- CFR = ND (Number of Deaths) / TI (Total Infected)
- TI = USP (US Population) / IR (Infection Rate)
- IR = TP (Total Positive) / TT (Total Tested)
- ND, TP & TT are from the CDC & Covid Tracking Project
- Long formula: CFR = ND / ((TP/TT) x UPS)
- 5/15/20 CFR is 0.18% | 85,974 / ((1,510,924 / 10,217,573) x 331,000,000)
- 5/25/20 CFR is 0.24 | 91,941 / ((1,634,921 / 14,163,915) x 331,000,000) = 91,941 / (.12 x 331,000,000) = 91,941 x 38,206,869 =
I wanted to do the math on the CFR (case fatality rate) using data from the CDC & Covid Tracking Project using high school math. These numbers are as of 5/15/20.
Here’s what I did and what I got. This is the acronym/initial decipher:
- CFR Case Fatality Rate
- ND Number of Deaths
- TP Total Positives (Total Tested & POSITIVE)
- TI Total INFECTED
- TT Total TESTED
- IR Infection Rate
- USP U.S. Population
- CFR Case Fatality Rate
- CMR Crude Mortality Rate
So, in figuring the CFR, the numerator is always ND* which was 85,974 on 5/15/20.
The denominator has a significant effect on your CFR. Despite most of our first instincts, you do not divide by just TP (positives) or the entire population (discussed below). You divide by TI (Total # Infected).
#1. INFECTION FATALITY RATE
THIS IS THE CORRECT METHOD TO FIGURE THE FATALITY RATE. There are two other methods people use (both discussed below), but both give an incorrect result: one much lower, one much higher.
To CORRECTLY figure the fatality rate of a disease, you take ND (the number of deaths) and divide by TI (total infected). ND / TI = CFR
Finding TI is tricky. Since we never test an entire population, we have to calculate TI by extrapolating the testing data. Once you have tested a lot of people (as we have now), you can make a close estimate of how many of the untested are infected.
To do this, you take TP (positives) divided by TT (total tested). This gives us an IR (Infection Rate).
- TP (positives) = 1,510,924
- TT (tested) = 10,217,573
- TP / TT = 1,510,024 / 10,217,573 = IR (infection rate) of .14778695 or 15%
Now we extrapolate that to the US population: USP x IR.
Thus: 330,756,000 x .15 = 49,613,400 (TI)
TI is the projected number of Americans who are or will become infected with CV. If we tested the entire USP, this is the number of positives we would come up. After 10 million tests, this should be a pretty accurate projection.
Now we have our numerator (ND) and denominator (TI) and can calculate the CFR:
- ND = 85,974
- TI = 49,613,400
- ND / TI = 85,974 / 49,613,400 = CFR of .00173287 or 0.18%
This is the number of those infected who will die. That translates to 170 per 100,000 patients.
This reflects your chances of dying of the disease once you are infected. Remember that this is inflated because of the way the CDC is counting deaths*. Plus not everybody will be infected and the majority of the deaths are in the aged/ill population just like flu/pneumonia.
#2 CMR or TOTAL POPULATION MORTALITY RATE
The CMR (Crude Mortality Rate) calculates your chances of dying of CV simply because you live in the US. You figure this by dividing ND (number of dead) by USP (U.S. Population).
- 5/15/20 ND / USP = 85,974 / 330,756,000 = .00025 or .025%
- 5/25/20 = 91,941 / 331,000,000 = .027%
It’s higher than the flu** (.019%), but remember that CV deaths have been inflated.*
We should also correct these figures for age and time, but I haven’t because it’s more than my high school math will allow me to do confidently. (Neither have the fear-mongers accounted for these factors.) Nevertheless, it has been well established that CV — like flu & pneumonia*** — is most dangerous for the old and sick.
Keep in mind that, once you are 65yo, your overall chances of dying this year are 1600 in 100,000. Catching CV will increase that by about 10%. Those numbers are for people who are NOT healthy.
STAY HEALTHY, MY FRIENDS!!!
#3 CFR or SCARY FATALITY RATE
This method uses only TP (positives) as the denominator and dramatically exaggerates the deadliness of the disease. It appears that this method is used by fear-mongers: media and the professionals at the White House, along with some websites like covidusa.net.
Because TP (positives) is a much smaller number than TI (total infected), it renders a much higher fatality rate. This ONLY tells you fatality among CONFIRMED positives, not fatality among USP (total population) or TI (total infected).
- ND = 85,974
- TP = 1,420,299
- ND / TP = 85,974 / 1,420,299 = .060632 or 6.1%
Yep, that’s scary: 6,100 per 100,000 Americans. Holy crap. Good thing it’s not an accurate prediction. It’s simply a useful distortion if you want to scare the hell out of people.
By the way, that’s approximately your chances of dying this year if you are between 80 and 81yo in average health.
If you scroll down the page at covidusa.net to just under the alarming 6.1%, you’ll see a disclaimer that explains which denominator they are using. Why give a disclaimer? Because they know it’s not an accurate reflection of the fatality rate. It’s just scary.
I hope this helps explain where I got my refreshingly low IFR of .17% with a recovery rate of 99.83%. Aren’t you wondering why the “authorities” are keeping the skeer alive?
*ND has been inflated by authorities.:
- CDC Instructions on filling out death certificates: https://www.thegatewaypundit.com/2020/04/cdc-tells-hospitals-list-covid-19-cause-death-even-assumed-caused-contributed-death-lab-tests-not-required/
- Dr Ngozi Ezike Director of Public Heath Illinois speaks about how the corona death are calculated: https://www.youtube.com/watch?v=zYUExBc1ijU
- Dr. Scott Jensen: https://www.facebook.com/watch/?v=251341969243888
- Dr. Birx on recording deaths: https://www.youtube.com/watch?v=GGHp1GdOD4k
**CDC has historically counted flu & pneumonia deaths together under the heading of FLI: Flu-Like Illness (CDC’s acronym). Now they appear to be separating them out which, of course, makes CV look more dangerous…
Experts define the case fatality rate [fatality rate] as “the ratio of deaths occurring from a particular cause to the total number of cases due to the same cause.”
The operative word is TOTAL. Prof Edmunds goes on to point out that if you just use the tested positive, “you will almost certainly get the wrong answer.”
CDC’s Viral Testing Data in the US: https://www.cdc.gov/coronavirus/2019-ncov/cases-updates/testing-in-us.html
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