CDC revises fatality rate
-
Even still it is definitely nowhere near true that 100% of all cases are 65 plus.
There’s just no bridging the gap between their estimate and the NYC reality.
-
I don’t see how any argument from authority or deep dive into demographics can bridge you from a 0.25% population fatality rate to a 0.26% infection fatality rate when only 20% of the population has been infected.
-
Not to mention that the 65+ age bucket certainly has drastically different expectations of fatality after symptomatic infection, when further broken down by age. I would not be surprised if an 85 year old had several multiples of the risk of a 65 year old.
-
So we were undercounting Covid deaths?
-
@Horace said in CDC revises fatality rate:
Right there in the story was that NY was sending its covid patients back to the nursing homes. There is reason to believe that the population who were dying had a greater than 20% rate of infection.
Yup, versus the much maligned gov of Florida who separated the elderly...and when Florida opened faster than the rest of the country and didn’t have disaster, the evil incarnate governor fell off the media pages.
-
I dont see either one of your points. - no conceivable demographic details get you from a 0.25% population fatality rate to a 0.26% infection fatality rate with a 20% serology result. And no conceivable fact about nursing homes or inter-state comparisons gets you there either.
-
Of course. The infection rate of the 21k numerator is 100%. Those are the Covid deaths.
-
@jon-nyc said in CDC revises fatality rate:
I dont see either one of your points. - no conceivable demographic details get you from a 0.25% population fatality rate to a 0.26% infection fatality rate with a 20% serology result. And no conceivable fact about nursing homes or inter-state comparisons gets you there either.
It seems conceivable that the 20% underestimates the rate of infection of the pool of folk who comprised the numerator of the fatality rate. I don't mean to make the tautology that if you died of it then you had it, i mean to say that they came from an identifiable cohort (nursing homes?) with far greater than 20% infection rate.
-
Right. So sample bias in serology because serology test recipients were unlikely to be nursing home residents.
That’s a point, though even if every New Yorker over 75 was positive and unaccounted for in the serology sample that would bring us to 25% infection rate rather than 20%
-
-
@Loki said in CDC revises fatality rate:
NYC deaths by age per100,000
Over 75 is greater than all the other categories combined by well (vastly) more than double.
https://www.statista.com/statistics/1109867/coronavirus-death-rates-by-age-new-york-city/
That link implies 16500 total deaths in nyc rather than 21000. (196/100000)*8400000 = 16500
-
@Loki said in CDC revises fatality rate:
In NYC you were almost 100 times as likely to die if you were over 75 than under 44.
Loki by now we all fully understand your discount function on Covid deaths. Does it have any bearing on the accuracy of the CDC model?
-
@Loki said in CDC revises fatality rate:
In NYC you were almost 100 times as likely to die if you were over 75 than under 44.
This sort of distinction seems important, in a debate about whether to shut down a society. And all the biggest impact rhetoric of the debate, such as counts of lives lost or lives that could have been saved, completely ignores it.
-
If we were talking about lockdown measures I wouldn’t have found the comment out of the ordinary.
But that makes some sense out of the inability to see the obvious arithmetic impossibility of the CDC estimate in NY. I thought we were arguing about a CDC model not lockdown measures. But I guess we’re always arguing about the lockdown.
-
It is not impossible that a model built from a large set of data will seem arithmetically at odds with some subset of that data, which might be an outlier. Are we concentrating on NYC because it seems to be an outlier, while ignoring other sets of data which seem to corroborate the model? The CDC model is actually under no obligation to conform to every subset of the data, it is meant to predict in general. And yes, it is conceivable that the NYC numbers imply fatality rates which overestimate the general case.
-
“My model of heat dissipation in ceramic tiles was confirmed by 134 out of 135 Space Shuttle missions.”
-
I get the model can’t conform to every conceivable subset of data but your biggest outbreak by far isn’t just another subset of data.
If the model is going to have any useful predictive power it can’t miss the big important cases.
