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> Downvoting and brigading don’t change them. Just to be clear, I did not downvote, nor brigade. > No, it doesn’t. You’re cherry-picking old data, and ignorin

> Downvoting and brigading don’t change them. Just to be clear, I did not downvote, nor brigade. > No, it doesn’t. You’re cherry-picking old data, and ignoring the links I gave you, which tell you the updated numbers: I think you may not have understood the numbers in your link. > ages 0-19: .003% ages 20-49: .02% ages 50-69: .5% ages 70+: 5.4% Calculate the overall IFR from these numbers given the age breakdown of the population of the US, and you'll end up a bit above 0.6%, which is what I said (when referencing 0.65%). Your link explicitly confirms what I said. > The old reported numbers in New York are not a counterargument: we now know that number of cases was drastically under-counted, and the inferred IFR, if you use the right denominator, was a fraction of a percent. You misunderstood this. When I referred to NYC boroughs and countries, I was referring to population fatality rates. Those are independent of case rates - it's a measure of what percentage of the entire population of an area has died, independent of infection spread. They help set baselines. > You clearly know this, because you’re trying to talk about the rates in “boroughs” which are higher. But sample heterogeneity matters: I’m sure if you compute the IFR in Maimonedes ICU, it’s really high, too. The fact is, the fatality rate estimated by the Santa Clara study has been shown to be correct, and you’re simply spreading misinformation, because these new facts aren’t as scary. That's fine. We can look at entire states. 0.18% of the entire population of New Jersey has died from Covid. 0.17% of the entire population of New York has died from Covid. And does anyone think nearly the entire population of NJ or NY has been infected already? No. Even in NYC, we've only seen a couple of boroughs that have shown around 50% antibody positives, with most in the 20-30% range. And, of course, incidence rates in the rest of New York state are lower than NYC. What does this mean? With 0.17% of the entire population of NY state dead from Covid, with incidence rates generally at 1/5 to 1/3 of NYC, with incidence rates lower in NY outside of NYC, the implied IFR for NY state is north of 0.5%. With all that said.. you sourced a CDC resource that puts the IFR of Covid-19 above 0.6%. You used it to support a claim of Covid having a similar IFR to flu. Given that your source actually confirmed the IFR I mentioned, does that change your stance?

irq11 6y ago
”That's fine. We can look at entire states. 0.18% of the entire population of New Jersey has died from Covid. 0.17% of the entire population of New York has died from Covid. And does anyone think nearly the entire population of NJ or NY has been infected already? No....And, of course, incidence rates in the rest of New York state are lower than NYC. What does this mean?” Any number of different things: it could mean that the deaths are overcounted (which seems likely in any case; we keep finding examples of “covid deaths” that are better described as “deaths with covid”). It could mean that there’s uncertainty on the IFR estimates, and it’s a bit higher than the point estimate. It could mean that New York City has a higher percentage of elderly and poor people who died at higher rates than average, which you can’t extrapolate to other populations. It could mean that the virus burned through the most vulnerable population already. It could mean that policy and medical decisions made early on in the pandemic were tragically misguided. Or, it could be some combination of the above. Point being, the Santa Clara study was a lot closer to right than wrong, and dismissing it, and the people involved, because of politics and methodological mistakes is a cheap shot. When it came out, the media and “experts” were routinely claiming that this virus had a fatality rate as high as 3-5%. So I don’t particularly care if they were off by a factor of 3 (~0.5%); they were much closer to correct than anyone else at the time.
- rallison 6y ago
  > Any number of different things Yes, it can mean lots of different things, potentially, which is why it is useful to look at factors that may have impacted implied IFRs. E.g. is the population you are studying older than the national average (NY: about average for percent above age 65). Is the population unhealthier than average, e.g. looking at obesity rates (NY: significantly lower obesity than the national average). You want to look at how many deaths were in nursing homes vs not, to see if policy decisions meant disproportionate impact in high risk populations (NY: one of states with the lowest percentage of deaths linked to nursing homes). So many factors were either neutral for NY, or suggest that NY's IFR would be lower than the national average. But, one also considers that NY was stretched thin, which no doubt meant non-ideal care for many people, so that likely countered some of the advantages NY had had. You also then want to compare with other serology studies, and the implied IFRs from those studies - are we looking at outlier numbers, or does it generally align? Outliers can help uncover factors that were missed, or flaws in the study itself. And that's the thing, all of the NY numbers have suggested an IFR above 0.5%, potentially upwards of 1%, which is broadly in line with other IFR calculations from other places. > Point being, the Santa Clara study was a lot closer to right than wrong, and dismissing it, and the people involved, because of politics and methodological mistakes is a cheap shot. > When it came out, the media and “experts” were routinely claiming that this virus had a fatality rate as high as 3-5%. So I don’t particularly care if they were off by a factor of 3 (~0.5%); they were much closer to correct than anyone else at the time. This is just flatly wrong. You've conflated case fatality rates with infection fatality rates. The general consensus around the time of the Santa Clara study was that the IFR for the US was most likely in the 0.5% to 1% range. The 3-5% numbers were referring to case fatality rates. In April, I wrote this - https://news.ycombinator.com/item?id=22969994 https://news.ycombinator.com/item?id=22969994 - "The NY study, if it holds up, suggests an IFR in the 0.8-1.0% range for NYC (depending on whether or not you include the additional excess deaths), which is in the range most experts have been assuming (0.5%-1.0% has been a common range that's been tossed around)." You'll note April is also when the Santa Clara antibody study was released. > they were much closer to correct than anyone else at the time If the best science at the time of the Santa Clara study was estimating an IFR of 0.5-1%, and the Santa Clara study estimated 0.12-0.2%, and the current CDC estimate is 0.65%, who was closer to correct? Given this, does that change your stance?

irq11 6y ago

”That's fine. We can look at entire states. 0.18% of the entire population of New Jersey has died from Covid. 0.17% of the entire population of New York has died from Covid. And does anyone think nearly the entire population of NJ or NY has been infected already? No....And, of course, incidence rates in the rest of New York state are lower than NYC. What does this mean?” Any number of different things: it could mean that the deaths are overcounted (which seems likely in any case; we keep finding examples of “covid deaths” that are better described as “deaths with covid”). It could mean that there’s uncertainty on the IFR estimates, and it’s a bit higher than the point estimate. It could mean that New York City has a higher percentage of elderly and poor people who died at higher rates than average, which you can’t extrapolate to other populations. It could mean that the virus burned through the most vulnerable population already. It could mean that policy and medical decisions made early on in the pandemic were tragically misguided. Or, it could be some combination of the above. Point being, the Santa Clara study was a lot closer to right than wrong, and dismissing it, and the people involved, because of politics and methodological mistakes is a cheap shot. When it came out, the media and “experts” were routinely claiming that this virus had a fatality rate as high as 3-5%. So I don’t particularly care if they were off by a factor of 3 (~0.5%); they were much closer to correct than anyone else at the time.

rallison 6y ago
> Any number of different things Yes, it can mean lots of different things, potentially, which is why it is useful to look at factors that may have impacted implied IFRs. E.g. is the population you are studying older than the national average (NY: about average for percent above age 65). Is the population unhealthier than average, e.g. looking at obesity rates (NY: significantly lower obesity than the national average). You want to look at how many deaths were in nursing homes vs not, to see if policy decisions meant disproportionate impact in high risk populations (NY: one of states with the lowest percentage of deaths linked to nursing homes). So many factors were either neutral for NY, or suggest that NY's IFR would be lower than the national average. But, one also considers that NY was stretched thin, which no doubt meant non-ideal care for many people, so that likely countered some of the advantages NY had had. You also then want to compare with other serology studies, and the implied IFRs from those studies - are we looking at outlier numbers, or does it generally align? Outliers can help uncover factors that were missed, or flaws in the study itself. And that's the thing, all of the NY numbers have suggested an IFR above 0.5%, potentially upwards of 1%, which is broadly in line with other IFR calculations from other places. > Point being, the Santa Clara study was a lot closer to right than wrong, and dismissing it, and the people involved, because of politics and methodological mistakes is a cheap shot. > When it came out, the media and “experts” were routinely claiming that this virus had a fatality rate as high as 3-5%. So I don’t particularly care if they were off by a factor of 3 (~0.5%); they were much closer to correct than anyone else at the time. This is just flatly wrong. You've conflated case fatality rates with infection fatality rates. The general consensus around the time of the Santa Clara study was that the IFR for the US was most likely in the 0.5% to 1% range. The 3-5% numbers were referring to case fatality rates. In April, I wrote this - https://news.ycombinator.com/item?id=22969994 https://news.ycombinator.com/item?id=22969994 - "The NY study, if it holds up, suggests an IFR in the 0.8-1.0% range for NYC (depending on whether or not you include the additional excess deaths), which is in the range most experts have been assuming (0.5%-1.0% has been a common range that's been tossed around)." You'll note April is also when the Santa Clara antibody study was released. > they were much closer to correct than anyone else at the time If the best science at the time of the Santa Clara study was estimating an IFR of 0.5-1%, and the Santa Clara study estimated 0.12-0.2%, and the current CDC estimate is 0.65%, who was closer to correct? Given this, does that change your stance?