The high end estimate is deaths / verified infections (30 million).
The low end estimate is the deaths / verified infections plus a very huge number of asymptomatics (30million +30 million= 60 million ).
You actually asked a few times if your 0.3% estimated death rate is BS because you feel aggrieved or something.
I gave you the clear numbers and methodology to say that , yes, it is BS.
The death rate is at least 3x-6x higher.
Not satisfied with the answer ? Not my problem
Why I’m doing this is to show that some people want to believe what they want to believe while accusing others all the time of exaggerating or lying. Then when I go through the numbers and check back people get all aggrieved.
I’m quite surprised the mods didn’t understand why I was taking the piss out of a paper that, in plain English, is titled “ageing is the cause of ageing”. I was not “throwing my toys out of the pram”. I was passing comment on an astoundingly pointless “scientific” observation.
As for your calculation above, you can’t know how many of those who died would have died anyway, and you’re not prepared to accept any of the evidence (and basic statistical truisms) suggesting the answer is ‘a lot’. Or even ‘more than 0%’.
You’re suggesting the CFR should be ~0.9% based on crude cumulative deaths and known seroprevalence (ie., about 20% of the US population). However if one were talking about people dying of COVID as opposed to with COVID - that is, people whose life expectancy was considerably greater than zero at the time of their death - then the 0.3% figure is not unreasonable.
Even if you’re not prepared to accept that viewpoint at all - which amounts to asserting that there’s no such thing as life expectancies - 0.9% is still a rather small number, and may still be an overestimate. The global number of confirmed cases and deaths are 132m and 2.86m, respectively; 132/2.86 = 2.16%. There are multiple papers claiming a true infection rate anywhere between 4 and 10x the number of confirmed cases; that factor only needs to reach 7, and there’s your 0.3%. Since 70-80% of cases are asymptomatic and/or would not merit contacting the health authorities, that’s not implausible.
You’re being awfully confident about numbers that embody a large degree of uncertainty.
There’s no exaggeration with the numbers I posted and I even went through it methodically for the US and tried to give a balanced view
a) using the highest estimate for asymptomatics I could find (which has no real world data to back it…Just an estimate from an algorithm) and
b) the lowest number for covid deaths.
The numbers are as reliable as you will get and backed with real data.
You are downplaying the severity without providing any evidence, this is what I was referring to trivialization in the other thread. You are promoting a counter-narrative to the experts, therefore the responsibility is on you to provide the proof.
Can you provide links to the papers instead of just stating they exist.
The estimated age-specific IFR is very low for children and younger adults (e.g., 0.002% at age 10 and 0.01% at age 25) but increases progressively to 0.4% at age 55, 1.4% at age 65, 4.6% at age 75, and 15% at age 85. Moreover, our results indicate that about 90% of the variation in population IFR across geographical locations reflects differences in the age composition of the population and the extent to which relatively vulnerable age groups were exposed to the virus.
While the NYC data indicate a population IFR of about 1%, seroprevalence estimates from other locations have yielded a wide array of population IFR estimates, ranging from about 0.6% in Geneva to levels exceeding 2% in northern Italy.
Results: The overall infection fatality risk was 0.8% (19 228 of 2.3 million infected individuals, 95% confidence interval 0.8% to 0.9%) for confirmed covid-19 deaths and 1.1% (24 778 of 2.3 million infected individuals, 1.0% to 1.2%) for excess deaths.
I’m not sure why you deserve any more respect than anybody else in the thread, but criticising your analysis doesn’t amount to disrespect, does it?
I’m not suggesting exaggeration. I’m suggesting you’re failing to consider the extreme complexity of the situation, and the possible spread of values. I demonstrated that 0.3% is not implausible with reference to published research. There are no “reliable” numbers because certain things are unknowable; therefore one must factor in some probabilities.
If you’re arguing with my rationale, then explain why it’s wrong instead of claiming infallibility.
Actually my numbers are well worked out on real world data from the US.
Most estimates from papers around the world (see above )converge on 1-2% and when I worked on the numbers independently in the US that’s what I get too.
The IFR is actually fairly consisent and predictable it seems in different locations and over a period of time.
It’s up to you to find data to that there is massive pool of asymptomatics out there . You should be checking the REACT study for that data in the UK. But you didn’t. Why ?
It’s all here in black and white.
You could have found other papers and reference them but you didn’t . Why ?
There’s plenty of data out there you can link back to to make your point.
I was simply flagging up one possible uncertainty. The problem you have to consider is: what does one mean by “imminent death”? Do you count people who were very likely to check out within the month? Or statistically likely to do so within the year? There’s a lot of subjectivity here. Pick a number that makes you happy, but zero isn’t a valid answer.
Then there’s the additional point that undetected cases are much higher than the number of confirmed cases.
These multiple layers of uncertainty broaden the possible range of correct answers.
No, I’m not. Nothing I’ve said here contradicts the scientific consensus. I’m asking firstly: is it really valid to quote hard numbers for IFR when there are a wide range of published values; and do these numbers mean what the politicians say they mean?
You just posted one yourself!
For example, as shown in Table Table1,1, the New York Department of Health conducted a large-scale seroprevalence study and estimated about 1.6 million SARS-CoV-2 infections among the 8 million residents of New York City [10]. However, only one-tenth of those infections were captured in reported COVID-19 cases,
There are any number of other papers estimating the number of unreported cases - if you’d like to look them up yourself then you can pick the values that match your preconceptions.
And yet I end up with much less than that using equally reliable numbers: I picked global deaths, global confirmed cases, and a (very rough) estimate of unreported cases, which as I said to Zepto is known to be in the 80% ballpark. So what’s wrong with that estimation?
The US might well have a higher fatality rate than the world-at-large. But I don’t think so. The infographic someone posted a while back was pretty clear: 550k deaths and 20% seroprevalence, and that latter number might slightly underestimate the number of true infections because we don’t know if asymptomatic cases develop any measurable antibody response. See how silly this gets? You can get wildly different answers depending on the data you choose and the nature of your assumptions.
They are using this as an example of why CFR numbers are unreliable. The study quoted was done in April 2020 after the 1st wave in New York, testing in the US was a shambles at the time with most people only being tested if they had serious infection leading to the higher CFR numbers. Applying this factor of like this for the CFR/IFR to the current global statistics that you use above is not reasonable. Do you have any other more recent sources to justify your extrapolation to give 0.3% IFR?
It’s a news report of a study, not the study itself, but there’s no reason to believe the situation is markedly different now, for the simple reason that most “true” cases aren’t even noticed:
This paper explains in great detail why the IFR is a very slippery concept, and why the numbers are all over the place:
Just to be absolutely clear: I’m not asserting that the IFR is 0.3%. I’m saying 0.3% is entirely plausible given the uncertainties in measurements. So, for that matter, is 1.5%. We do not know what the IFR actually is, and given the curious nature of the disease, it’s probably unknowable.
No risk assessment of Covid is worth much unless you factor in the possibility of having long-term health consequences and continuing debilitating illness
I’d be interested to know if this correlates in any way with pre-existing conditions. The rise of ME over the last few years is quite bizarre, and I wonder if there’s some connection.