Chapter 8 in the R.M. Dolin book, “Truth and Trust in Crisis,” 2021
This chapter spans mid-May to mid-July in year one of our COVID crisis. We assess four expert models that made May and June projections, evaluate their performance relative to our model, and determine if our bold June 7th prediction materializes. If our end-of-the-epidemic prediction is confirmed via multiple validations, we can declare the COVID epidemic over. However, don’t be surprised when our primary source of validation runs counter to prescribed narratives and is banned, buried, and censored; as am I about to be, I’m sorry to say.
May 17, 2020: The government declared COVID’s growth stopped on April 14th and has since been in decline, which is represented by the blue curve in Figure 8.1. Since then, the CDC publishes daily death data that follows a steep but steady decline, (red curve). But then on May 15th, something oddly interesting happens, the death rate suddenly reverses course, which generally doesn’t happen in nature once decay begins. It may just be a coincidence or a reporting anomaly, but it happens to coincide with Dr. Fauci’s Senate testimony where COVID’s politicalization is on full display; at least some senators are starting to get suspicious.
Figure 8.1. COVID Death Data with an End of Crisis Projection.
The CDC has a history of sensationalizing data to align with political narratives. If you’re inclined to believe CDC COVID data can’t be corrupted, in Minnesota, getting hit by a bus is considered COVID caused, in Florida, motorcycle crashes are no longer vehicle accidents, they’re COVID cases, at the VA, suicide is now a COVID calamity, and in Colorado alcohol poisoning[1] is considered a form of COVID. A Denver resident whose blood-alcohol content was found to be seven times over the legal limit (0.55%), is initially declared dead by alcohol poisoning but the Colorado Coroner’s office[2], overrules this assessment asserting instead that the man died from COVID. Sadly, this is not Colorado’s first case of COVID corruption causing Governor Jared Polis[3] to push back against CDC compiled data. In April, Colorado health officials declare three nursing home fatalities as COVID deaths, even though attending physicians rule they’re unrelated to COVID. These kind of data shenanigans lead Dr. Brix to recently declare “There is nothing from the CDC that I can trust.”
New York and Pennsylvania admit to overcounting COVID deaths by over 50%. Other states that have been accused of fudging COVID death data include California, Minnesota, and Washington. It may be coincidence, but a correlation is emerging between states that falsely inflate COVID death data and states having Democratic governors. With so many non-COVID related calamities now on the list of COVID caused we’re on track to soon have all deaths be COVID caused.
I applaud Governor Polis for revising his state’s COVID death count downward 24%, but unfortunately, other states are less forthcoming. This begs the question, if lock-down states are guilty of intentionally overcounting, are free states equally guilty of undercounting? Is each state creating unique fiction to suit whatever narrative their attempting to market, and if so, what hope do scientists have of using data to make any sense of this crisis? Unless that’s the goal of our political purgatory, to obfuscate the data to the point credible postmortems are not possible thereby absolving them of their sins; the potential for conspiracy theories abounds.
What politicians, academics, medical professionals, and the media don’t understand though, is truth has a way of permeating obfuscation. A technique often employed in science while trying to prove one thing is to look for evidence somewhere else. For example, let’s do an end-run around the government-controlled CDC to wrap your mind around the fact that the U.S. has 4% of the world’s population but according to the CDC, accounts for 29% of global COVID deaths. Raise your hand if you think this seems plausible given our general state of health, high standard of living, and seemingly enviable healthcare system.
According to the IndexMundi[4] U.S. Deaths Clock, their pre-COVID estimate was that 165,000 people would die in New York from things like heart attack, cancer, flu, and of course, murder in 2020, which translates to 452 people per day. As of May 17th, 62,200 New Yorkers have died from all calamities including COVID, while the expected pre-COVID number is 62,314. This means that even with the highly hyped COVID crisis, New York has only experienced an increase of 114 deaths during COVID. Watching the New York governor’s daily briefings suggest both he and the willful media are not only misleading us, they’re also intentionally causing widespread worry and hysteria.
Throughout this crisis, expert models have consistently been outrageously wrong, yet federal and state officials continue utilizing these inaccurate projections for policy making, which defies all reasonable and responsible logic. The media continues to legitimize these marginal models because it permits sensationalizing dystopian despair, which sells newspapers and garners online viewers. The star student in academia’s rush to soil themselves is the University of Washington (UW), whose model is devoid of sound science. The fallacy of the UW model is that it’s developed by medical doctors rather than scientists trained in data analysis and interpretation.
Sadly, UW is not the only campus club in the fray as the University of Pennsylvania, Wharton School of Business (Penn),[5] has an equally unscientific model now projecting 310,282 COVID deaths by the end of June. The ridiculousness of a COVID model coming from a business school stands on its own, but what pushes this to the absurd is both the government and media legitimize their projection because it aligns with their prescribed narratives.
The fine folks at Penn, based on years of business school studies, have estimated there will be 117,000 COVID caused deaths from May 1st to the end June if lockdowns continue, which translates to 2,187 people per day. Their model further asserts that if lockdowns end, 350,000 people die from COVID, which is 8,161 people per day. To put these numbers in perspective, at the apex of the COVID crisis, there were 4,900 COVID deaths in one day and the average throughout this crisis is 1,128 people per day dying from something the CDC and medical professionals claim is COVID caused, like getting hit by a bus.
This means the upper range of the Penn projection is twice what the COVID crisis experienced at its peak and seven times higher than the average daily death rate. If we are to believe Penn, May and June is going to be significantly worse than anything we’ve experienced so far, and given we’re mostly through a relatively mild May, they’re predicting June to be wildly catastrophic is contrary to prevailing evidence, which leaves me questioning whether Penn or UW faculty ever lecture students on the need to validate models with either first principal calculations or empirical evidence. Just because you successfully compile your software program and get an answer when you execute the code, it is not indicative of the answer being correct or even reasonable.
Let’s compare the Penn and UW models by assuming conservatively the average projected Penn outcome results in 201,794 COVID deaths between now and the end of June. This makes their cumulative death count at the end of June 296,523. The UW model projects a cumulative death count at the end of June of 172,872. For the Penn model to be valid, 5,174 must die from COVID each day, and for the UW model to be valid, 2,004 Americans must die each day. Both projections are in stark contrast to CDC reported death rates that continue to trend downward.
Our model predicts 98,000 cumulative COVID deaths by June 30th, and we are still maintaining COVID will cease to be an epidemic on June 7th. Our prediction is significantly less than either academic model, but then again, we are not bound by the narratives of funded research.
May 24, 2020: The CDC is reporting the lowest seven day running COVID death average since the April 14th apex. We’ve gone from a high of 2,579 deaths per day in April to a current low of 1,034 deaths per day. As Figure 8.2 indicates, our steady descent from the apex is slowing, which is expected as we approach the tail region of our exponential curve. We boldly predicted in early May that the COVID epidemic would end on June 7th, and indications are we’re on track to hit our milestone.
Figure 8.2. Exponential Model Projection as of 24 May.
The pillars of academic excellence at Penn and UW, however, paint a distinctly different dystopian picture. Penn projects 4,007 COVID deaths per day in June, while UW projects 2,336 deaths per day. Our model predicts daily death rates decline until June 7th, when the rate drops below the CDC threshold for declaring an epidemic over. Intuitively this seems like an implausible prediction, but I urge you to trust the math and our model that has been spot-on thus far. My frustration with the nation’s STEM universities who have chosen to abdicate their expertise to business schools and medical departments so as not to risk lucrative research contracts with companies profiting from COVID, continues unabated. Fortunately, you and I are blue collar enough to have the temerity to operate in a world without such constraints.
May 31: Having reached the end of May, it’s time to check in on the academic models to see how well they performed and compare them to our simple exponential model. At the beginning of May, the CDC reported 63,023 COVID caused deaths and by the end of May, that number climbed to 103,153, for a May-month delta of 40,130, which equates to an average of 1,295 deaths per day. The seven-day rolling average number of deaths per day at the start of the month was 1,852, while at the end of May, it dropped to 1,015. It is difficult not to conclude based on these numbers, that the crisis is abating.
Table 8.1. Assessing COVID Model Performance for May.
As Table 8.1 highlights, the UW model predicted 138,586 COVID deaths by the end of May reaching 214,148 by the end of June. The Penn model projected 190,072 COVID deaths by the end of May reaching 310,282 by the end of June. The government’s Federal Emergency Management Agency (FEMA) projected 156,023 COVID deaths by the end of May, reaching 276,233 by the end of June. All three models over-projected COVID deaths in May by ~50% and what’s even more disconcerting is they’re collectively predicting a significantly worsening crisis in June, even though current data indicates the crisis is abating.
Meanwhile, our model continues to be spot-on with an error rate of 8.98% in May. We project that only 22,500 people will die from COVID in June, which makes our projection between 5 and 9 times lower than federal and academic models, which is astonishing given we utilize a simple four-line program running on a $300 laptop.
June 7, 2020: At the beginning of May, our model estimated that the COVID epidemic would end on June 7th, which given media hysteria and draconian government mandates at that time seemed outrageous. Well, here we are at D-day, ready to see if we landed on the right beach. The UW model projects an average of 3,700 COVID deaths per day in June, the Penn model 6,904 COVID deaths per day, and the federal model 5,770 COVID deaths per day. One thing’s very clear, the “experts” don’t expect June to be pleasant or the COVID crisis to be ending anytime soon.
Meanwhile, our simple model projects 750 COVID deaths per day in June. As a benchmark, the CDC’s current rolling seven-day average is 1,015 COVID deaths per day. When you factor in that the CDC over counts by at least 25%, the adjusted current death rate could be as low as 761, making our model spot on once again.
Brix/Fauci and members of the media don’t seem to realize that the numbers reported by the CDC are a combination of phenomena, influenza, and COVID (PIC) deaths. In other words, all three causes of death are reported by the CDC as one number. In addition, the daily death count for phenomena and influenza during a normal flu season is around 750, which means that once the PIC number drops below 750 per day, we can conclude that the number of COVID caused deaths is having minimal impact and the crisis has abated.
Table 8.2. Actual Versus Expected Deaths Through June 7th.
Confirmation of our model predictions is one validation the COVID crisis has ended, but for further confirmation, we make the following observation: the only reasonable way to untangle the mess medical professionals have made by intentionally misrepresenting COVID deaths is to look at actuarial data. The pre-COVID number of Americans expected to die from all causes by June 7th is 1,244,135 as shown in Table 8.2. The number of Americans who did die from all causes including COVID by June 7th is 1,242,783, which means 1,352 fewer Americans have died thus far this year than were expected to die pre-COVID. It seems crazy to say, but the data suggests COVID caused fewer Americans to die so far this year than expected, which is flat out astonishing; we will work through why this is happening latter, and the reason will be equally astonishing.
The COVID crisis has unfortunately become as much political as epidemiological, and the crisis cannot be declared over until politicians have an exit strategy shielding accusations of mismanagement and over-reach. Currently forty-two states have reopened with no reported spike in COVID deaths. In New York City, 400,000 people are returning to work[6], which indicates New York politicians know the crisis is over. Both political parties got necessary cover to divert attention away from crisis mismanagement. Republicans got treason hearings to intoxicate their base with the possibility the Obama administration was involved in a failed coup. Meanwhile, Democrats got riots to remind their base who’s got their backs come November.
Medically the crisis must be in remission as 1,000 healthcare professionals recently signed a letter[7] advocating violent riots should not be shutdown using COVID concerns as an excuse. They argue that the needs of a small segment of society to riot trumps the rights of the larger population to remain safe. Since in theory, medical professions follow an oath to “do no harm”, they can only reach that assessment if the crisis is in full remission.
The media struggles to make COVID matter after working relentlessly to maintain hysteria; sometimes on their own, and other times with the help of the medical, academic, and political professions. Media venues dutifully follow the profit models of their mentors, William Hearst and Joseph Pulitzer[8], who advocated sensationalized journalism, even if forced to make stuff up. But try as they might, the media can’t maintain the ruse. The very nature of media’s exhausting attempts to scare us with increasingly absurd COVID boogeymen is evidence the crisis is abating.
Pundits predicted 7 million jobs would be lost in May but instead, 2.5 million jobs were added to the economy.[9] It appears the peasant class has had enough of being scared and manipulated, as 80% of all small businesses are now reopened, some after being jailed by overzealous governors. The greatest fear any oligarchy has is open disobedience, so once Americans again taste the refreshing newness of freedom, state and federal politicians are quickly acquiescing.
The Markets have also spoken. Despite blue state governor’s attempts to stall recovery, American confidence is returning with the stock market soaring upward 1,000 points in one day, and up several thousand points over the past couple weeks.
While the words and deeds of politicians, medical professionals, the media, the markets, and the masses are fine and good, as scientists, we need quantitative evidence to support those qualitative observations. For that we look at how the CDC declares an epidemic over. An epidemic ends[10] when “the number of new reported illnesses [/deaths] drops back to the number normally expected.” The CDC declares the end of an influenza outbreak when the number of infections or deaths, depending on the metric you are measuring, drops to a level at or below the number for endemic influenza. For example, the death rate for an average flu season[11] in the U.S. is 36,000 per year, which means when an influenza epidemic drops below 36,000 deaths per year the outbreak is over.
July 14, 2020: The CDC has just announced that based on June COVID death count data, the epidemic is over[12]. While the CDC did not point to a specific date, they indicated that “Based on death certificate data, the percentage of deaths attributed to pneumonia, influenza or COVID-19 (PIC) decreased from 9.0% during week 25 to 5.9% during week 26, representing the tenth week of a declining percentage of deaths[13] due to PIC.” The baseline death rate for COVID and other diseases such as influenza and pneumonia typically range from 5% to 7% at the height of flu season. According to the CDC, the COVID epidemic is over when the death rate drops below 5.9%, which was achieved in early June, validating our projection, in other words, we courageously came ashore on the right beach on COVID D-day.
It’s worth noting that the CDC reference cited above declaring the COVID epidemic over in June, was pulled from the CDC website within days of being posted. Keep that bit of information in your back pocket as we continue our march toward the November election.
For further evidence the COVID epidemic ended on June 7th, let’s come at the issue from another angle. According to the CDC and WHO, at the start of June, the global percentage of confirmed COVID cases resulting in death[14] was 6.12%. By the end of June that rate dropped to 4.88%, which represents a significant increase in survivability (or decrease in mortality). The percentage of confirmed COVID cases resulting in death within the U.S. at the start of June was 5.87% and dropped to 4.79% by the end of the month. The takeaways are
- The COVID survival rate significantly improved in June.
- The U.S. is currently performing better than the rest of the world in terms of COVID survivability, which is the opposite of what’s being reported.
Advocates of government and media narratives assert the increase in COVID survivability is due to more people being tested, which is true, however, these same people ridiculed President Trump when he made the same assertion. Mathematically, survivability can be computed as “s=n/d”. Where n is the number of COVID deaths and d is the number of people tested. Increasing the number of people tested increases the value of d, while the number of deaths is not affected by increased testing. So, while n is fixed, d increases, which results in s getting smaller, which translates to survivability increasing but has no correlation with the COVID death rate.
According to the Mundi Index[15], through June, the country has experienced 4,128 fewer deaths than pre-COVID expectations. Given that 1.44 million Americans have died so far this year, to be below the pre-COVID expectation by over four thousand is not only remarkable, it indicates the overall impact of COVID on U.S. deaths thus far is astonishingly minimal. Just as significant, the delta between the Mundi projection and actual deaths remained constant in June. These two outcomes seem shocking given government and media hysteria, but data does not possess narratives.
While government and media focus on infection rates to promote hysteria, it’s a false flag, as infection rates are correlated to the number of people being tested. The COVID death rate is the only metric that matters, and in June, not only did death rates remain constant, but hospitalizations also remained low[16] at 98 patients per 100,000 infected.
If the previously discussed Stanford study is to be believed, at the end of June, 71% of the country has been infected, which means we are entering herd immunity. Rather than celebrate this milestone though, “experts,” like Dr. Fucci[17], liberal governors, and the media[18] intend to scare you into thinking a cataclysmic second wave is descending, however, the inconvenient truth is that the data does not support this assertion.
Table 8.3. June Month-End COVID Model Performance Results.
Every month since March, we’ve compared our simple COVID model against sophisticated government and academic models, and each month, our model has been spot-on, while their models have been hyperbolically wrong. In mid-June Harvard University joined the fraternity of non-STEM schools wading into the STEM swim lane; not with a predictive model, but with a model based on linear extrapolation. Recall from figure 8.2 that by June our exponential model enters into the “tail” part of the decay, which means the rate of change is essentially flat or linear as was shown in figure 8.2.
In other words, extrapolating linear death rates using linear extrapolation is underwhelming. You can think of the Harvard model as a connect-the-dots graph you used to play with in kindergarten, their model could not have been applied during the February to May timeframe because COVID was not behaving linearly, but once we entered the tail region of COVID’s exponential decay in late May a linear model is applicable. You can think of the Harvard model as a rolling average useful for independently validating our exponential model.
Table 8.3 depicts how each of the expert models compared to June CDC data. Consistent with past performance, the government and academic models relied on for policy planning and to promote hysteria, were off by as much as 60% in June. Our model, however, which has been on target since March, was off 3%. Given all the uncertainties involved in something like a nationwide epidemic, 3% error is pretty much spot-on.
The Harvard extrapolation was off by 4%. Their simple analysis has utility in validating our earlier assertion that there was no COVID surge in June. The June performance of the other government and academic models is heartbreakingly disconcerting because it reveals that in times of crisis Americans cannot count on “experts” to tell the truth, and we cannot rely on politicians to cast aside adolescent pettiness and make policy based on the truth. In addition, Americans cannot trust the media to responsibly uncover and report the truth. Our simple exponential model demonstrates that finding truth within the COVID crisis is not really all that hard, all we needed was some high school algebra, a four-line spreadsheet program, and a $300 laptop.
Raise your hand if you believed back in April our simple program’s outrageous projection when powerful government and academic models were promoting dystopian despair. I’ll admit I was a bit unsettled, but knew our model was good, our math consistent, and our evidence sound, so as scientists we pitched our flag where the math took us, devoid of political and profit motivated narratives.
Of course, we’d be naive to believe that just because our model has been validated multiple ways using vastly different approaches, that pre-COVID life as we once knew it is going to suddenly return. We’d be even more naive to believe that the political and profit motivative narratives can allow COVID to end just because our science and CDC says so. Instead, we are about to enter a new phase of the COVID crisis, a phase so macabre science and sound logic may not save us, brothers and sisters, fellow travelers, welcome to the politics of crisis in a campaign season.
[1] Colorado man died of alcohol poisoning, but death was later blamed on coronavirus: report | Fox News
[2] Coroner’s office disputes Colorado coronavirus death claim, says victim died from alcohol poisoning | Washington Examiner
[3] Colorado Gov. Polis pushes back against CDC’s coronavirus death counts | Fox News.
[4] United States Deaths Clock – IndexMundi
[5] Coronavirus Latest: New University Of Penn Model Predicts 350,000 Deaths By End Of June If All States Fully Reopen – CBS Philly (cbslocal.com)
[6] New York City could see 400,000 workers return next month in first phase of a long recovery – POLITICO
[7] Over 1,000 health professionals sign a letter saying, Don’t shut down protests using coronavirus concerns as an excuse – CNN
[8] Yellow Journalism: The “Fake News” of the 19th Century – The Public Domain Review
[9] May jobs report: Unemployment rate comes in at 13.3%, better than expected (cnbc.com)
[10] Step 7: Decide an Outbreak is Over | Foodborne Outbreaks | Food Safety | CDC
[11] This Is How Many People Die From the Flu Each Year, According to the CDC | Health.com
[12] CDC: 10-Week Decline In COVID Deaths Means Possible Epidemic End (thefederalist.com)
[13] CDC: Covid Death Rate ‘Currently At The Epidemic Threshold’ | The Daily Wire
[14] coronavirus statistics – Search (bing.com)
[15] United States Deaths Clock – IndexMundi
[16] COVID Data Tracker Weekly Review | CDC
[17] Fauci: COVID-19 cases could swell to 100,000 a day if U.S. doesn’t control virus (nbcnews.com)
[18] Second wave of coronavirus hits US after state reopenings (nypost.com)
