Posted on 05/23/2020 3:52:29 PM PDT by Michal T
I'm a scientist. I don't deal in narratives and estimations; I deal with real numbers and hard data. The planning scenario did not say how it came up with that fatality rate, but that fatality rate is something like 1/24th of the actual rate calculated using real case data.
It represents the CDC'S current best estimate of the numbers to be expected, based on confirmed symptomatic cases and fatalities, along with an estimate based on other studies that 35% of cases don't show symptoms. Merely using reported cases will necessarily give a deceptively higher fatality rate than one that also approximates how many mild or asymptomatic cases are not being reported (based on studies of the population as a whole).
To date, there has been no credible study that shows that a majority or even significant minority of Covid-19 cases are asymptomatic. The antibody studies which have been widely touted due to their supposed confirmation of that dangerous narrative show no such thing; antibodies are non-specific in that they cannot tell the difference between proteins of similar structure and chemical properties, such as proteins from different (yet similar) coronaviruses. This gives those antibody tests a high false positive rate, which pretty much negates any value they may have as a research tool.
BTW, you did your calculations based on non-real numbers, so they really don't mean a lot. The CDC pandemic planning scenario is NOT an analysis of the disease situation, but suppositions used to war game various courses of action. The actual case and death data can be found at the Johns Hopkins tracker and the worldometer tracker. In order to make determinations about case fatality rates, I would stick with the information from the trackers that are collecting real verified data. Your state also has a website where you can access Covid-19 data that may be more pertinent to you than the world or US data.
“anyone still claiming to work out rates based on CASES is either stupid or being deceptive. CASES means nothing, except as a weapon for the FakeNewsMSM to wield against those govts they don’t approve of.”
Positive and negative covid-19 tests are meaningless.
If one wants to avoid illness, stop eating crap food (full of sugar and preservatives and bad fats, but free of fiber) and start supplementing vit A, B, C and D3 and a chelated zinc. And stay hydrated.
>>”For those reasons, the “true” death rate figure for the Wuhan Virus may be much lower than CDC’s current estimate of .4%...”<<
Yes, it’s common sense to recognize that if some cases aren’t serious enough to be reported, then fatality rates based only on reported cases are going to be too high. How much too high I don’t know, and will await the outcome of the disputes over that before forming a judgment (I suspect a good bit too high, though).
>>”BTW, you did your calculations based on non-real numbers, so they really don’t mean a lot.”<<
Well, when reporting on the CDC page, I still think it’s a good idea to do the math correctly (and I’ve asked this question at two sites, and I’ve yet to have anyone either vouch for my computation or disagree with it).
As for non-real numbers, the numbers at sites like Worldmeter may be real (allowing for some misreporting, of course), but when it’s known that not all cases are being reported (”not-all” being an understatement), I don’t think they can be said to give a “real” picture of the situation. That’s especially true of the fatality rate, which needs to include both treated and untreated cases of the virus, else it will be too high.
I think that’s comparable to walking into a room with a camera and taking a high resolution photo of one part of the room, then reporting to the world, “Here’s our picture of the room.” You don’t have to be a scientist or expert mathematician to see what’s wrong with that.
>>”This gives those antibody tests a high false positive rate, which pretty much negates any value they may have as a research tool.”<<
I understand that critics have found fault with some of those studies, but there have been several, and if some weren’t reliable, then more reliable ones need to be devised that include samples of the population of the whole. Obviously not everyone who’s sick is being treated by doctors and recorded in the stats — not to mention tested — and the lack of that information likewise negates much of the value of reported cases as a research tool. The CDC must have had some reason for estimating that around 35% of the cases are asymptomatic, and for including that estimate.
<< “The CDC pandemic planning scenario is NOT an analysis of the disease situation, but suppositions used to war game various courses of action.”<<
They weren’t chosen arbitrarily but are based on the disease situation, and intended to represent, in the judgment of the CDC, some of the more likely outcomes that need to be planned for. Also the CDC must have had some reason for labeling the fifth scenario “Best Estimate” (and “Best” doesn’t mean most favorable because some of the other scenarios have much more favorable numbers). I think “Best Estimate” represents the estimate that the CDC considers most likely — based on previous information.
Danny, I think your analysis is still wrong.
The fundamental difference is that the USA was shut down and Sweden was NOT shutdown.
As a result, Sweden is now MUCH closer to herd immunity with total deaths of 0.0396% of the population, compared to US deaths at 0.0298% of the population.
To assume we will need an additional 33,333 US deaths (total 133,333) to approach herd immunity is rational.
To assume we will need an additional 1,233,333 to approach herd immunity is fear mongering nonsense!
The numbers at Worldometer, Johns Hopkins, the WHO, various state reporting agencies, etc., may miss a small number of cases just because it takes time to conduct lab tests and send in the results. This is the case with all reportable disease tracking. That does not mean, however, that millions of cases are being missed or that the reported numbers are significantly different than the real number of cases. This applies to all data elements relative to the pandemic: new cases, deaths, and recovered cases. The significance of this is that there is uncertainty for the last 7 days of reporting at any time, but the cumulative case data preceding the last 7 days is fairly accurate.
I do not know what assumptions the CDC made when coming up with those exceedingly low case fatality rates for the pandemic scenario.
However, what I do know is that the antibody studies are deeply flawed, and no other "studies" even came close to using any real data. The antibody studies are flawed due to the physicochemical properties of antibody/antigen interactions. There isn't a way to get around the fact that antibodies tend to recognize proteins that are similar in shape, hydrophobicity, and charge status. There are not "more reliable" antibody studies, since no one can get around the laws of physics. The *only* reliable way to show that someone has been infected with Covid-19 is by a direct test for virus, either by detecting viral RNA through an RT-PCR analysis, or by culturing the virus. Once a person has had Covid-19, their body clears the virus. I do not know if there is a window where virus would still be detectable in patient samples after recovery from the illness. But that would be the only way to establish that a person has had Covid-19 after the fact.
If the antibody checks aren't specific enough, maybe they show something just as encouraging. I notice in a recent thread a link to this:
"The study, written by researchers in California, New York and North Carolina and soon to be published in the journal Cell, discovered that certain types of cells in blood samples taken from donors in 2015-2018—well before COVID-19 arose—were reactive against the COVID-19 virus. In other words, those blood samples were at least partially immune from the coronavirus even though they had never been exposed to it. " [Study: A majority of the population may have 'some degree' of preexisting immunity to COVID-19]
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