Archive for October 2nd, 2020

CDC: 95% survivability rate if over 70 … higher with no symptoms, no co-morbidities.

October 2, 2020

Pres. Trump has tested COVID-positive… here are his medical odds.

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Last week, the CDC updated its so-called Pandemic Planning Scenarios.

English translation: They revealed the key parameter settings for their “Current Best Estimate” of COVID outcomes.

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Buried in the details (and minimally reported by the MSM) were IFRs — “infection fatality rates” … the odds of dying of you get COVID-infected.

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Here’s the key exhibit from the CDC report.

There are 3 sets of numbers presented: a best case, a worst case and a “best estimate” (where “best” might be more appropriately called “most likely”).

Below, we’ll decode the numbers.

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Let’s focus on the far right column — the “current best estimate” of the IFR — the  “infection fatality rate”.

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The rates are very low … indicating that the likelihood of dying from COVID is very, very low … even if a person gets infected.

Let’s make those numbers a bit more understandable…

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The 3rd column is the ISR — the “infection survivability rate” … the inverse of the IFR.

For example, if you’re in the 50 to 60 age group and you get infected, there’s  a 99.5% chance that you’ll survive … that you WON’T die.

Even in the most vulnerable age group (the over 70 crowd) the probability of surviving a COVID infection is over 95% … over 07% in the best case and over 91% in the worst case (see charts below).

The 4th column — the F-Odds — are read, for example, that the current best estimate is that 1 in 20 infectees who get infected succumb to the virus.

The grain of salt: COVID deaths are a reasonably accurate number .. but the number of infections is a wild guesstimate given the unknown number of asymptomatic infectees who don’t get tested.

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Flashback: Ohio Gov. DeWine tested negative … after testing positive.

October 2, 2020

Not a surprise according to Bayes’ Theorem
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According to the NYT and many other sources…

As part of a screening by the White House, Mr. DeWine first received an antigen test, a newer type of test that provides faster results but is less accurate than traditional laboratory testing.

He tested positive for Covid-19

He was later twice-tested using a more standard procedure known as polymerase chain reaction, or P.C.R., an accurate but time-intensive method that requires samples to be processed at a laboratory.

That test result was negative for the Covid-19.

DeWine’s experience is a classic “false positive” … to be expected based on Bayes’ (Statistical) Theorem.

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Let me explain…

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If I test positive for COVID, am I infected?

October 2, 2020

The answer may surprise you, and it has big implications for test & trace.
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In a prior post, we reported that “Asymptomatics” are not rushing to get tested and provided some subjective reasons why that might be (e.g. no doctor referral, high hassle factor, privacy concerns).

OK, let’s up our game a notch or two and throw some math & economics at the problem.

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I’m a fan of “Freakonomics” … the popular call sign for a discipline called Behavioral Economics … the study of the rationality that underlies many seemingly irrational decisions that people sometimes make.

And, in my strategic business analytics course, I used to teach something called Bayesian Inference … a way to calculate probabilities by combining contextual information (called “base rates” or “priors”) with case-specific observations (think: testing or witnessing).

Today, we’ll connect Freakonomics and Bayesian Inference and apply them to the COVID testing situation…

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