Hey this is Warren Redlich. We're obviously in a challenging time today,
with Covid 19. A lot of different countries having a lot of different problems.
I want to talk about one particular issue.
that I think a lot of the conversation is missing.
There's a big issue with data, about how we're handling data,
how we understand data.
We're seeing many different issues.
But I want to focus on one particular thing. One particular thing.
That everyone is missing. Bayesian inference.
There's a mathematican named Bayes.
I learned about it in college or graduate school
I see it as highly relevant to what we're seeing today and I'll explain it.
This has some math in it.
Fractions. Percents. No algebra. No calculus. No trigonometry.
Small amount of math. Pretty simple. I'm going to do my best to make it simple.
I hope you like it. Not like it. I hope you appreciate
why this challenge makes everything more difficult
for not just this particular epidemic but any epidemic.
There's a bunch of data problems we're seeing. The one that I'm most focused on
is something called false positives. I'm going to talk about that a lot in this video.
There's a few other problems.
Inconsistent reporting by geography.
One country reports data one way. Another reports it another way.
Even in the United States we're seeing different states reporting in different ways.
Maybe the biggest example of this
is in Italy.
Any death reported with Covid-19 is reported as a death FROM Covid-19.
A substantial number of the people are dying
are people who would have died anyway.
The cause of death was not Covid-19.
We don't know how much that is happening
in different places because of inconsistent reporting.
That covers the inconsistent reporting.
Some places are testing more. Some are seeing more.
Some places have a lot of activity.
Biggest example is death rate on Diamond Princess.
It was much lower than elsewhere.
Not a lot of good explanations for this.
Japan is not seeing the explosion of problems
that was predicted for it. Why?
All potentially data related problems.
Biggest problem no one is talking about
is the Bayesian inference problem.
Let's talk about Bayes.
Bayesian inference starts with false positive rate.
We don't know what the false positive rate is
for the tests that are being used.
A false positive rate is - if you test 100 people
for anything - virus, bacterial infection ...
could be something completely different. If ...
one percent of the people who don't have it
test positive - the test has -
Any test has some level of inaccuracy.
It's very hard to know what.
Simple example: If you test 100 people who don't have it
and you have a 1% false positive rate
then 1 of those 100 will test positive even though they don't have it.
There's also a false negative problem.
I'm not going to talk about that today. Not significant.