Alright folks, the world is still caught up in the fear vortex of the Coronavirus Hoax, with the masses practically lining up and begging for their RFID & Gates' Vaccines. Every day, the numbers show that this virus is less and less deadly than they claimed to begin with (surprise...), but the response keeps getting more and more Nazi-like.
Today, I'd just like to talk about a little bit of math. Unfortunately, this math requires some understanding of terms first, but I promise it is important, and while somewhat complicated, we can just look at the simple end of things.
Basic Reproduction Number ("R0") - This is a number that is assigned to any contagion, and indicates how infectious it is. Specifically, the term "can be thought of as the expected number of cases directly generated by one case in a population where all individuals are susceptible to infection." - Put simply, this means that with each serial interval (don't worry, it's our next term), each infected person can be expected to have spread the infection to a number of people equal to the R0. Here is some further reading if you feel like diving deeper int R0.
Serial Interval - This is another number assigned to a contagion, and indicates how long it is expected to take for another generation of the R0. At each serial interval, we expect the number of infected people to increase by a factor of the *basic reproduction number.
Put them together - The number of infections expected can be calculated, if we have an R0 & SI, as such: R0^x, where x is the number of Serial Intervals that have passed.
Example - Basically, we start with "patient 0," the first person to get the infection. Let's say that the R0 for our example is 2 (for simplicity's sake), and the serial interval is 7 days/1 week (also for simplicity).
- One Week In - 3 cases (1 + 2^1)
- Two Weeks In - 5 cases (1 + 2^2)
- Three Weeks In - 9 cases (1 + 2^3)
- Four Weeks In - 17 cases (1 + 2^4)
- Five Weeks In - 33 cases (1 + 2^5)
- Six Weeks In - 65 cases (1 +2^6)
- And on and on and on.
R0 & SI for "Coronavirus"
Obviously this is an extremely simple breakdown, and I am not even going to bother trying to fully learn, then teach you, exactly how these two numbers are calculated. Luckily, for our purposes that is totally unnecessary, we'll simply use the ones that are being touted by WHO/CDC/etc. There is a bit of a range for both, so this is going to be more of a look at worst-case, best-case, and a range of options in between.
For both numbers (R0 & SI), I am going to simply list out all the estimates I can find in studies/papers, with a link to the source for each estimate. To be clear, most of the sources have an average estimate, with a 95% confidence interval, and a wide range. I'm just using their averages.
Basic Reproduction Number of COVID-19 (by date):
- January 23, 2020: 1.4 - 2.5 (WHO)
- January 28, 2020: 3.11
- January 30, 2020: 2.2
- February 22, 2020: 2.28
- March 12, 2020: 2 - 3
- March 19, 2020: 1.94
- March 19, 2020: 1.32
- March 26, 2020: 2.2
- April 4, 2020: 2.37
Serial Interval of COVID-19 (by date):
- February 14, 2020: 4 - 4.6 days
- March 12, 2020: 4.6 days
- March 19, 2020: 3.96 days
- March 20, 2020: 3.96 - 4.75 days
Alright, so we've got a pretty good range on that R0 number, from 1.32 - 3.11 (that's an insanely big range when dealing with exponential growth, as you'll see in a moment.) Luckily, the Serial Interval, while there are far less studies/papers that discuss it, seems to have a much tighter range. The next part is what I'm actually excited about, because (AFAIK) nobody else has done this math (at least publicly/for laypeople.)
So, the earliest known case of COVID19 was on November 17, 2019, making it 139 days ago. Since the SI has a pretty tight range, I'm going to do one set of estimates using a Serial Interval of 4 days, and another at 4.75 days. For each of those sets, we'll look at a wide range of potential R0s (the lowest, the highest, and a few points in between), and how many estimated cases of COVID-19 that would give us right now.
Serial Interval @ 4 days = 34.75 intervals (139/4)
- RO = 1.32: (1.32^34.75) = 15,486 infected as of 4/4/2020
- RO = 1.94: (1.94^34.75) = 10,025,594,041 infected as of 4/4/2020
- RO = 2.2: (2.2^34.75) = 792,844,756,960 infected as of 4/4/2020
- RO = 2.37: (2.37^34.75) = 10,531,858,701,498 infected as of 4/4/2020
- RO = 3.00: (3^34.75) = 38,015,753,374,507,017 infected as of 4/4/2020
- RO = 3.11: (3.11^34.75) = 132,868,954,799,304,436 infected as of 4/4/2020
Serial Interval @ 4.75 days = 29.26 intervals (139/4.75)
- RO = 1.32: (1.32^29.26) = 3,373 infected as of 4/4/2020
- RO = 1.94: (1.94^29.26) = 263,680,866 infected as of 4/4/2020
- RO = 2.2: (2.2^29.26) = 10,454,123,849 infected as of 4/4/2020
- RO = 2.37: (2.37^29.26) = 92,286,166,111 infected as of 4/4/2020
- RO = 3.00: (3^29.26) = 91,320,422,706,532 infected as of 4/4/2020
- RO = 3.11: (3.11^29.26) = 261,920,193,506,951 infected as of 4/4/2020
I went into this expecting to see some pretty crazy high numbers (certainly much higher than the 1,201,933 that is the current official count), but this is pretty insane. Using either Serial Interval, and any but the lowest possible R0s... the entire population would have already been infected by this thing.
I'm very open to being corrected if there is some other step that is supposed to happen, or if I completely misunderstood what these terms mean and how the math is done... if somebody can correct me on that end of things, I'll happily update this post and give you the rewards (both on HIVE & CommuSteem.)
For the moment, I'm just going to leave it at this, and remind folks of my post three weeks back, where I focused on the clearly incorrect "fatality rate" of this thing. Finally, if you haven't read it yet, this post from TheRant is BY FAR the best COVID-19 article I've seen. Factually, un-biased, sourced, and straight to the point.