Video on airborne spread of COVID-19 and other respiratory diseases such as tuberculosis and influenza, discussing implications for lockdowns, masks, and other measures. About twenty-five (25) minutes.
L0025227 Mask used by a plague doctor
Credit: Wellcome Library, London. Wellcome Images
images@wellcome.ac.uk
http://wellcomeimages.org
Mask used by a plague doctor: Masque porte a la fin au XVIIe siecle par les medecins visitant les pestiferes. Ce masque provient du lazaret de Venise; nous le reproduisons d’apres une aquarelle du Dr. L. Sambon.
Photograph
Aesculape
Published: 1932
Copyrighted work available under Creative Commons Attribution only licence CC BY 4.0 http://creativecommons.org/licenses/by/4.0/
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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).
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 is a short post on the critical distinction between the Case Fatality Rate (CFR) of a disease such as the SARS-COV-2 coronavirus thought to cause COVID-19 and the Infection Fatality Rate (IFR), also sometimes known as the actual mortality rate or lethality. This remains a source of confusion and perhaps deliberate obfuscation several months into the crisis.
The case fatality rate or CFR is the number of deathsattributed to the disease usually among those diagnosed with the disease divided by the number of diagnosed casesaccording to some diagnostic criterion, for example a “positive” RT-PCR (Reverse Transcriptase-Polymerase Chain Reaction) test.
The infection fatality rate or IFR is the number of deaths attributed to the disease divided by the actual number of people infected, which generally includes mild or asymptomatic infections that are not diagnosed. Most diseases have many mild or asymptomatic infections. This is not unusual and is the case for the coronavirus SARS-COV-2.
The CFR generally reflects those with more serious infections who seek medical attention, go to a hospital emergency room, etc. It is generally biased, usually higher than the IFR for most diseases, and also can vary a lot depending on the availability of tests and on other causes unrelated to the genuine lethality of the disease.
A disease that kills everyone who exhibits symptoms and no one who has no symptoms even though actually infected can have a case fatality rate (CFR) of 100 percent and and an infection fatality rate (IFR) of nearly 0.0 percent.
For example, an exotic disease that produces distinctive green and purple spots in those it kills — easily identifiable even without advanced tests like RT-PCR — but in fact kills only 100 people out of a United States population of 330 million even though most are infected for some reason.
Although there are a number of subtleties in the definition and computation of these numbers that I have omitted for clarity, the infection fatality rate (IFR), ideally broken down by age, medical conditions, and other risk factors, is key to evaluating the proper public health response to an outbreak of an infectious disease. Not the case fatality rate or CFR.
(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).
A key issue in the current coronavirus COVID-19 pandemic is how it compares to “seasonal flu,” “flu,” “influenza” or “pneumonia and influenza,” terms that are often used interchangeably but have different definitions or implied definitions in different contexts.
An overlapping key issue is whether false positives from the coronavirus RT-PCR test or diagnoses without the test (e.g. lung x-rays or examination of a patient by a doctor) has attributed a substantial number of conventional pneumonia deaths and heart attacks to the COVID-19 coronavirus.
Remarkably, the United States Centers for Disease Control (CDC) appears to use two different counts (or model outputs?) of annual deaths from “influenza and pneumonia” or “pneumonia and influenza” that differ by over a factor of three.
The “Deaths: Final Data for 2017” report (Page Six, Table B) lists “influenza and pneumonia” as the eighth leading cause of death with 55,672 deaths in 2017.
Influenza and pneumonia (8th Leading cause of death in Deaths: Final Data for 2017)
In contrast, the Weekly Pneumonia and Influenza (P&I) Mortality Surveillance lists over 180,000 deaths from “pneumonia and influenza” (mostly pneumonia) in 2017 in the data files on the site apparently used to generate the FluView plot displayed. The weekly surveillance number provides a much larger pool of potential false positives than the more widely quoted number of about 50,000 “flu” deaths per year.
Influenza: a study in contemporary medical politics by Peter Doshi
This is a well written but long (312 pages) dissertation. All of it is relevant to the current pandemic crisis, but it is a lot to digest. The most important and most relevant to the current pandemic section is Chapter 4: False Assumptions: a Shaky Foundation for Consensus (Pages 151-212, including tables and figures).
Key topics discussed in detail in this chapter include:
The CDC uses models to assign many deaths to influenza (the influenza viruses) even though doctors rarely diagnose influenza, rarely list influenza as a cause of death on death certificates, and most laboratory tests of samples from patients with respiratory illnesses (often called Influenza Like Illnesses or ILI) do not confirm the presence of the influenza viruses and often identify other viruses such as rhinovirus, adenovirus, various coronaviruses, etc. as present instead. These models may even assign deaths listed on death certificates as heart attacks to the total.
The models differ from researcher and publication to researcher and publication, have changed dramatically over the years, notably a jump from 20,000 estimated “flu” deaths in 2002 to a widely quoted estimate of 36,000 in 2003.
The evidence that flu vaccines work is weak and contradictory.
Conclusion
There is a remarkable lack of key measurements in the current coronavirus COVID-19 pandemic. These include the actual mortality rate (aka infection fatality rate) broken down by age, sex, race, pre-existing medical conditions, ambient temperature, sunlight levels, pollution levels, and other risk factors. The false positive and false negative rates of the tests for the disease, both the tests for an active infection such as the RT-PCR tests and tests for past infection such as the antibody tests. The methods and rates of transmission for the disease. Aerosol transmission probably occurs at least at a low level and is virtually unstoppable.
The confusing language and numbers on pneumonia and influenza on the CDC web site and in various official reports and documents seem to be primarily for marketing the flu vaccines rather than enabling informed decisions by patients and doctors or supporting external scientific research into the influenza viruses or other diseases.
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).
A key question about pneumonia and influenza is the role of sunlight and vitamin-D production on the incidence, severity, and mortality rates from the diseases. Pneumonia and influenza cases and deaths are seasonal, peaking in the winter when sunlight levels and temperatures are lower. The curve for pneumonia and influenza deaths is roughly sinusoidal, which would be expected from something connected to sunlight levels. This is not what one would naively expect from children spreading the disease during the school year; we would expect an abrupt step up in the fall when kids return to school and a step down in the spring when school closes.
The body needs sunlight to produce vitamin D. Sunlight, particularly the ultraviolet component, can damage or kill viruses and bacteria. These and other effects related to sunlight levels may play a role in the sinusoidal pattern of pneumonia and influenza deaths.
Roughly Sinusoidal Model of Pneumonia and Influenza Deaths
Vitamin D has long been recognized as essential to the skeletal system. Newer evidence suggests that it also plays a major role regulating the immune system, perhaps including immune responses to viral infection. Interventional and observational epidemiological studies provide evidence that vitamin D deficiency may confer increased risk of influenza and respiratory tract infection. Vitamin D deficiency is also prevalent among patients with HIV infection. Cell culture experiments support the thesis that vitamin D has direct anti-viral effects particularly against enveloped viruses. Though vitamin D’s anti-viral mechanism has not been fully established, it may be linked to vitamin D’s ability to up-regulate the anti-microbial peptides LL-37 and human beta defensin 2. Additional studies are necessary to fully elucidate the efficacy and mechanism of vitamin D as an anti-viral agent.
It
is now clear that vitamin D has important roles in addition to its
classic effects on calcium and bone homeostasis. As the vitamin D
receptor is expressed on immune cells (B cells, T cells and antigen
presenting cells) and these immunologic cells are all are capable of
synthesizing the active vitamin D metabolite, vitamin D has the
capability of acting in an autocrine manner in a local immunologic
milieu. Vitamin D can modulate the innate and adaptive immune responses.
Deficiency in vitamin D is associated with increased autoimmunity as
well as an increased susceptibility to infection. As immune cells in
autoimmune diseases are responsive to the ameliorative effects of
vitamin D, the beneficial effects of supplementing vitamin D deficient
individuals with autoimmune disease may extend beyond the effects on
bone and calcium homeostasis.
DisclaimerThe publisher’s final edited version of this article is available at J Investig MedSee other articles in PMC that cite the published article.Go to:
Over the past decade, interest has grown in the role of vitamin D in many nonskeletal medical conditions, including respiratory infection. Emerging evidence indicates that vitamin D-mediated innate immunity, particularly through enhanced expression of the human cathelicidin antimicrobial peptide (hCAP-18), is important in host defenses against respiratory tract pathogens. Observational studies suggest that vitamin D deficiency increases risk of respiratory infections. This increased risk may contribute to incident wheezing illness in children and adults and cause asthma exacerbations. Although unproven, the increased risk of specific respiratory infections in susceptible hosts may contribute to some cases of incident asthma. Vitamin D also modulates regulatory T-cell function and interleukin-10 production, which may increase the therapeutic response to glucocorticoids in steroid-resistant asthma. Future laboratory, epidemiologic, and randomized interventional studies are needed to better understand vitamin D’s effects on respiratory infection and asthma.PMID: 19063829 DOI: 10.1007/s11882-009-0012-7
The scientific and scholarly articles listed above include links to many other scholarly and scientific articles on the role of Vitamin D in respiratory illnesses.
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).
A key question about the coronavirus COVID-19 pandemic and pneumonia and influenza deaths in general is the role of air pollution in the disease and deaths. Wuhan, China — the presumed source of the outbreak — had high levels of pollution resulting in mass protests in July of 2019 and the hard hit Lombardy region of Italy had some of the highest air pollution levels in Europe. There are many kinds of air pollution and how they interact with the lungs, immune system, and various infections is unclear. There is a long body of research that air pollution increases the risk and severity of pneumonia.
I have listed several popular and scientific articles on air pollution and the coronavirus or pneumonia in general below. I also included several articles from 2019 on the protests in Wuhan at the end.
Keep in mind when medical scientists and the press say “linked” or “associated” this usually mean a statistical correlation has been found. Correlation (even perfect correlation) does not prove causation.
This New York Times article is about the Harvard research listed below in the Scientific Articles section.
Conclusions: A small increase in long-term exposure to PM2.5 leads to a large increase in COVID-19 death rate, with the magnitude of increase 20 times that observed for PM2.5 and all-cause mortality. The study results underscore the importance of continuing to enforce existing air pollution regulations to protect human health both during and after the COVID-19 crisis. The data and code are publicly available.
“Does air pollution make you more susceptible to coronavirus? California won’t like the answer” by Tony Barboza, March 21, 2020, LA Times
A relatively recent study at McMaster University in Canada linking air pollution to pneumonia.
Scientific Articles
Exposure to air pollution and COVID-19 mortality in the United States (Updated April 5, 2020)
Xiao Wu MS, Rachel C. Nethery PhD, M. Benjamin Sabath MA, Danielle Braun PhD, Francesca Dominici PhD All authors are part of the Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
Lead authors: Xiao Wu and Rachel C. Nethery Corresponding and senior author: Francesca Dominici, PhD
Conclusions: A small increase in long-term exposure to PM2.5 leads to a large increase in COVID-19 death rate, with the magnitude of increase 20 times that observed for PM2.5 and all-cause mortality. The study results underscore the importance of continuing to enforce existing air pollution regulations to protect human health both during and after the COVID-19 crisis. The data and code are publicly available.
Conclusions: Increased rates of culture-negative pneumonia and influenza were associated with increased PM2.5 concentrations during the previous week, which persisted despite reductions in PM2.5 from air quality policies and economic changes. Though unexplained, this temporal variation may reflect altered toxicity of different PM2.5 mixtures or increased pathogen virulence.
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).
Weekly Pneumonia and Influenza Death Numbers for 2020 and 2019 Compared
The updated plot is from a LibreOffice spreadsheet which can be downloaded at the link below. LibreOffice is a free, open-source alternative to Microsoft Office. It is available for Microsoft Windows, Mac OS X, and most flavors of Unix. It can be downloaded here.
I made a mistake copying the column of pneumonia and influenza deaths from early 2019 (weeks 1-13 of 2019 and weeks 50-52 of 2020) with LibreOffice Calc (the spreadsheet). The spreadsheet copied the macros in the cells instead of the number values. These macros were then applied to the columns to the left of the copied column, giving incorrect values that exaggerated the excess of deaths in comparable weeks last year (2019).
The actual excess is 1,841 more deaths in 2019, not about 6,000.
I was using the spreadsheet to make the results more accessible to a general audience. The copying error was consistent with the results from the more in-depth Python data analysis. In retrospect I should have checked the spreadsheet numbers more carefully.
Discussion
This does not change the conclusion that there is no sign of COVID-19 in the numbers until March 14, 2020 and a weak rise consistent with normal fluctuations in the weekly numbers in the final two weeks (March 14-28, 2020, weeks 12 and 13). It does reduce the size of the discrepancy between the two years. It remains possible that all the about 1,600 COVID-19 deaths reported as of March 28, 2020 could be conventional pneumonia and influenza deaths labeled as COVID-19 due to false positive RT-PCR tests and other misdiagnoses.
As I have discussed, there are strong reasons to doubt the CDC numbers. The most egregious I have found so far is the remarkable difference between the about 55,000 deaths from “influenza and pneumonia” in the leading causes of death tables (Table B, Page Six) and the about 188,000 deaths from “pneumonia and influenza” in the NCHSData14.csv file and other NCHSData<Week Number>.csv files.
An educated guess is that the 55,000 deaths from “influenza and pneumonia” is the output of a model the CDC uses to estimate the number of deaths directly or indirectly caused by “influenza viruses.” In the weekly pneumonia and influenza death numbers, the vast majority of deaths are listed as pneumonia and not the separate “influenza” category. Thus about 130,000 deaths appear to have been assigned to other categories in the final deaths for 2017 report, possibly “chronic lower respiratory diseases” which is the fourth (4th) leading cause of death. This is however a theory and CDC should carefully clarify what they are doing.
Accordingly, it is difficult to know what pre-processing or modeling/estimation may have been applied to the weekly pneumonia and influenza death numbers, although the commentary on the CDC web site implies these numbers are counts of death certificates and the causes of death on death certificates reported to the CDC by state and local authorities.
I am looking through the NCHSData<Week Number>.csv files to see how complete they may actually be. The FluView web page contains a table that seems to imply that all weeks except the very last week in the file are complete or almost complete. They use the label “> 100%” where > is presumably “greater than”. Of course, 100 percent usually means complete.
CDC “Percent Complete” Table (Misleading language at best)
There are many possible reasons for COVID-19 deaths not showing up in the weekly pneumonia and influenza death numbers before March 14, 2020 despite the Chinese coverup in December and early January, the US testing fiasco, the 430,000 visitors to the United States from China since the coronavirus surfaced, and the many asymptomatic carriers now being detected. These different possible reasons have different, even opposite in some cases, implications for public health policy.
Possible reasons include:
Despite the many problems above, the public health authorities have been remarkably successful in identifying nearly all COVID-19 deaths up to March 14, 2020. This seems too good to be true, but cannot be excluded.
The infection fatality rate (aka actual mortality rate) of the COVID-19 coronavirus is much less than early numbers such as 3.4 percent from the World Health Organization (WHO) or the 0.9-1 percent used by various authorities. Iceland, South Korean, Denmark and German data suggest about 0.5 percent mortality rate – which still could be higher than real rate.
Many COVID-19 deaths are due to aggressive treatment of the disease, e.g. intubation, rather than the disease alone.
The weekly pneumonia and influenza death numbers are substantially incomplete, due to normal delays or due to unusual delays associated with the crisis.
There has been a compensating drop in non-COVID pneumonia and influenza deaths due to shelter-in-place and taking it easy. Elderly and susceptible persons may have taken precautions in January and February due to the publicity, even before the shutdown in mid March.
Something else
Some combination of some or all of the above!
Conclusion
There is a remarkable lack of key measurements in the current coronavirus COVID-19 pandemic. These include the actual mortality rate (aka infection fatality rate) broken down by age, sex, race, pre-existing medical conditions, ambient temperature, sunlight levels, pollution levels, and other risk factors. The false positive and false negative rates of the tests for the disease, both the tests for an active infection such as the RT-PCR tests and tests for past infection such as the antibody tests. The methods and rates of transmission for the disease. Aerosol transmission probably occurs at least at a low level and is virtually unstoppable.
It is important to collect this data and measure these key parameters as quickly as possible in an open, “transparent” manner with multiple independent teams, not all funded or controlled by the CDC, as soon as possible to make good decisions based on knowledge and data, rather than fear, ignorance, and the primal fight or flight response.
(C) 2020 by John F. McGowan, Ph.D.
About Me
John F. McGowan, Ph.D. solves problems using mathematics and mathematical software, including developing gesture recognition for touch devices, video compression and speech recognition technologies. He has extensive experience developing software in C, C++, MATLAB, Python, Visual Basic and many other programming languages. He has been a Visiting Scholar at HP Labs developing computer vision algorithms and software for mobile devices. He has worked as a contractor at NASA Ames Research Center involved in the research and development of image and video processing algorithms and technology. He has published articles on the origin and evolution of life, the exploration of Mars (anticipating the discovery of methane on Mars), and cheap access to space. He has a Ph.D. in physics from the University of Illinois at Urbana-Champaign and a B.S. in physics from the California Institute of Technology (Caltech).
In coverage of the coronavirus COVID-19 pandemic, one often sees a value for the infection fatality rate (also known as the actual mortality rate, which is different from the “case fatality rate”) of 0.1 percent, meaning one in 1000 people infected by the “flu” dies. Infected includes people who are asymptomatic, have mild cases — anyone who is actually infected even if never detected. It is often explicitly or implicitly argued that if the infection fatality rate of COVID-19 is only 0.1 percent as suggested by a recent study by Stanford researchers (https://www.medrxiv.org/content/10.1101/2020.04.14.20062463v1.full.pdf) we can relax and go back to work. Unfortunately, it is probably not that simple.
We can use our prevalence estimates to approximate the infection fatality rate from COVID-19 in Santa Clara County. As of April 10, 2020, 50 people have died of COVID-19 in the County, with an average increase of 6% daily in the number of deaths. If our estimates of48,000-81,000 infections represent the cumulative total on April 1, and we project deaths to April 22 (a 3 week lag from time of infection to death22), we estimate about 100 deaths in the county. A hundred deaths out of 48,000-81,000 infections corresponds to an infection fatality rate of 0.12-0.2%. If antibodies take longer than 3 days to appear, if the average duration from case identification to death is less than 3 weeks, or if the epidemic wave has peaked and growth in deaths is less than 6% daily, then the infection fatality rate would be lower. These straightforward estimations of infection fatality rate fail to account for age structure and changing treatment approaches to COVID-19. Nevertheless,our prevalence estimates can be used to update existing fatality rates given the large upwards revision of under-ascertainment
COVID-19 Antibody Seroprevalence in Santa Clara County, California by Bendavid et al
It is thought the vast majority of adults get at least two symptomatic “colds” or “flus” in common usage (The CDC claims adults get 2-3 “common colds” per year and children more on their web site which matches common experience.). These are caused by a wide variety of viruses and bacteria and sometimes chemical toxins. These include the rhinovirus, various coronaviruses other that the “novel” SARS-COV-2 coronavirus, and many others including a category of viruses known as “influenza” or “influenza viruses”.
With a total US population of about 330 million, we can estimate at least 660 million individual cases and separate infections of these “cold” or “flu” organisms (either viruses or bacteria) each year. This gives a naive effective infection fatality rate averaged over the population and different diseases of:
188,000 divided by 660 million is: 0.028 percent (0.00028484848484848485)
55,000 divided by 660 million is: 0.008 percent (0.00008333333333)
This is of course much less than 0.1 percent (one in 1000).
What gives?
In common English usage, the terms “cold” and “flu” are often used interchangeably. The use of the terms “flu” and “influenza” to describe respiratory illnesses that vary in incidence seasonally predates the discovery of the influenza viruses, a category of viruses that can cause these symptoms. Influenza is Italian, from the Latin “influentia,” for “influence,” referring to the baleful influence of the stars that the ancients blamed for the disease.
The CDC hopelessly blurs the distinctions, if any, between “common cold”, “cold”, “flu”, “influenza”, “influenza like illness,” “influenza associated,” “pneumonia,” and other terms in its promotional and “scientific” materials.
Influenza as in the influenza viruses is rarely listed as a cause of death on death certificates. The weekly “pneumonia and influenza” death numbers from the National Center for Health Statistics (NCHS) only list about 8,000 deaths from influenza in 2017. The CDC cites several different reasons for claiming there is massive underdiagnosis and underreporting of influenza (THE VIRUS) deaths, dating back to at least 2005 and persisting despite the CDC’s extensive educational efforts.
The CDC uses a mysterious model to estimate about 55,000 annual deaths from influenza (THE VIRUS). Presumably the number of deaths from “influenza and pneumonia” in the leading causes of death is this number or something closely related — but this is not clear. Incidentally, in this age of the Internet and pervasive computing, the CDC could publish the actual source code for their model in a free open-source language such as Python on their web site for all to see and review.
CDC Graphic About Influenza Model (There is a HUGE correction for under-detection)
Part of this model is an estimate of how many “colds” are caused by an influenza virus. Presumably this number is about 55 million to get the widely quoted 0.1 percent (one in 1000) infection fatality rate for influenza. This is an example of the CDC’s estimates from https://www.cdc.gov/flu/about/burden/index.html:
Thus, the CDC estimates about 55 million of the annual over 660 million “cold” cases in the United States is caused by “influenza disease” or “influenza” or “flu,” presumably meaning cases caused by influenza viruses. This is probably less than ten percent of all “colds.” The CDC also estimates about 55,000 deaths from influenza viruses. This presumably gives the about 0.1 percent (one in 1000) number widely quoted in the media.
Everyone should understand that a 0.1 percent (one in 1000) infection fatality rate is much higher than the effective infection fatality rate of all the diseases that cause deaths attributed to “pneumonia and influenza” and that also typically cause two “common colds” or “flus” in healthy adults each year.
Even accepting the CDC’s estimates of the prevalence of illness due to influenza viruses (THE VIRUS), less than ten percent of all “common colds,” if the coronavirus COVID-19 spreads more easily than the influenza viruses, it may be able to kill more people than the influenza viruses with the same infection fatality rate (e.g. one in 1000, 0.1 percent). We also need to know how the SARS-COV-2 coronavirus spreads and how quickly.
If everyone in the United States were infected with the COVID-19 coronavirus, a 0.1 percent infection fatality rate (one in 1,000) would probably mean somewhat less than 330,000 additional deaths on top of the roughly 188,000 deaths from “pneumonia and influenza” (or is it 55,000 from “influenza and pneumonia”). There would be some overlap between COVID-19 coronavirus deaths and deaths of susceptible, mostly elderly persons that would have happened anyway due to conventional non-COVID diseases including the influenza viruses.
Conclusion
There is a remarkable lack of key measurements in the current coronavirus COVID-19 pandemic. These include the actual mortality rate (aka infection fatality rate) broken down by age, sex, race, pre-existing medical conditions, ambient temperature, sunlight levels, pollution levels, and other risk factors. The false positive and false negative rates of the tests for the disease, both the tests for an active infection such as the RT-PCR tests and tests for past infection such as the antibody tests. The methods and rates of transmission for the disease. Aerosol transmission probably occurs at least at a low level and is virtually unstoppable.
The CDC and the National Security bioweapons defense programs should have been set up to quickly and efficiently collect these key data and parameters as soon as a possible outbreak or attack was detected, independent of warnings and information provided by a potential adversary such as China or from the World Health Organization (WHO).
The confusing language and numbers on pneumonia and influenza on the CDC web site and in various official reports and documents seem to be primarily for marketing the flu vaccines rather than enabling informed decisions by patients and doctors or supporting external scientific research into the influenza viruses or other diseases.
(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).
This is a video showing the CDC Cold vs Flu web page on April 16, 2020 shortly after I published my “Uncounted COVID Deaths? The CDC’s Contradictory Pneumonia and Influenza Death Numbers” where I discussed the contradictory language and claims on the CDC’s Cold vs Flu web page. The video was recorded to support further my discussion in the Uncounted COVID article/presentation and because I think it likely the web page will change as the CDC fields hard questions about its Influenza and Pneumonia web pages, reports, and other documentation.
Astonishingly the CDC gives two radically different numbers of deaths from pneumonia and influenza: about 55,000 “influenza and pneumonia” deaths in the leading causes of death table in the “Final Deaths” report for 2017, the latest year available, and about 188,000 in data on weekly “pneumonia and influenza” deaths, over THREE TIMES the leading causes of death number.
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).
