This is an opinion column. The thoughts expressed are those of the author.
Wuhan China Virus Statistics
Sam Pearson 4/22/2021 10:33 AM
Statistics is a tool that we use to plan for and execute all of these variables. Training, equipping, and transporting 300,000 men and their equipment halfway around the world is simply a matter of identifying the capability of available systems and mathematically assigning the appropriate resource to the task.
This all works pretty good until politics becomes involved. While assisting a PhD candidate on his dissertation, he mentioned that he was concerned that the new virus COVID-19 (SAR-2) was being politicized. We had both been reading about how various Flu like illnesses seemed to have established a pattern. SARS-1 (2003) and MERS (2012) are both coronavirus like COVID-19 and seemed to follow the same pattern of reaching herd immunity based on the simple formula 1 − 1/R0.
The only thing you need to know is how many people, on average, are infected by each infected person. This value is called R0 (pronounced “R naught”). Once you have that, you can plug it into a simple formula: 1 − 1/R0. Let’s say the R0 for COVID-19 is 2.5, meaning each infected person infects, on average, two and a half other people (a common estimate). In that case, the herd immunity threshold for COVID-19 is 0.6, or 60%. That means the virus will spread at an accelerating rate until, on average across different places, 60% of the population becomes immune.
Going further down this path we can ask how long does it take to reach 60%, well in the US we have a population of about 320 million such that 60% is about 192 million Americans. COVID-19 seems to reach transmittable phase in an average of 10 days. So if we started with 1 person we would reach 60% with an R0of 2.5 in about 300 days, however, Travel data of passengers arriving in the United States from China during the critical period in December, January and February, when the disease took hold in that country, shows a stunning 759,493 people entered the U.S.
Assuming that only one person in a thousand had COVID-19, then this takes us down to reaching herd immunity in just 14 cycles. This is the same as we saw for both SARS-1 and MERS. So 14 x 10 day cycles starting on 1 March would be Sunday, 19 June 2020.
Guess what we decided to do, we would flatten the curve by distancing ourselves by 6 feet. This one decision meant that the R0 of 2.5 was no longer a standard variable. In fact, this decision made COVID-19 go into the normal Flu season and we did not reach herd immunity until mid-February 2021.
There is also a statistical anomaly associated with the way that we have counted COVID-19 cases. COVID-19 is an “A” type virus like all other Coronaviruses. The magic of COVID is that it has stopped all “A” type flu cases in the United States, however, it has only been effective up to the borders as most of Central America and Canada still had a flu season!
It does not take a genius to figure out that when one incentivizes COVID, then all Flu like illnesses become COVID. Below is what a normal flu season looks like
So far we have had 561,775 deaths attributed to COVID during the last 13 months. Using the same criteria for the Flu in 2018 we would have had 574,863 deaths during the same 13 months. Pretty amazing is it not?
We have never counted deaths in the way we have done so for COVID.
If we did the same for all causes of death, we would have 29,070,613 deaths in 2020 as opposed to the 2,913,144 who CDC says actually died. The US has an annual death rate of 0.8% for the last 20 years, 2020 was more than 10% lower than when I was in High school back in the 1970s.
“Only Thing We Have to Fear Is Fear Itself”: Franklin D. Roosevelt
Sam Pearson is a retired Army Colonel with a variety of experience in both government and private sectors. As arguably one of the World's foremost military logisticians, he has been responsible for the on time delivery of supplies and services worth billions of dollars. After service in Southwest Asia, he was hand picked to support logistics operations in support of earthquake relief operations in Haiti. Pearson now serves as a consultant and volunteer mentor for students seeking their doctorates in advance statistical analysis.
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