There are several mathematical axioms that the COVID-19 pandemic has validated, but the most consequential of these are:
1) For any group of people (# of people = n), as n approaches infinity, that group’s capacity for stupidity approaches infinity.
2) Despite the fact that we’re >200 years removed from The Enlightenment, people still prefer…
…emotional reasoning grounded in anecdotes to data analysis grounded in mathematics and statistics.
Conducting simplistic arithmetic analysis is rarely advised, but if we add the asymptotic caveats of the current raw mortality numerator and denominator limits, some reasonable estimations can be produced:
A raw mortality rate consists of two numbers – a numerator (x) and a denominator (y). The numerator is the number of people who have died from COVID-19 and the denominator is the number of people who have contracted COVID-19.
Currently, anyone who has contracted COVID-19 and dies from COVID-19 is counted as a COVID-19 death, regardless of co-morbidities or risk factors.
Therefore, the numerator is heavily biased towards overcounting the number of people that die from COVID-19 because there are no controls for other variables correlated with deaths.
With respect to the denominator, we have NO IDEA how many people have actually contracted COVID-19.
Due to lack of testing and the fact that the vast majority of people who contract COVID-19 don’t experience any symptoms or only experience mild symptoms, the denominator is heavily biased towards undercounting the number of people that have contracted the virus.
The evidence isn't definitive, but it is suggestive...
As of April 13, 2020, Johns Hopkins University reported 557,590 confirmed cases (denominator of raw mortality rate) of COVID-19 and 22,109 deaths (numerator of raw mortality rate) associated with COVID-19.
The 2019 U.S. census reported a population of 328.2 million people. The confirmed cases (denominator) accounts for .17% of the U.S. population and the deaths associated with COVID-19 accounts for .0067% of America’s population.
According to the CDC, 647,000 Americans die from heart disease every year, which accounts for .2% of the population. While there tragically will be more people that WILL die from COVID-19 (numerator and overall rate will increase), the raw mortality rate of heart disease is not plagued by the numerator/denominator biases of the COVID-19 epidemic. Heart disease is not a communicable disease – there are no people who die from heart disease who do not experience heart-related symptoms.
Because of this fact, the raw mortality rate of heart disease is FAR MORE accurate than COVID-19’s rate.
Stanford University epidemiologist and professsor medicine, Dr. John Ioannidis and his research team recently estimated that, if you’re under 65 and in good health, the risk of dying from COVID-19 is roughly equivalent to the probability of dying in a car accident if you drive 9 to 415 miles per day (depending on location with Germany and NYC being the min/max locations, respectively).
Dr. Ioannidis’ research has highlighted the “major gaps” of understanding regarding (i) the lethality of COVID-19, (ii) the number of people that COVID-19 has infected, (iii) the number of people that will be infected, and (iv) the long-term impact of the virus and the efficacy of different proposed policy measures.
Dr. Ioannidis criticized the World Health Organization’s March raw mortality estimate of 3.4 percent.
One of the reasons for his concern was the fatality rate of the Diamond Princess cruise ship, which only experienced a 19% infection rate (not mortality rate). Out of those infected, only 1 percent died. Furthermore, the median age of the passengers and crew was 65. The combination of COVID-19’s “strong age gradient” and the high percentage of seniors on the ship made Dr. Ioannidis skeptical of the WHO’s high estimate. He pegged the estimate at between .05 percent to 1 percent.
“…I think that the estimates are exaggerated. And I think that there is a risk of really making some fundamental decisions about the structure of our civilization, of our society, of our future, that may not be appropriate… Flattening the curve to avoid overwhelming the health system is conceptually sound—in theory…
A visual that has become viral in media and social media shows how flattening the curve reduces the volume of the epidemic that is above the threshold of what the health system can handle at any moment.
Yet if the health system does become overwhelmed, the majority of the extra deaths may not be due to coronavirus but to other common diseases and conditions such as heart attacks, strokes, trauma, bleeding, and the like that are not adequately treated.
If the level of the epidemic does overwhelm the health system and extreme measures have only modest effectiveness, then flattening the curve may make things worse. Instead of being overwhelmed during a short, acute phase, the health system will remain overwhelmed for a more protracted period. That’s another reason we need data about the exact level of the epidemic activity.”
On Friday, Dr. Ioannidis and his team released a preliminary study, which lowered his initial estimate even further.
The study analyzed the antibodies of 3,300 residents of Santa Clara County, California. When the study was conducted, there were only about 1,000 known cases in the entire country. 2.5% to 4.2% of the 3,300 residents were estimated to have COVID-19 antibodies. When extrapolated, that translates to 50 to 85 times as high as the total number of COVID-19 cases that were identified at the time.
When this antibody rate is analyzed with the number of known cases and confirmed deaths, the estimated COVID-19 mortality rate suggested by the study is between .12% and .2%, which is significantly close to the mortality rate of the seasonal flu (but still higher).