailed discussion of the FluView, leading causes of death, and the CDC’s influenza virus death model death numbers (yet another number) may be found below. It appears likely the FluView death numbers are defined similarly to current COVID-19 death counts; the reasons for this are explained in some detail.
US CDC Leading Causes of Death Report Attributes Only About Two Percent of All Deaths to Pneumonia and Influenza (Line Item 8: Influenza and Pneumonia) — not the Six to Ten Percent in the FluView Graphs
Which Pneumonia and Influenza Death Numbers Should Be Compared to the COVID-19 Death Numbers?
The FluView and Leading Causes of Death numbers for “pneumonia and influenza” differ by a factor of OVER THREE. Note that both the FluView and Leading Causes of Death numbers have no error bars or error estimates given, implying exact numbers in common scientific and engineering practice. Should the number of COVID-19 deaths be compared to the FluView number or the leading causes of death number or some other number?
The CDC also uses a mysterious mathematical model that attributes roughly 55,000 deaths from pneumonia and influenza to the influenza virus as the underlying cause, a number roughly comparable to the total pneumonia and influenza deaths in the leading causes of death. The influenza virus is confirmed by laboratory tests in only a small fraction of pneumonia and influenza deaths, about 6,000 per year.
Although the language is often unclear in the CDC documents and web site, the CDC appears to claim that an initial influenza infection which disappears or becomes undetectable in laboratory tests leads to the subsequent pneumonia, presumably a bacterial pneumonia although other viruses would be consistent with some lab tests. Based on this argument, the CDC appears to attribute most pneumonia deaths where historically pneumonia was listed as the “underlying cause of death” to the influenza virus for which there is a flu vaccine that the CDC promotes heavily — even though laboratory tests frequently fail to confirm influenza or even detect other viruses or bacteria instead. The “underlying cause of death” is discussed in more detail below.
As shown in the graphic above, the CDC web site Disease Burden of Influenza (Figure 1) appears to give a range from 12,000 to 61,000 influenza deaths from this model. The graphic does not indicate if this range is a 95 percent confidence interval — a common scientific and engineering practice — or some other error estimate. The range in the graphic does not appear to match any of the 95 percent confidence levels for estimated deaths attributed to influenza in Table 1.
The History of Serious Criticism of the CDC’s “Flu” Death Numbers
There is a long history of serious criticism of the CDC’s “flu” death numbers by medical scientists and others. The most prominent critic is Peter Doshi, currently a professor at the University of Maryland and an associate editor at the British Medical Journal.
Citing the results of actual laboratory tests of deceased patients, critics of the CDC’s flu death numbers such as University of Maryland Professor Peter Doshi have argued that pneumonia deaths are due to a range of different viruses, bacteria, other pathogens, and even toxins, rather than predominantly influenza as implied by the CDC’s influenza deaths model. The output of this model appears to be the basis of the baseline “flu” deaths numbers used in most popular and public policy discussions of COVID-19 deaths — although the leading causes of death report number may also be used.
Why the Large Difference Between the FluView and Leading Causes of Death Numbers
The US CDC documents and web site are frequently unclear and even contradictory as in the case of these grossly contradictory totals of deaths from pneumonia and influenza. This makes it difficult to be certain of the cause for the difference. Nonetheless, the technical notes for each document — FluView and the leading causes of death — give a highly probable reason.
Leading Causes of Death Report for 2017 with “the underlying cause of death” language circled in red. (Dec. 18, 2020)
Death certificates frequently have multiple causes of death. One of these is assigned as the underlying cause of death. This may be quite arbitrary in some cases. Indeed the concept of “underlying cause of death” may not be well defined for some deaths because the aged may develop multiple health problems in parallel that are fatal in combination.
In contrast, the FluView site, the much larger number of deaths, appears to count deaths where pneumonia or influenza is listed as “a cause of death,” even if it is not the “underlying cause of death.”
FluView Mortality Surveillance notes with “A Cause of Death” Language Circled in Red (Dec. 18, 2020)
The Rules for Assigning the Underlying Cause of Death Before COVID-19
Prior to 2020 and COVID-19, most pneumonia deaths did not list pneumonia or the pneumonia-causing pathogen if known as the underlying cause of death. This will be discussed in detail below. The only common partial exception was HIV/AIDS where pneumocystis carinii pneumonia (a common fungus) was often the immediate cause of death and the Human Immunodeficiency Virus (HIV) is almost always listed as the underlying cause of death. However, HIV is not the pneumonia-causing pathogen which is the pneumocystis fungus. Instead, most pneumonia deaths, those included in the FluView numbers but not included in the leading causes of death numbers, were attributed to a cause such as a chronic lower respiratory disease, heart disease, cancer, even accidents, and other usually pre-existing conditions as the underlying cause of death.
The CDC follows the World Health Organization (WHO)’s definition of the underlying cause of death. WHO defines the underlying cause of death as “the disease or injury which initiated the train of morbid events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury” in accordance with the rules of the International Classification of Diseases (ICD).
In the United States, the underlying cause of death is listed last in the list of causes of death in part I of the death certificate. The immediate cause of death is listed first. Part 2 lists other conditions that are considered contributing factors but somehow not causes. Pneumonia is often the immediate cause of death in part 1 of the death certificate.
In principle, death certificates and the assignment of causes of death including the underlying cause of death is governed or at least guided by the CDC’s Medical Examiners’ and Coroners’ Handbook on Death Registration and Fetal Death Reporting (2003 Revision). This one-hundred and thirty-eight (138) page manual actually provides very limited guidance on how to assign the underlying cause of death in cases where pneumonia is present. Page twenty-five (25) has the only detailed discussion of deaths involving pneumonia:
US CDC Medical Examiners’ Handbook 2003 suggests attributing a pneumonia death to either chronic ischemic heart disease or diabetes mellitus as the underlying cause but not the pneumonia or pneumonia-causingpathogen. No clear direction to the physician.
Pneumonia is often a nonspecific process that occurs as the terminal event in someone who dies of a more specific underlying cause of death, such as congestive heart failure resulting from ischemic heart disease. In such cases, the specific underlying cause of death should be included in the cause-of-death statement.
Pneumonia is often designated as either community acquired or hospital or institution acquired (nosocomial). If the community- or institution-acquired nature of the pneumonia is known, the cause-of-death statement should include an indication of which one applies.
The specific bacterial, viral, or other infectious agent, if known, should be cited in the cause-of-death statement.
Relevant risk factors should also be cited in the cause-of-death statement, as might occur in an alcoholic who develops tuberculous pneumonia. Only in those instances where pneumonia has caused death and there is no known underlying cause or risk factor should the underlying cause of death be stated as “Pneumonia,” being sure to specify the infectious agent, if known, or specifying that a specific etiology is unknown, if such is the case.
Emphasis Added
And on page 113 of Cause of Death and the Death Certificate by Randy Hanzlick, dementia, cerebrovascular disease, cardiac disease, and lung disease are all listed as common underlying causes of death in cases of deaths due to pneumonia:
Thus, traditionally, pre-2020 and COVID-19, pneumonia deaths were frequently assigned a non-pneumonia underlying cause of death, usually a pre-existing condition and not the pneumonia-causing pathogen such as the influenza virus or SARS-COV-2, in common medical practice. Based on the technical notes these pneumonia and influenza deaths would be included in the FluView death numbers but not in the leading causes of death report.
Comparing COVID-19 Death Numbers to the Pneumonia and Influenza Death Numbers and Estimates from Previous Years
As shown above, the CDC has at least three (3) different pneumonia and influenza death numbers and estimates: the Leading Causes of Death Report (about 55,000 deaths per year, about two percent of annual deaths from all causes), the FluView graph and underlying data from the NCHS (about 188,000 deaths per year, six to ten percent of annual deaths from all causes, before 2020), and the influenza death model estimates that range from 12,000 to 61,000 deaths per year with the best estimate close to the number of pneumonia and influenza deaths in the leading causes of death report. Are any of these the proper baseline for comparing COVID-19 deaths to prior years or should some other number or estimate be used?
In the absence of the RT-PCR and antibody tests for the SARS-COV-2 virus, most COVID-19 deaths would have been unexplained pneumonia deaths lacking a laboratory test confirming influenza or other known pathogen. Possibly, some COVID-19 deaths would have been listed as heart attacks or strokes, those COVID-19 deaths attributed to the blood clots and other blood-related anomalies currently blamed on COVID-19, or even some other causes. The rest of this article will focus on the pneumonia deaths which would probably comprise most of the COVID-19 deaths in the absence of laboratory tests.
The US CDC’s April 2020 guidelines for reporting COVID-19 deaths clearly direct physicians and others not to list chronic obstructive pulmonary disease (COPD) as the underlying cause of death in COVID-19 cases, moving it to Part 2 of the death certificate reserved for “non-cause” contributing factors, which differs dramatically from medical practice prior to 2020 as described in Randy Hanzlick’s book and implicit in the FluView pneumonia and influenza deaths data.
In some cases, survival from COVID–19 can be complicated by pre-existing chronic conditions, especially those that result in diminished lung capacity, such as chronic obstructive pulmonary disease (COPD) or asthma. These medical conditions do not cause COVID–19, but can increase the risk of contracting a respiratory infection and death, so these conditions should be reported in Part II and not in Part I.
This guidance also gives a specific example of a COVID-19 death with COPD relegated to Part 2:
COVID-19 Death Guidance Example with COPD as Contributing Factor Only
Although other causes of death that are often given as the underlying cause of death in pneumonia cases on pre-2020 death certificates are not explicitly identified in the April 2020 guidance document, it seems probable most physicians would move these pre-existing conditions to Part 2 and not list them as the underlying cause of death for COVID-19 based on the April 2020 CDC guidance document. Note that COPD would fall under the category “lung disease” in the list below from Randy Hanzlick’s Causes of Death and the Death Certificate:
Thus, COVID-19 deaths since the April 2020 guidance are probably roughly comparable to the FluView deaths, the larger number, the 188,000 pneumonia and influenza deaths per year. The language “roughly” is used because the April 2020 guidance appears to strongly encourage physicians and others to assign COVID-19 as the underlying cause of death in any death where COVID-19 is detected by tests or perhaps even just suspected, raising the possibility that heart attack and stroke deaths might be wrongly classified as COVID-19 deaths as well as the traditional pneumonia and influenza deaths that would be listed in the FluView data. These would presumably be misclassified as the COVID-19 deaths exhibiting the mysterious blood clots and other blood-related problems reported in some COVID-19 cases and deaths. Thus, the FluView death numbers may represent a lower bound on COVID-19 deaths rather than an exact baseline — unfortunately.
Ealy et al have raised the question whether the CDC complied with the Paperwork Reduction Act (PRA) and Information Quality Act (IQA) requirements in issuing the April and earlier March COVID-19 death certification guidelines, apparently without submitting these for public comment through the Federal Register as Ealy and co-authors claim is required by these federal laws.
Conclusion
Thus, due to the guidance on the death certificates from the CDC in April 2020, COVID-19 deaths on death certificates appear comparable to the larger FluView death numbers — or even larger numbers if heart attacks, strokes or other blood coagulation related deaths with a positive test or clinical diagnosis are being classified as COVID-19 deaths.
The CDC’s documents and web site are remarkably unclear, contradictory, and confusing for public health and scientific information presented to the general public, busy doctors and other medical professionals, or even research scientists — as previously noted by Peter Doshi and others.
(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).
In spite of and even because of the stay-at-home, lockdown and other restrictions by health authorities in Santa Clara County, California (USA), there are many plausible routes for rapid efficient spread of the SARS-COV-2 virus presumed to cause COVID-19 in the county. Despite this, total deaths attributed to COVID-19 in the year 2020 remained remarkably low, comparable to expected deaths from pneumonia and influenza in prior years. This may of course change in the coming year.
Many probable routes of transmission are discussed and illustrated with pictures below.
Many Seeming Routes for Rapid Spread of the Disease
Santa Clara County, California (United States) continues to authorize many luxury apartment and other construction projects with teams of workers in close proximity five full days per week, after a brief 3-4 week shutdown in May. The lockdown herds large numbers of citizens into a few gigantic stores such as Safeway, Walmart, and Target, enabling what would seem like an efficient route for rapid spread of the disease. The lockdown also confines a large proportion of residents to large apartment complexes with many seemingly easy routes of transmission. “Essential” workers and others continue to use the county bus service which confines workers and others to an enclosed shared space for many minutes each day.
Ongoing Construction Projects
Santa Clara County continues to authorize many luxury apartment and other construction projects with teams of workers in close proximity five full days per week, after a brief 3-4 week shutdown in May — several weeks after the original lockdown order. On a personal note, a four-story luxury apartment building construction project with at least a dozen workers every weekday from about 7:30 am to 4:30 pm has continued across the street from my apartment building since the original lockdown order except for the brief shutdown in May.
Santa Clara County Allows Construction Projects Despite COVID-19Over a Dozen Construction Workers Arriving for Work on Luxury Apartment Project in Santa Clara County, CA (August 6, 2020)Construction Workers in Close Proximity (Santa Clara County, Dec. 7, 2020)
Herding Residents into Big Box Retail Stores
The lockdown continues to herd large numbers of citizens in Santa Clara County into a few gigantic stores such as Safeway, Walmart, and Target, enabling what would seem like an efficient route for rapid spread of the disease.
Safeway with Over Thirty Cars in Parking Lot (about 9:30 AM, December 7, 2020, Santa Clara County)
These giant “Big Box” retail stores have heavily used shared spaces and surfaces where one would expect the virus will rapidly spread. These include the entry/exit door areas, checkout counters, and refrigerators with popular products such as milk purchased by a large fraction of the customers and with door handlesthat all purchasers must use. These large stores often have hundreds of patrons in the store at the same time — all day, seven days per week.
Milk and other dairy products in a refrigerator with door handle that customers must use
In Santa Clara County, the lockdowns have closed or heavily curtailed restaurants, popular with the large population of single people and leading to a large increase in demand for microwave dinners often found in store refrigerators with door handles that must be used by the customers.
Microwave dinners in store refrigerator with door handle that customers must use
Several other specific scenarios exist for rapid efficient spread of the virus through these giant retail stores.
Bus Service for “Essential” Workers and Others
Santa Clara County also has a bus service, the VTA or Valley Transportation Authority, in widespread use with patrons, often “essential workers,” sharing an enclosed space and seats.
VTA Bus in Operation on December 7, 2020, Santa Clara County, California
Giant Apartment Complexes are Common in Santa Clara County
The lockdown shelter in place and stay at home orders confine “non-essential workers” to numerous generally large apartment complexes, often with hundreds of tenants, possibly thousands in some cases. In Mountain View, California — site of Google’s headquarters — about fifty-eight percent of residents (Town Charts, see Figure 5) are renters, most in large complexes. These complexes feature shared trash chutes/rooms, laundry rooms, hallways and lobbies with exterior doors and fire doors that must be opened by hand in most cases, providing many shared surfaces and spaces for spread of the virus.
Typical Trash Chute Room with Fire Door in Santa Clara County — Note Door Handle (December 7, 2020)Typical Trash Room Interior with Trash Chute (Note Handle) in Santa Clara County (December 7, 2020)Typical Laundry Room Door with Handle in Santa Clara County (December 7, 2020)
Note that fire regulations require closely spaced closed fire doors in the interior hallways — with handles or knobs that all residents must use to open the fire doors.
The large apartment complexes common in Santa Clara County provide numerous shared spaces where aerosol virus particles can collect and linger in the air as well as shared surfaces such as door handles that all residents must touch.
Gloves are not required. Gloves would have to be handled carefully and sterilized before and after each or nearly each use to avoid spreading the virus, something probably impractical and certainly currently NOT done by most residents.
In general, the apartment complex support staff cannot clean each door handle after each use. The shared support staff themselves are a high risk of both becoming infected and spreading the infection to other residents. Almost none have training or experience in bio-safety measures.
Conclusion
Despite hyperbolic headlines and reasonable expectations, deaths attributed to COVID-19 have remained remarkably low in 2020, although that may — of course — change. We will see if the usual winter surge in cases and deaths from respiratory illnesses subsides for COVID-19 or not. Prediction is notoriously difficult — especially about the future. As demonstrated above, this appears to have been in spite of rather than because of the stay-at-home and lockdown measures.
The reasons for this remarkably low death rate in Santa Clara County in 2020 remain unknown. Possible explanations include the bright sunny climate — among the most pleasant in the world, close long term past contact with China contributing to a high pre-existing immunity or resistance to SARS type coronaviruses in general, some genetic or cultural difference in Santa Clara County’s heavily Asian population (death rates in Japan and other Asian nations have been quite low compared to the United States or Europe if official figures are to be believed), better treatment of patients such as avoiding intubation, higher consumption of vitamin D by the health conscious residents, or some other cause.
(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).
Short video on the censored-search.com search engine for censored web sites and Internet content with a brief demo of the search and other main features of the web site/service.
Censored Search:https://censored-search.com/ Find the answers to your problems censored by advertisers and other powerful interests!
A search engine for censored web sites and content.
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
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).
