Uncounted COVID Deaths? The CDC’s Contradictory Pneumonia and Influenza Death Numbers

This presentation discusses the CDC’s contradictory weekly and annual pneumonia and influenza deaths. Even the latest (as of April 14, 2020) weekly death numbers show fewer deaths in 2020 than comparable weeks last year (2019) despite the Coronavirus COVID-19 pandemic. Given asymptomatic carriers and inadequate testing in the United States, one would expect a surge in reported pneumonia and influenza deaths.

Remarkably summing the weekly pnemonia and influenza deaths gives about 180,000 annual deaths from pneumonia and influenza, over THREE TIMES the widely cited 55,000 “influenza and pneumonia” deaths from the annual leading causes of death report.

These numbers raise troubling questions about the CDC and its collection, analysis and reporting of pneumonia and influenza numbers. The low number of weekly deaths compared to last year could indicate that there may be many uncounted COVID deaths, or that the disease is much less deadly than popular reports, or several other possibilities with substantially different public health implications. The numbers need to be clarified as soon as possible.

Both a video version and a written PDF version are provided below. The written version is generally faster to read and includes references and some additional technical details.

NOTE: If you are concerned about these odd numbers, please consider sharing this post by e-mail, a link on your web site or blog, or other methods in addition to advertising-funded and other big company social media. My post of this on Hacker News soared for a few hours and then was flagged and shut down, for example. I have also encountered social media mobs that engage in name calling and do not address the substantive issues.

Video Presentation

Uncounted COVID Deaths? The CDC’s Contradictory Pneumonia and Influenza Death Numbers

The video is also available at BitChute: https://www.bitchute.com/video/wXvokey7AYj3/

Slides and Written Text with References (PDF Document)

UPDATE:

Links: The NCHS data file is: https://www.cdc.gov/flu/weekly/weeklyarchives2019-2020/data/NCHSData14.csv

The weekly pneumonia and influenza deaths page/section is: https://www.cdc.gov/flu/weekly/#S2

The FluView sub-page is: https://gis.cdc.gov/grasp/fluview/mortality.html

The Final Deaths Report for 2017 is: https://www.cdc.gov/nchs/data/nvsr/nvsr68/nvsr68_09-508.pdf

See Table B on page six(6) with the leading causes of death. See item 8 “influenza and pneumonia”

Companion Video showing CDC Web Site on April 15, 2020 showing the analysis in a spreadsheet: https://youtu.be/UY-ULcQM0jY

CDC Cold vs Flu Web Site Archival Video: https://youtu.be/xF1_LSH0-Xk

In case the CDC Pneumonia and Influenza weekly deaths web site changes.

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

Support Us: PATREON: https://www.patreon.com/user?u=28764298

(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).

Doomsday Virus? Coronavirus Cases NOT Boosting Weekly Death Numbers…YET

Doomsday Virus? Coronavirus Cases NOT Boosting Weekly Death Numbers…YET

Doomsday Virus? Coronavirus cases are NOT boosting the weekly death numbers for pneumonia and influenza from the CDC and National Center for Health Statistics yet. This would be expected if the coronavirus is unusually deadly compared to other diseases that contribute to deaths categorized as pneumonia and influenza… YET! (Based on data through March 14, 2020)

CDC Pneumonia and Influenza Mortality Surveillance: https://www.cdc.gov/flu/weekly/#S2

Credit: Pete Linforth by way of Pixabay for the background image of the coronavirus.

https://pixabay.com/illustrations/coronavirus-corona-virus-covid-19-4833754/

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

Support Us: PATREON: https://www.patreon.com/user?u=28764298

(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).

Fake Indian? Elizabeth Warren’s DNA Test

(C) 2020 by John F. McGowan, Ph.D.

United States Senator and Democratic Presidential Candidate Elizabeth Warren has claimed to be an American Indian, a Cherokee and/or Delaware, or at least to have American Indian ancestry depending on how one interprets her past statements and actions, backing this up in October of 2018 with a widely criticized DNA test.

In this video I argue that the DNA test was based on TOO SMALL a sample of Europeans (NOT American Indians) to reliably determine whether Senator Warren has American Indian ancestry.

The European sample was only a few hundred people, too small to accurately measure the incidence among Europeans of Asiatic DNA segments that American Indians appear to share with some peoples in central and east Asia.

Links:

Reliability of DNA Ancestry Tests: https://wordpress.jmcgowan.com/wp/the-reliability-of-dna-ancestry-tests/

Professor Carlos Bustamante’s Report on Elizabeth Warren’s DNA Test: https://elizabethwarren.com/wp-content/uploads/2018/10/Bustamante_Report_2018.pdf

Rule of Three in Statistics: https://en.wikipedia.org/wiki/Rule_of_three_(statistics) 1000 Genome Project: https://www.internationalgenome.org/

Popular Article on Celtic Aspects of Tarim Mummies in Xinjiang, China: https://www.independent.co.uk/news/world/asia/a-meeting-of-civilisations-the-mystery-of-chinas-celtic-mummies-5330366.html

Support US: PATREON: https://www.patreon.com/user?u=28764298

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).

Vioxx: The Case of the Deadly Data Analysis [Video]

Vioxx: The Case of the Deadly Data Analysis (Video)

(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).

How to Evaluate a Counting Statistic

A counting statistic is simply a numerical count of the number of some item such as “one million missing children”, “three million homeless”, and “3.5 million STEM jobs by 2025.” Counting statistics are frequently deployed in public policy debates, the marketing of goods and services, and other contexts. Particularly when paired with an emotionally engaging story, counting statistics can be powerful and persuasive. Counting statistics can be highly misleading or even completely false. This article discusses how to evaluate counting statistics and includes a detailed list of steps to follow to evaluate a counting statistic.

Checklist for Counting Statistics

  1. Find the original primary source of the statistic. Ideally you should determine the organization or individual who produced the statistic. If the source is an organization you should find out who specifically produced the statistic within the organization. If possible find out the name and role of each member involved in the production of the statistic. Ideally you should have a full citation to the original source that could be used in a high quality scholarly peer-reviewed publication.
  2. What is the background, agenda, and possible biases of the individual or organization that produced the statistic? What are their sources of funding? What is their track record, both in general and in the specific field of the statistic? Many statistics are produced by “think tanks” with various ideological and financial biases and commitments.
  3. How is the item being counted defined. This is very important. Many questionable statistics use a broad, often vague definition of the item paired with personal stories of an extreme or shocking nature to persuade. For example, the widely quoted “one million missing children” in the United States used in the 1980’s — and even today — rounded up from an official FBI number of about seven hundred thousand missing children, the vast majority of whom returned home safely within a short time, paired with rare cases of horrific stranger abductions and murders such as the 1981 murder of six year old Adam Walsh.
  4. If the statistic is paired with specific examples or personal stories, how representative are these examples and stories of the aggregate data used in the statistic? As with the missing children statistics in the 1980’s it is common for broad definitions giving large numbers to be paired with rare, extreme examples.
  5. How was the statistic measured and/or computed? At one extreme, some statistics are wild guesses by interested parties. In the early stages of the recognition of a social problem, there may be no solid reliable measurements; activists are prone to providing an educated guess. The statistic may be the product of an opinion survey. Some statistics are based on detailed, high quality measurements.
  6. What is the appropriate scale to evaluate the counting statistic? For example, the United States Census estimates the total population of the United States as of July 1, 2018 at 328 million. The US Bureau of Labor Statistics estimates about 156 million people are employed full time in May 2019. Thus “3.5 million STEM jobs” represents slightly more than one percent of the United States population and slightly more than two percent of full time employees.
  7. Are there independent estimates of the same or a reasonably similar statistic? If yes, what are they? Are the independent estimates consistent? If not, why not? If there are no independent estimates, why not? Why is there only one source? For example, estimates of unemployment based on the Bureau of Labor Statistics Current Population Survey (the source of the headline unemployment number reported in the news) and the Bureau’s payroll survey have a history of inconsistency.
  8. Is the statistic consistent with other data and statistics that are expected to be related? If not, why doesn’t the expected relationship hold? For example, we expect low unemployment to be associated with rising wages. This is not always the case, raising questions about the reliability of the official unemployment rate from the Current Population Survey.
  9. Is the statistic consistent with your personal experience or that of your social circle? If not, why not? For example, I have seen high unemployment rates among my social circle at times when the official unemployment rate was quite low.
  10. Does the statistic feel right? Sometimes, even though the statistic survives detailed scrutiny — following the above steps — it still doesn’t seem right. There is considerable controversy over the reliability of intuition and “feelings.” Nonetheless, many people believe a strong intuition often proves more accurate than a contradictory “rational analysis.” Often if you meditate on an intuition or feeling, more concrete reasons for the intuition will surface.

(C) 2019 by John F. McGowan, Ph.D.

About Me

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

The Reliability of DNA Ancestry Tests

DNA ancestry tests are tests marketed by genetic testing firms such as 23andme , Ancestry.com , Family Tree DNA, and National Geographic Geno as well as various consultants and academic researchers that purport to give the percentage of ancestry of a customer from various races, ethnic groups, and nationalities. They have been marketed to African-Americans to supposedly locate their ancestors in Africa (e.g. Ghana versus Mozambique) and many other groups with serious questions about their family history and background.

It is difficult (perhaps impossible) to find detailed information on the accuracy and reliability of the DNA ancestry kits on the web sites of major home DNA testing companies such as 23andMe. The examples shown on the web sites usually show only point estimates of ancestry percentages without errors. This is not common scientific practice where numbers should always be reported with errors (e.g. ±0.5 percent). The lack of reported errors on the percentages implies no significant errors are present; any error is less than the least significant reported digit (a tenth of one percent here).

Point Estimates of Ancestry Percentages from 23andMe web site (screen capture on Feb. 14, 2019)

The terms of service on the web sites often contain broad disclaimers:


The laboratory may not be able to process your sample, and the laboratory process may result in errors. The laboratory may not be able to process your sample up to 3.0% of the time if your saliva does not contain a sufficient volume of DNA, you do not provide enough saliva, or the results from processing do not meet our standards for accuracy.* If the initial processing fails for any of these reasons, 23andMe will reprocess the same sample at no charge to the user. If the second attempt to process the same sample fails, 23andMe will offer to send another kit to the user to collect a second sample at no charge. If the user sends another sample and 23andMe’s attempts to process the second sample are unsuccessful, (up to 0.35% of all samples fail the second attempt at testing according to 23andMe data obtained in 2014 for all genotype testing),* 23andMe will not send additional sample collection kits and the user will be entitled solely and exclusively to a complete refund of the amount paid to 23andMe, less shipping and handling, provided the user shall not resubmit another sample through a future purchase of the service. If the user breaches this policy agreement and resubmits another sample through a future purchase of the service and processing is not successful, 23andMe will not offer to reprocess the sample or provide the user a refund. Even for processing that meets our high standards, a small, unknown fraction of the data generated during the laboratory process may be un-interpretable or incorrect (referred to as “Errors”). As this possibility is known in advance, users are not entitled to refunds where these Errors occur.

23andMe Terms of Service on Feb. 14, 2019 (Emphasis added)

“A small, unknown fraction” can mean ten percent or even more in common English usage. No numerical upper bound is given. Presumably, “un-interpretable” results can be detected and the customer notified that an “Error” occurred. The Terms of Service does not actually say that this will happen.

Nothing indicates the “incorrect” results can be detected and won’t be sent to the customer. It is not clear whether “data generated during the laboratory process” includes the ancestry percentages reported to customers.

On January 18, 2019 the CBC (Canadian Broadcasting Corporation) ran a news segment detailing the conflicting results from sending a reporter and her identical twin sister’s DNA to several major DNA ancestry testing companies: “Twins get some ‘mystifying’ results when they put 5 DNA Ancestry Kits to the test.” These included significant — several percent — differences in reported ancestry between different companies and significant differences in reported ancestry between the two twins at the same DNA testing company! The identical twins have almost the same DNA.

The CBC is not the first news organization to put the DNA ancestry tests to a test and get surprising results. For example, on February 21, 2017, CBS’s Inside Edition ran a segment comparing test results for three identical triplets: “How Reliable are Home DNA Ancestry Tests? Investigation Uses Triplets to Find Out.”

The sisters were all 99 percent European but the test from 23andMe also showed some surprising differences.

Nicole was 11 percent French and German but Erica was 22.3 percent. Their sister Jaclyn was in the middle at 18 percent.

Inside Edition: How Reliable Are Home DNA Ancestry Tests

It is not uncommon to encounter YouTube videos and blog posts reporting experiences with home DNA tests where the results from different companies differ by several percent, the results from the same company change by several percent, or report a small percentage of ancestry not supported by any family history, documentation or visible features. Ashkenazi Jewish, African, Asian, and American Indian are all common in the surprising results. Test results from commercial DNA tests reporting American Indian ancestry seem remarkably uncorrelated with family traditions of American Indian ancestry. Some users have reported gross errors in the test results although these seem rare.

The major DNA ancestry testing companies such as 23andMe may argue that they have millions of satisfied customers and these reports are infrequent exceptions. This excuse is difficult to evaluate since the companies keep their databases and algorithms secret, the ground truth in many cases is unknown, and many customers have only a passing “recreational” interest in the results.

Where the interest in the DNA ancestry results is more serious customers should receive a very high level of accuracy with the errors clearly stated. Forensic DNA tests used in capital offenses and paternity tests are generally marketed with claims of astronomical accuracy (chances of a wrong result being one in a billion or trillion). In fact, errors have occurred in both forensic DNA tests and paternity tests, usually attributed to sample contamination.

How Accurate are DNA Ancestry Tests?

DNA ancestry tests are often discussed as if the DNA in our cells comes with tiny molecular barcodes attached identifying some DNA as black, white, Irish, Thai and so forth. News reports and articles speak glibly of “Indian DNA” or “Asian DNA”. It sounds like DNA ancestry tests simply find the barcoded DNA and count how much is in the customer’s DNA.

The critical point, which is often unclear to users of the tests, is that the DNA ancestry test results are estimates based on statistical models of the frequency of genes and genetic markers in populations. Red hair gives a simple, visible example. Red hair is widely distributed. There are people with red hair in Europe, Central Asia, and even Middle Eastern countries such as Iran, Iraq and Afghanistan. There were people with red hair in western China in ancient times. There are people with red hair in Polynesia and Melanesia!

However red hair is unusually common in Ireland, Scotland, and Wales with about 12-13% of people having red hair. It is estimated about forty percent of people in Ireland, Scotland, and Wales carry at least one copy of the variant M1CR gene that seems to be the primary cause of most red hair. Note that variations in other genes are also believed to cause red hair. Not everyone with red hair has the variation in M1CR thought to be the primary cause of red hair. Thus, if someone has red hair (or the variant M1CR gene common in people with red hair), we can guess they have Irish, Scottish, or Welsh ancestry and we will be right very often.

Suppose we combine the red hair with traits — genes or genetic markers in general — that are more common in people of Irish, Scottish or Welsh descent than in other groups. Then we can be more confident that someone has Irish, Scottish, or Welsh ancestry than using red hair or the M1CR gene alone. In general, even with many such traits or genes we cannot be absolutely certain.

To make combining multiple traits or genes more concrete, let’s consider two groups (A and B) with different frequencies of common features. Group A is similar to the Irish, Scots, and Welsh with thirteen percent having red hair. Group A is more southern European with only one percent having red hair. The distributions of skin tone differ with Group A having eighty percent with very fair skin versus only fifty percent in Group B. Similarly blue eyes are much more common in Group A: fifty percent in group A and only 8.9 percent in group B. To make the analysis and computations simple, Groups A and B have the same number of members — one million.

For illustrative purposes only, we are assuming the traits are uncorrelated. In reality, red hair is correlated with very fair skin and freckles.

Estimating Group Membership from Multiple Traits

Using hair color alone, someone with red hair has a 13/(13+1=14) or 95.86% chance of belonging to group A. Using hair color, skin tone, and eye color, someone with red hair and very fair skin and blue eyes has a 5.2/(5.2+0.0445=5.2445) or 99.14% chance of belonging to group A.

Combining multiple traits (or genes and genetic markers) increases our confidence in the estimate of group membership but it cannot give an absolute definitive answer unless at least one trait (or gene or genetic marker) is unique to one group. This “barcode” trait or gene is a unique identifier for group membership.

Few genes or genetic markers have been identified that correlate strongly with our concepts of race, ethnicity, or nationality. One of the most well known and highly correlated examples is the Duffy null allele which is found in about ninety percent of Sub-Saharan Africans and is quite rare outside of Sub-Saharan Africa. The Duffy allele is thought to provide some resistance to vivax malaria.

Nonetheless, white people with no known African ancestry are sometimes encountered with the Duffy allele. This is often taken as indicating undocumented African ancestry, but we don’t really know. At least anecdotally, it is not uncommon for large surveys of European ancestry populations to turn up a few people with genes or genetic markers like the Duffy allele that are rare in Europe but common in Africa or Polynesia or other distant regions.

A More Realistic Example

The Duffy null allele and the variant M1CR gene that is supposed to be the cause of most red hair are unusually highly correlated with group membership. For illustrative purposes, let’s consider a model of combining multiple genes to identify group membership that may be more like the real situation.

Let’s consider a collection of one hundred genes. For example these could be genes that determine skin tone. Each gene has a light skin tone and a dark skin tone variant. The more dark skin tone variants someone has, the darker their skin tone. For bookkeeping we label the the light skin tone gene variants L1 through L100 and the dark skin tone genes variants D1 through D100.

Group A has a ten percent (1 in 10) chance of having the dark skin variant of each gene. On average, a member of group A has ninety (90) of the light skin tone variants and ten (10) of the dark skin variants. Group A may be somewhat like Northern Europeans.

Group B has a thirty percent (about 1 in 3) chance of having the dark skin variant of each gene. On average, a member of group B has seventy (70) of the light skin variants and thirty (30) of the dark skin variants. Group B may be somewhat like Mediterranean or some East Asian populations.

Notice that none of the gene variants is at all unique or nearly unique to either group. None of them acts like the M1CR variant associated with red hair, let alone the Duffy null allele. Nonetheless a genetic test can distinguish between membership in group A and group B with high confidence.

Group A versus Group B

Group A members have on average ten (10) dark skin variants with a standard deviation of three (3). This means ninety-five percent (95%) of Group A members will have between four (4) and sixteen (16) of the dark skin variant genes.

Group B members have on average thirty (30) dark skin variants with a standard deviation of about 4.6. This means about ninety-five percent (95%) of Group B members will have between twenty-one (21) and thirty-nine (39) dark skin variants.

In most cases, counting the number of dark skin variants that a person possesses will give over a ninety-five percent (95%) confidence level as to their group membership.

Someone with a parent from Group A and a parent from group B will fall smack in the middle, with an average of twenty (20) dark skin gene variants. Based on the genetic testing alone, they could be an unusually dark skinned member of Group A, an unusually light skinned member of Group B, or someone of mixed ancestry.

Someone with one grandparent from Group B and three grandparents from Group A would have fifteen (15) dark skin gene variants in their DNA, falling within two standard deviations of the Group A average. At least two percent (2%) of the members of Group A will have a darker skin tone than this person. Within just a few generations we lose the ability to detect Group B ancestry!

In the real world, each gene will have different variant frequencies, not ten percent versus thirty percent for every one, making the real world probability computations much more complicated.

The Out of Africa Hypothesis

The dominant hypothesis of human origins is that all humans are descended from an original population of early humans in Africa, where most early fossil remains of human and pre-human hominids have been found. According to this theory, the current populations in Europe, Asia, the Pacific Islands, and the Americas are descended from small populations that migrated out of Africa relatively recently in evolutionary terms — fifty-thousand to two-hundred thousand years ago depending on the variant of the theory. Not a lot of time for significant mutations to occur. Thus our ancestors may have started out with very similar frequencies of various traits, genes, and genetic markers. Selection pressures caused changes in the frequency of the genes (along with occasional mutations), notably selecting for lighter skin in northern climates.

Thus all races and groups may contain from very ancient times some people with traits, genes and genetic markers from Africa that have become more common in some regions and less common in other regions. Quite possibly the original founding populations included some members with the Duffy allele which increased in prevalence in Africa and remained rare or decreased in the other populations. Thus the presence of the Duffy allele or other rare genes or genetic markers does not necessarily indicate undocumented modern African ancestry — although it surely does in many cases.

Racially Identifying Characteristics Are Caused By Multiple Genes

The physical characteristics used to identify races such as skin tone, the extremely curly hair among most black Africans, and the epicanthic folds in East Asians (Orientals) that give the distinctive “slant” eyed appearance with the fold frequently covering the interior corner of the eye appear to be caused by multiple genes rather than a single “barcode” racial gene. Several genes work together to determine skin tone in ways that are not fully understood. Thus children of a light skinned parent and a dark skinned parent generally fall somewhere on the spectrum between the two skin tones.

Racially identifying physical characteristics are subject to blending inheritance and generally dilute away with repeated intermixing with another race as is clearly visible in many American Indians with well documented heavy European ancestry as for example the famous Cherokee chief John Ross:

The Cherokee Chief John Ross (1790-1866)

Would a modern DNA ancestry test have correctly detected John Ross’s American Indian ancestry?

There are also many examples of people with recent East Asian ancestry who nonetheless look entirely or mostly European. These include the actresses Phoebe Cates (Chinese-Filipino grandfather), Meg Tilly (Margaret Elizabeth Chan, Chinese father), and Kristin Kreuk (Smallville, Chinese mother). Note that none of these examples has an epicanthic fold that cover the interior corner of the eyes. Especially since these are performers, the possibility of unreported cosmetic surgery cannot be ignored, but it is common for the folds to disappear or be greatly moderated in just one generation — no longer covering the interior corner of the eye for example.

Phoebe Cates at the 81st Academy Awards (Credit:
Greg in Hollywood (Greg Hernandez)Flickr
CC BY 2.0 )
Meg Tilly at the Toronto International Film Festival in 2013 (Credit:
Mr. BombdiggityFlick
CC BY 2.0 )
Smallville actress Kristin Kreuk in 2011 (Credit:
Carlos Almendarez from San Francisco, USA
CC BY 2.0 )

How well do the DNA ancestry tests work for European looking people with well-documented East Asian ancestry, even a parent?

There are also examples of people with recent well-documented African, Afro-Carribean, or African-American ancestry who look very European. The best-selling author Malcolm Gladwell has an English father and a Jamaican mother. By his own account, his mother has some white ancestry. His hair is unusually curly and I suspect an expert in hair could distinguish it from unusually curly European hair.

Malcolm Gladwell speaks at PopTech! 2008 conference. (Credit:
Kris Krüg – https://www.flickr.com/photos/poptech2006/2967350188/
CC BY 2.0 )

In fact, some (not all) of the genes that cause racially identifying physical characteristics may be relatively “common” in other races, not extremely rare like the Duffy allele. For example, a few percent of Northern Europeans, particularly some Scandinavians, have folds around the eyes similar to East Asians, although the fully developed fold covering the interior corner of the eye is rare. Some people in Finland look remarkably Asian although they are generally distinguishable from true Asians. This is often attributed to Sami ancestry, although other theories include the Mongol invasions of the thirteenth century, the Hun invasions of the fifth century, other unknown migrations from the east, and captives brought back from North America or Central Asia by Viking raiders.

The Icelandic singer Björk (Björk Guðmundsdóttir) is a prominent example of a Scandinavian with strongly Asian features including a mild epicanthic fold that does not cover the interior corners of her eyes. Here are some links to closeups of her face that look particularly Asian: https://nocturnades.files.wordpress.com/2014/06/bjork.jpeg, http://music.mxdwn.com/wp-content/uploads/2015/03/Bjork_1_11920x1440_International_Star_Singer_Wallpaper.jpg and https://guidetoiceland.is/image/4927/x/0/top-10-sexiest-women-in-iceland-2014-10.jpg

There is a lot of speculation on-line that Björk has Inuit ancestry and she has performed with Inuit musicians, but there appears to be no evidence of this. As noted above, a small minority of Scandinavians have epicanthic folds and other stereotypically Asian features.

The epicanthic fold is often thought to be an adaptation to the harsh northern climate with East Asians then migrating south into warmer regions. It is worth noting that the epicanthic fold and other “East Asian” eye features are found in some Africans. The “Out of Africa” explanation for milder forms of this feature in some northern Europeans is some early Europeans carried the traits with them from Africa and it was selected for in the harsh northern climate of Scandinavia and nearby regions, just as may have happened to a much greater extent in East Asia.

The critical point is that at present DNA ancestry tests — which are generally secret proprietary algorithms — are almost certainly using relative frequencies of various genes and genetic markers in different populations rather than a mythical genetic barcode that uniquely identifies the race, ethnicity, or nationality of the customer or his/her ancestors.

Hill Climbing Algorithms Can Give Unpredictable Results

In data analysis, it is common to use hill-climbing algorithms to “fit” models to data. A hill climbing algorithm starts at an educated or sometimes completely random guess as to the right result, searches nearby, and moves to the best result found in the neighborhood. It repeats the process until it reaches the top of a hill. It is not unlikely that some of the DNA ancestry tests are using hill climbing algorithms to find the “best” guess as to the ancestry/ethnicity of the customer.

Hill climbing algorithms can give unpredictable results depending both on the original guess and very minor variations (such as small differences between the DNA of identical twins). This can happen when the search starts near the midpoint of a valley between two hills. Should the algorithm go up one side (east) or up the other side of the valley (west)? A very small difference in the nearly flat valley floor can favor one side over the other, even though otherwise the situation is very very similar.

In DNA testing, east-west location might represent the fraction of European ancestry and the north-west location might represent the fraction of American Indian ancestry (for example). The height of the hill is measure of the goodness of fit between the model and the data (the genes and genetic markers in the DNA). Consider the difficulties that might arise discriminating between someone, mostly European, with a small amount of American Indian ancestry (say some of the genes that contribute to the epicanthic fold found in some American Indians) and someone who is entirely European but has a mild epicanthic fold and, in fact, some of the same genes. Two adjacent hills with a separating valley may appear — one representing all European and one representing Mostly European with a small mixture of American Indian.

This problem with hill climbing algorithms may explain the striking different results for two identical twins from the same DNA testing company reported by the CBC.

Other model fitting and statistical analysis methods can also exhibit unstable results in certain situations.

Again, the DNA ancestry tests are using the relative frequency of genes and genetic markers found in many groups, even in different races and on different continents, rather than a hypothetical group “barcode” gene that is a unique identifier.

Conclusion

It is reasonable to strongly suspect, given the many reports like the recent CBC news segment of variations in the estimated ancestry of several percent, that DNA ancestry tests for race, ethnicity, and nationality are not reliable at the few percent level (about 1/16, 6.25%, great-great-grandparent level) at present (Feb. 2019). Even where an unusual gene or genetic marker such as the Duffy null allele that is highly correlated with group membership is found in a customer, some caution is warranted as the “out of Africa” hypothesis suggests that many potential group “barcode” genes and markers will be present at low levels in all human populations.

It may be that the many reports of several percent errors in DNA ancestry tests are relatively rare compared to the millions of DNA ancestry tests now administered. Many DNA ancestry tests are “recreational” and occasional errors of several percent in such recreational cases are tolerable. Where DNA ancestry tests have serious implications, public policy or otherwise, much higher accuracy — as is claimed for forensic DNA tests and DNA paternity tests — is expected and should be required. Errors (e.g. ±0.5 percent) and/or confidence levels should be clearly stated and explained.

Some Academic Critiques of DNA Ancestry Testing

Inferring Genetic Ancestry: Opportunities, Challenges, and Implications

Charmaine D. Royal, John Novembre, Stephanie M. Fullerton, David B. Goldstein, Jeffrey C. Long, Michael J. Bamshad, and Andrew G. Clark

The American Journal of Human Genetics 86, 661–673, May 14, 2010

The Illusive Gold Standard in Genetic Ancestry Testing

  1. Sandra Soo-Jin Lee1,*,
  2. Deborah A. Bolnick2,
  3. Troy Duster3,4,
  4. Pilar Ossorio5,
  5. Kimberly TallBear6

See all authors and affiliations Science  03 Jul 2009:
Vol. 325, Issue 5936, pp. 38-39
DOI: 10.1126/science.1173038

The Science and Business of Genetic Ancestry Testing

  1. Deborah A. Bolnick1,*,
  2. Duana Fullwiley2,
  3. Troy Duster3,4,
  4. Richard S. Cooper5,
  5. Joan H. Fujimura6,
  6. Jonathan Kahn7,
  7. Jay S. Kaufman8,
  8. Jonathan Marks9,
  9. Ann Morning3,
  10. Alondra Nelson10,
  11. Pilar Ossorio11,
  12. Jenny Reardon12,
  13. Susan M. Reverby13,
  14. Kimberly TallBear14,15

See all authors and affiliations Science  19 Oct 2007:
Vol. 318, Issue 5849, pp. 399-400
DOI: 10.1126/science.1150098

The American Society of Human Genetics Ancestry Testing Statement

November 13, 2008

(C) 2019 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).

How to Tell Scientifically if Advertising Boosts Profits Video

Daily Sales Pie Charts
How to Tell Scientifically if Advertising Boosts Profits Video


Short (seven and one half minute) video showing how to evaluate scientifically if advertising boosts profits using mathematical modeling and statistics with a pitch for our free open source AdEvaluator software and a teaser for our non-free AdEvaluator Pro software — coming soon.

Download the free open source version of AdEvaluator at http://wordpress.jmcgowan.com/wp/downloads/

(C) 2019 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).

How to Tell Scientifically if Advertising Works Video

Daily Sales Pie Charts
AdEvaluator Demo Video (January 1, 2019)

Download: http://wordpress.jmcgowan.com/wp/downloads/

(C) 2019 by John F. McGowan, Ph.D.

About Me

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

Video of “Automating Complex Data Analysis” Presentation to the Bay Area SAS Users Group

 

This is an edited video of my presentation on “Automating Complex Data Analysis” to the Bay Area SAS Users Group (BASAS) on August 31, 2017 at Building 42, Genentech in South San Francisco, CA.

The demonstration of the Analyst in a Box prototype starts at 14:10 (14 minutes, 10 seconds). The demo is a video screen capture with high quality audio.

Unfortunately there was some background noise from a party in the adjacent room starting about 12:20 until 14:10 although my voice is understandable.

Updated slides for the presentation are available at: https://goo.gl/Gohw87

You can find out more about the Bay Area SAS Users Group at http://www.basas.com/

Abstract:

Complex data analysis attempts to solve problems with one or more inputs and one or more outputs related by complex mathematical rules, usually a sequence of two or more non-linear functions applied iteratively to the inputs and intermediate computed values. A prominent example is determining the causes and possible treatments for poorly understood diseases such as heart disease, cancer, and autism spectrum disorders where multiple genetic and environmental factors may contribute to the disease and the disease has multiple symptoms and metrics, e.g. blood pressure, heart rate, and heart rate variability.

Another example are macroeconomic models predicting employment levels, inflation, economic growth, foreign exchange rates and other key economic variables for investment decisions, both public and private, from inputs such as government spending, budget deficits, national debt, population growth, immigration, and many other factors.

A third example is speech recognition where a complex non-linear function somehow maps from a simple sequence of audio measurements — the microphone sound pressure levels — to a simple sequence of recognized words: “I’m sorry Dave. I can’t do that.”

State-of-the-art complex data analysis is labor intensive, time consuming, and error prone — requiring highly skilled analysts, often Ph.D.’s or other highly educated professionals, using tools with large libraries of built-in statistical and data analytical methods and tests: SAS, MATLAB, the R statistical programming language and similar tools. Results often take months or even years to produce, are often difficult to reproduce, difficult to present convincingly to non-specialists, difficult to audit for regulatory compliance and investor due diligence, and sometimes simply wrong, especially where the data involves human subjects or human society.

A widely cited report from the McKinsey management consulting firm suggests that the United States may face a shortage of 140,000 to 190,000 such human analysts by 2018: http://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/big-data-the-next-frontier-for-innovation.

This talk discusses the current state-of-the-art in attempts to automate complex data analysis. It discusses widely used tools such as SAS and MATLAB and their current limitations. It discusses what the automation of complex data analysis may look like in the future, possible methods of automating complex data analysis, and problems and pitfalls of automating complex data analysis. The talk will include a demonstration of a prototype system for automating complex data analysis including automated generation of SAS analysis code.

(C) 2017 John F. McGowan, Ph.D.

About the author

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).

 

Machine Learning at Google Event

I attended a “Machine Learning at Google” event at the Google Quad 3 building off Ellis in Mountain View last night (August 23, 2017).  This seemed to be mostly a recruiting event for some or all of Google’s high profile Machine Learning/Deep Learning groups, notably the team responsible for TensorFlow.

Token Good Looking Woman Opens Event
Woman Opens Event

I had no trouble finding the registration table when I arrived and getting my badge.  All the presentations seemed to run on time or nearly on time.  There was free food, a cute bag with Google gewgaws, and plenty of seating (about 280 seats with attendance about 240 I thought).

The event invitation that I received was rather vague and it did not become clear this was a recruiting event until well into the event.  It had the alluring title:

An Exclusive Invite | Machine Learning @ Google

Ooh, exclusive!  Aren’t I special!  Along with 240 other attendees as it turned out.  🙂

Andrew Zaldivar (see below) explicitly called it a recruiting event in the Q&A panel at the end.  It would have been good to know this as I am not looking for a job at Google. That does not mean the event wasn’t interesting to me for other reasons, but Google and other companies should be up front about this.

Although I think the speakers were on a low platform, they weren’t up high enough to see that well, even though I was in the front.  This was particularly true of Jasmine Hsu who was short.  I managed to get one picture of her not fully or mostly obscured by someone’s head.  Probably a higher platform for the presenters would have helped.

A good looking woman who seemed to be some sort of public relations or marketing person opened the event at 6:30 PM.  She went through all the usual event housekeeping and played a slick Madison Avenue style video on the coming wonders of machine learning.  Then she introduced the keynote speaker Ravi Kumar.

Ravi Kumar Keynote
Ravi Kumar Keynote

Ravi was followed by a series of “lightning talks” on machine learning and deep learning at Google by Sandeep Tata, Heng-Tze Cheng, Ian Goodfellow, James Kunz, Jasmine Hsu, and Andrew Zaldivar.

The presentations tended to blur together.  The typical machine learning/deep learning presentation is an extremely complex model that has been fitted to a very large data set.  Giant companies like Google and Facebook have huge proprietary data sets that few others can match.  The presenters tend to be very confident and assert major advances over past methods and often to match or exceed human performance.  It is often impossible to evaluate these claims without access to both the huge data sets and vast computing power.  People who try to duplicate the reported dramatic results  with more modest resources often report failure.

The presentations often avoid the goodness-of-fit statistics, robustness, and overfitting issues that experts in mathematical modeling worry about with such complex models.  A very complex model such as a polynomial with thousands of terms can always fit a data set but it will usually fail to extrapolate outside the data set correctly.  Polynomials, for example, always blow up to plus or minus infinity as the largest power term dominates.

In fact one Google presenter mentioned a “training-server skew” problem where the field data would frequently fail to match the training data  used for the model.  If I understood his comments, this seemed to occur almost every time supposedly for different reasons for each model.  This sounded a lot like the frequent failure of complex models to extrapolate to new data correctly.

Ravi Kumar’s keynote presentation appeared to be a maximum likelihood estimation (MLE) of a complex model of repeat consumption by users: how often, for example, a user will replay the same song or YouTube video.  MLE is not a robust estimation method and it is vulnerable to outliers in the data, almost a given in real data, yet there seemed to be no discussion of this issue in the presentation.

Often when researchers and practitioners from other fields that make heavy use of mathematical modeling such as statistics or physics bring up these issues, the machine learning/deep learning folks either circle the wagons and deny the issues or assert dismissively that they have the issues under control.  Move on, nothing to see here.

Sandeep Tata
Sandeep Tata

Hang Tze
Hang Tze

Ian Goodfellow on Deep Learning Research at Google
Ian Goodfellow on Deep Learning Research at Google

Jasmine Hsu on Robotics and Computer Vision
Jasmine Hsu on Robotics and Computer Vision

James Kunz
James Kunz

Andrew Zaldivar on SPAM Fighting with Machine Learning
Andrew Zaldivar on SPAM Fighting with Machine Learning

Andrew Zaldivar introduced the Q&A panel for which he acted as moderator.  Instead of having audience members take the microphone and ask their questions uncensored as many events do, he read out questions supposedly submitted by e-mail or social media.

Andrew Zaldivar Introduces the Panel
Andrew Zaldivar Introduces the Panel

Q and A Panel
Q and A Panel

The Q&A panel was followed by a reception from 8-9 PM to “meet the speakers.”  It was difficult to see how this would work with about thirty (30) audience members for each presenter.  I did not stay for the reception.

Conclusion

I found the presentations interesting but they did not go into most of the deeper technical questions such as goodness-of-fit, robustness, and overfitting that I would have liked to hear.  I feel Google should have been clearer about the purpose of the event up front.

(C) 2017 John F. McGowan, Ph.D.

About the author

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).