Chapter 3 in the R.M. Dolin book, “Truth and Trust in Crisis.” 2021
It’s late March of 2020 and based on dire predictions from the United Kingdom’s Imperial College of London (UK) and University of Washington (UW) COVID models, the government is announcing an effort to spread the projected number of COVID deaths over a wider timeline so as not to overwhelm death and dying industries; they market their campaign based on the mathematical concept of “flattening the Curve.”[1] Imagine two triangles of different shape having the same area. Suppose one triangle is half as tall as the other, then to have the same area, its base must be wider. In “flattening the curve,” the area under the curve is the number of people projected to die, and the base is time; to spread out the number of deaths over a longer period, the curve’s apex must be lowered or flattened. To flatten a curve, the parameters driving the curve’s rate of change (i.e., slope), must be minimized. In the case of COVID, this means reducing the rate at which the number of deaths increases over time.
The UK and UW academic models are utilized to justify drastic and draconian mitigation measures, such as quarantining healthy individuals and shutting down commerce, except for businesses designated as “essential.” We’re told these mitigation measures will last two, possibly three weeks, and then our lives can return to normal. However, two plus years later, long after the CDC declares the crisis over, many of these mitigation measures are still in effect.
Our inquiry begins with an essay I published titled “Earthquakes, Gypsy Moths, and the President’s Dire Warning.”[2] The essay reviews the government’s prediction that in two weeks, or on April 14th, the spread of COVID will reach an apex and begin to recede. To date, there have been 2,900 COVID deaths reported in America and President Trump is introducing two new members to his White House Coronavirus Task Force Team, medical doctors Deborah Brix and Anthony Fauci. I’ve never met Dr. Fauci[3], but was involved in cleaning up one of his messes in 2005 when 5,000 samples of the deadly virus strain[4] causing over two million deaths worldwide in 1957, were accidentally sent to bio-labs in seventeen countries, including countries hostile to the United States.
While that’s the official story, talk around the homeland security water coolers at the time was that Fauci’s National Institute of Allergy and Infectious Diseases (NIAID)[5] had inadvertently released the unattenuated Spanish Flu virus that killed one-hundred million people. NIAID apparently obtained the unattenuated virus strain by exhuming a WWI solider who had died from Spanish Flu, and we would have no immunity to. If true, this would represent the greatest act of medical malfeasance in human history.
The Brix/Fauci duo announce that based on the academic model projections and their own predictions which are never explained and never held for peer review, the COVID death count in America is about to explode from 2,900 deaths to 250,000 deaths by April 14th, a mere two weeks away. This sudden and unvalidated claim shocks our sensibilities, particularly since in the three previous months less than 3,000 American’s had reportedly died from COVID, which is well within the expected bounds of a normal flu season. Their assertion is so spectacular and unprecedented in the history of viral pandemics it demands skeptical evaluation.
My initial foray into COVID modeling was not to prove or disprove the claims being made by academic models and the Brix/Fauci duo, but to prove the COVID virus is human engineered, which I’m beginning to suspect based on observable data. After all, the only seemingly possible way the death rate could jump from 1,000 deaths per month nationwide to a rate of 500,000 deaths per month, is if the virus is engineered. To put this level of virulence in perspective, the Spanish Flu[6], claimed 675,000 American lives, for an average of 28,125 deaths per month. Since the US population at that time was one-third what it is today, the adjusted rate in terms of our current population would be 84,375 deaths per month. The Spanish Flu remains the deadliest viral pandemic in recorded history, so claiming COVID is six times more virulent seems improbable for a naturally occurring virus.
While brainstorming ways to prove COVID is human engineered, I postulate what would motivate such research. Of course, there is its obvious use as a viral weapon but that doesn’t seem likely since there’s a global moratorium on biological weapons research and the Chinese would never violate that, right? And certainly the U.S. would never engage in such an illegal, dire, and diabolical act. I next consider the possibility that perhaps China developed COVID to deal with their issue of having too many elderly citizens relative to younger adults. While horrendous to consider, it’s nonetheless plausible given the Chinese communists have already demonstrated their willingness to kill over 50 million people during Mao’s Cultural Revolution. Another aspect of COVID causing me to suspect it could be human engineered is that, unlike the Spanish Flu that mostly killed young adults, COVID seems to be targeting the elderly and infirmed.
Then I have an epiphany, there are no dead bats. Whenever an avian pandemic arises, there are dead birds all over the place with photographic proof. When swine flu runs rampant, pigs are put down, again with demonstrative videos and pictures. When equine flu rears its ugly head, horse herds are euthanized, and evidence is everywhere. Here we are, in a pandemic reportedly six times worse than the worst pandemic in recorded history with no evidence that bats are involved.
The more I brainstorm, the more convinced I get that I can’t prove COVID is human engineered; what I can do is prove the null hypothesis[7], which in this case means I can prove COVID behaves like a naturally occurring virus. In science, the null hypothesis is useful for proving the opposite hypothesis of what you really want to investigate. To prove my null hypothesis, we’ll utilize the same Ordinary Differential Equations, applied to naturally occurring things like earthquake behavior, the rate at which machinery wears out, and gypsy moths reproduction. You’re probably wondering what any of this has to do with COVID, and while I fully intend to tell you, we first need a short tutorial on exponential growth and how it can be used to prove COVID either does, or does not, behave like a naturally occuring virus.
You’ve probably heard expressions such as “the stock market’s growing exponentially,” or “the exponential half-life of plutonium-239 is 24,110 years.” Expressions like these describe the rate at which something rapidly changes, and in nature things tend to follow exponential growth and decay patterns. For the past month, I’ve been charting COVID data with linear functions, which are simplest form of mathematical modeling. Exponential functions are a close second in simplicity.
The easiest way to explain an exponential is that it’s a base number raised to a power. You’re probably familiar with the simple exponential equation
which implies the number 2 raised to the x power. If x=2, this expression becomes two-squared, which equates to
You probably remember this from high school algebra, but it’s worth reviewing since it becomes a linchpin in our assessment of government and academic models used throughout the COVID crisis.
The term “exponential growth” refers to a condition in which each time x increments by one value, the function increases by a multiple of its base. As shown in Figure 1, exponential functions start out small then grow rapidly. In our example, f(x)=2x, each time x increments by one, the function doubles because the base is two. So, for x = 1, 2, 3, 4, 5 . . .14,
f(x) = 2, 4, 8, 16, 32, . . . 16,384.
For a simple exponential like f(x)=2x, after only fourteen cycles, f(x) grows from 2 to 16,384. But the unresolved COVID question is, is it possible for COVID to increase from a rate of 1,000 deaths per month to 500,000 deaths per month in fourteen days as the Brix/Fauci duo predict?
Figure 1. Typical Plot of an Exponential Function.
There’s an old parable[8] about a king who asks a craftsman to build him a chessboard. When it comes time to pay, the crafty craftsman asks to be paid with rice, telling the king to pay him one grain of rice for the first square on the chess board, two grains of rice for the second square and so on, doubling the grains for each square. The king, being bad at math, quickly agrees and the first row of payments are 1+2+4+8+16+32+64+128=255 grains of rice.
The king feels 255 grains of rice is trivial but fails to understand that by the time they reach the 64th square of their f(x)=264 exponential model, he’ll owe the craftsman over a trillion grains of rice, which is all the grain in his kingdom. To escape handing over his entire wealth, the cleaver king informs the craftsman that he’ll pay as agreed, but the craftsman must count each grain to ensure he’s paid in full. The craftsman understands his con’s exposed as it’ll take hundreds of years to count a trillion grains of rice, instead, he tells the king to just keep the chessboard as a gift.
This example demonstrates that it’s exponentially possible to model a rate increase from 1,000 COVID deaths per month to 500,000. What remains unanswered is if this rate of growth is practical and supported by evidence? Keep in mind we’re not attempting to prove or disprove government predictions or academic projections, that will come later, we’re attempting to prove or disprove whether COVID is behaving like a naturally occurring virus.
To catapult from our currently reported rate of 1,000 COVID deaths per month to the projected rate 500,000 deaths per month in fourteen days, we need an aggressive exponential. We know our exponential function is of the form, f(x)=ax. In addition, we know x=14 and we know that the Brix/Fauci duo predict 250,000 Americans will die by April 14th. What we don’t know is the value of a. Without getting too mired in math, to get the cumulative number of deaths on this date, we must integrate our function, which results in the equation,
Rather than work through the details for how to solve for a, I’ll just tell you a=2.4097. While on the one hand it seems unlikely the Brix/Fauci duo are correct or the academic models they rely on are accurate, given the world has never encountered such a deadly pandemic, the fact that we demonstrated this outcome is mathematically possible means COVID could be behaving like a naturally occurring virus even if there are no dead bats. However, this does not negate the possibility the virus is human engineered.
Brix/Fauci make their dire prediction citing the UW model, which we’re told is based on an elaborate set of parameters and variables we lay-folks have no hope of understanding. Since we’re too stupid for academic sophistication, let’s instead develop a simple mathematical model using exponentials that can be programed in any spreadsheet using four lines of code. We’ll then use our model to assess the accuracy of not only the UW model’s future projections, but other academic and government models that will emerge to compete with UW.
For months we’ve been told the world’s about to end based on the previously prestigious UK pandemic model, however, the problem is that to date, most Americans aren’t getting on board. Sure, we hoarded paper towels and toilet paper, but was it because we feared the end of days or because we didn’t want to listen to Doris next door saying, “I told you so,” when she found us foraging for a few squares of Charmin after taco Tuesday?
To facilitate us getting onboard with the COVID program, the White House announces on April 2, twelve days before Armageddon, that 250,000 Americans are going to die from COVID in the next twelve days. So dire is this prediction we’ll do anything to flatten the curve. Sure Dr. Brix seems to come out of nowhere with limited credentials, but the scarfs are reassuring. While Dr. Fauci has a track record of screwing things up, including the greatest act of medical malfeasance in the history of humanity, President Trump has chosen these fine folks to lead us through the darkness and he’s the self-proclaimed master of recognizing talent. So, we implicitly trust Brix and Fauci are the two best and brightest people within government to lead this crisis.
Our prize package of things we’ll submit to to “flatten the curve,” are things that have never been done before in the history of pandemics, like quarantining a healthy population and shutting down a nation’s economy. But the government, with the help of media and medical professionals, convince us that if COVID’s going to reach a dystopian death rate of 500,000 deaths per month by April 14th, and if the virus is naturally occurring, it requires equally proportioned mitigation measures, at least that’s our thinking before running some numbers. Figure 3.1, shows the COVID tracking numbers through April 2, using data from the WHO and the CDC.
Figure 3.1. WHO and CDC COVID Numbers Through April 2, 2020.
According to CDC reports, on April 1, thirteen days before Armageddon, 730 Americans have died from COVID. While tragic, this is far below what’s required to reach 250,000 deaths in thirteen days. On April 2nd, the number of American deaths reportedly attributed to COVID rose 486 for a total of 1,216 cumulative, and while tragically significant, this is far short of what the Brix/Fauci duo forecast.
To get from reported 3,000 COVID deaths in the U.S. on April 1st to the government predicted 250,000 deaths by April 14th, the number of deaths would have to increase by factor of 2.4097 each day in our exponential model. However, the increase in the number of deaths from April 1st to April 2nd was 1.31. This means one of three things, either the Brix/Fauci prediction is wrong, the CDC death data is wrong, or the mitigation measures taken at the federal and state levels to “flatten the curve,” are working. Since at this stage we have no reason to question the academic models, the wisdom and integrity of the Brix/Fauci duo, or CDC data, the curve must be flattening – way to go Team USA!
There is, however, another possibility that probably should be explored, namely, that the “curve” never needed flattening in the first place because it was never reasonable to assert that in fourteen days the country would go from 1,000 COVID deaths per month to half a million. Faced with an increasing sense that government predictions based on academic projections are wrong, I begin compiling collaborating, albeit ancillary, evidence. What better place to start than China where this confusing crisis began, and of course in Italy where the full force of COVID is right now running rampant.
While still trusting government leaders and believing in the truth of academic models, we have no reason to suspect COVID numbers reported by the CDC are flawed, even though it seems the Chinese, as well as other repressive countries, are underreporting. The Peoples Republic of China (PRC) reports the number of deaths in China due to COVID are 2,500 as of April 2. Meanwhile, Radio Free China (RFC) estimates 48,000 have died. Rather than argue who’s right, let’s run some numbers to make our own mathematical determination.
For that we’ll generate a statistical inference model utilizing data from Italy as our sample population. We use Italian data because Italy is further into their crisis than any other country. Also, Italy seems to represent a worst case COVID scenario amongst truthfully reporting nations, and one would expect with China’s overcrowding, poor standard of living, and lack of access to quality healthcare, that their numbers would be as bad as, if not worse, than Italy’s.
Our analysis begins by determining the number of people in China, which is 1,437,843,661 (~1.44 billion). The Italian infection rate is currently 0.16% of their population with 11.3% of those infected dying. For comparison, U.S. rates are currently 0.067% and 2.37% respectively, so we are currently fairing far better than Italy. If the PRC experiences the same infection and death rates as Italy, then China would have 2,232,214 infections with 256,343 deaths. In other words, two orders of magnitude more deaths than officially reported. This suggests that official PRC claims are likely Chinese misinformation.
One thing you’ll come to appreciate as we work through the COVID crisis is that every nation has a narrative. Some narratives require underreporting while other narratives can only be achieved via over-reporting. At this point we still implicitly trust both academic model projections and government predictions, and because of that have compliantly gone along with whatever mitigation measures are being imposed because we believe Armageddon is creeping up our unlit stairway. However, assessing COVID data from China causes skepticism in all sources of data, including the CDC. So, let’s dip our toes into the data integrity pond and see where it takes us.
When we apply Italian statistics to a Chinese population the estimated number of deaths is a thousand times higher than official government reporting. We can apply the same statistical model to the U.S., whose population is 320 million, or 22% of China’s population. This means for the U.S. to get to 250k deaths, we’d have to experience a COVID crisis four times worse than what Italy is experiencing. This seems unlikely since the U.S. has good health care, a government that’s been extremely proactive, and a healthy population (we hope). Applying Italian COVID statistics to the U.S. population results in 30,500 American deaths from COVID by April 14th which while a more likely outcome stills seems high.
There you have it, our first model projection. While academic projections and government predictions assert that 250,000 Americans die by April 14th, our analysis suggests a more likely estimate is 30,500. It may seem difficult to believe we could be right when it’s so far below those of “trusted experts,” but that’s the beauty of science, being right doesn’t mean being popular. Science requires the courage to stand confidently behind your conclusions, solid in a belief you laid out a logically consistent argument supported by defensible mathematics. Our simple statistical model is based on empirical evidence from Italy, which we assume is the best indicator of our likely future.
An unintended consequence of efforts to prove COVID is human engineered is that we have inadvertently placed ourselves on a new trajectory, one of assessing our impending Armageddon. Our new trajectory involves understanding what’s going on with the academic projections and government predictions being utilized as justification for enacting extreme mitigation measures; estimates that don’t seem grounded in logical consistency, solid science, or even rational thought. We’ll stay on this trajectory at least until April 14th, which is when, according to our crisis “experts,” 250,000 Americans will have died from COVID.
If our statistical model is correct, only 30,500 Americans will have died by April 14th and Armageddon will have been minimized. While this is still an uncomfortably large number when you remember we’re predicting American deaths, considering the alternative Brix/Fauci scenario, it’s the outcome we should be rooting for. If we are right, it opens new lines of inquiry, namely was the nation diverted away from Armageddon due to mitigation measures, or were the academic models so fallacious that we were never on track to realize 500,000 deaths per month?
Remember, it’s only April 2nd, and we have a long terrorizing twelve-day journey ahead of us. If nothing else, through the power of math there’s a small cohort of ad hoc scientists postulating that academic models and government predictions are wrong.
[1] Flattening the Curve for COVID-19: What Does It Mean and How Can You Help? (uofmhealth.org)
[2] http://rmdolin.com/qd-17/
[3] https://www.newscientist.com/article/dn7261-pandemic-causing-asian-flu-accidentally-released/
[4] https://www.nytimes.com/2005/04/13/us/deadly-1957-strain-of-flu-is-found-in-labtest-kits.html
[5] https://www.niaid.nih.gov/.
[6] https://www.healthaffairs.org/do/10.1377/forefront.20210329.51293/
[7] https://corporatefinanceinstitute.com/resources/knowledge/other/null-hypothesis-2/
[8] https://purposefocuscommitment.com/wisdom-story-king-con-artist-chessboard-rice/
