3 ms·

First of all, the two are both geographically and ideologically closely related — it's like "proving" Marx was right by quoting Engels. Second, the 0.2% IFR is

First of all, the two are both geographically and ideologically closely related — it's like "proving" Marx was right by quoting Engels. Second, the 0.2% IFR is patent nonsense. To pick only one example, Russia had an excess mortality of 1.22 million people in 2020-2022 [1]. A 0.2% IFR would imply 610 million infections. Russia has a population of 146 million, so every single inhabitant would have had more than 4 infections on average (with zero cross infection immunity). There were several regions in the world where in September 2000, when this paper appeared and multiple infections were not very common yet, more than 0.2% of the population had died already. [1] https://ourworldindata.org/grapher/cumulative-excess-deaths-covid?country=USA~RUS~MEX~BRA~IRN~PER https://ourworldindata.org/grapher/cumulative-excess-deaths-...

Manuel_D 4y ago
Excess deaths aren't entirely caused by Covid. People ceased getting medical care due to lockdowns, for instance. Furthermore, you're cherry-picking examples. As per your own source, the USA had an excess mortality of 1.2 million, out of a population of 330 million. There's 100 million reported infections, and with 70% or more cases being asymptomatic the actual infection rate is probably 300-400 million. Even if we're assuming every excess death was a Covid death, that's an infection fatality rate of 0.4 to 0.3% - not far off of the paper's estimate.
- microtherion 4y ago
  > People ceased getting medical care due to lockdowns, for instance Many countries had no lockdowns to speak of, and the vast majority had none after vaccinations became available. > that's an infection fatality rate of 0.4 to 0.3% - not far off of the paper's estimate That's still double the rate in the paper. And the whole assumption of the Great Barrington declaration, of which Bhattacharya was an author and Ioannidis a sympathizer, was that the rate would drop considerably on reinfection, because herd immunity would protect the population.
  - Manuel_D 4y ago
    > That's still double the rate in the paper. No, it's 1.5x on the lower end. Taking into account the fact that not all excess deaths are necessarily due to covid and the paper is very close. And the paper is still much closer than the 1-3% fatality rates often touted at the time. But then again, we're just randos on the internet. So let's trust the experts: subsequent research has largely vindicated his conclusions. Later research has put the IFR rate at 0.47% to 0.31%: https://pubmed.ncbi.nlm.nih.gov/35219376/ https://pubmed.ncbi.nlm.nih.gov/35219376/ This study only examined infections up until January of 2021, so vaccination is not in play here. > as that the rate would drop considerably on reinfection, because herd immunity would protect the population. Which is correct. Reinfections are less likely to result in severe symptoms. Even the CDC has acknowledged this: https://www.cdc.gov/coronavirus/2019-ncov/hcp/clinical-care/clinical-considerations-reinfection.html#:~:text=Symptoms%20during%20reinfection%20are%20likely,severe%20COVID%2D19%20during%20reinfection https://www.cdc.gov/coronavirus/2019-ncov/hcp/clinical-care/....
    - microtherion 4y ago
      > And the paper is still much closer than the 1-3% fatality rates often touted at the time. The paper you're citing shows that at the time (mid-2020) that Ioannidis wrote his paper estimating a 0.2% IFR, there were many regions in the world where a 1% fatality rate was well within the 95% confidence range. > Later research has put the IFR rate at 0.47% to 0.31% To be more precise, an IFR dropping from 0.47% in March 2020 to 0.31% in January 2021, mostly due to improved treatments. > [herd immunity would protect the population] is correct. Reinfections are less likely to result in severe symptoms. Yes, but this means you cannot claim 400 million infections as a denominator for a constant 0.2% IFR, because many of these are reinfections, so should have a lower IFR than whatever was estimated in mid-2020. As an aside, before Ioannidis started engaging in COVID-19 research, he estimated that the US would suffer 3.3 million infections and 10,000 deaths: https://www.statnews.com/2020/03/17/a-fiasco-in-the-making-as-the-coronavirus-pandemic-takes-hold-we-are-making-decisions-without-reliable-data/ https://www.statnews.com/2020/03/17/a-fiasco-in-the-making-a... His subsequent research seems to have been designed to justify the policy preference, rather than the other way around.

Manuel_D 4y ago

Excess deaths aren't entirely caused by Covid. People ceased getting medical care due to lockdowns, for instance. Furthermore, you're cherry-picking examples. As per your own source, the USA had an excess mortality of 1.2 million, out of a population of 330 million. There's 100 million reported infections, and with 70% or more cases being asymptomatic the actual infection rate is probably 300-400 million. Even if we're assuming every excess death was a Covid death, that's an infection fatality rate of 0.4 to 0.3% - not far off of the paper's estimate.

microtherion 4y ago
> People ceased getting medical care due to lockdowns, for instance Many countries had no lockdowns to speak of, and the vast majority had none after vaccinations became available. > that's an infection fatality rate of 0.4 to 0.3% - not far off of the paper's estimate That's still double the rate in the paper. And the whole assumption of the Great Barrington declaration, of which Bhattacharya was an author and Ioannidis a sympathizer, was that the rate would drop considerably on reinfection, because herd immunity would protect the population.
- Manuel_D 4y ago
  > That's still double the rate in the paper. No, it's 1.5x on the lower end. Taking into account the fact that not all excess deaths are necessarily due to covid and the paper is very close. And the paper is still much closer than the 1-3% fatality rates often touted at the time. But then again, we're just randos on the internet. So let's trust the experts: subsequent research has largely vindicated his conclusions. Later research has put the IFR rate at 0.47% to 0.31%: https://pubmed.ncbi.nlm.nih.gov/35219376/ https://pubmed.ncbi.nlm.nih.gov/35219376/ This study only examined infections up until January of 2021, so vaccination is not in play here. > as that the rate would drop considerably on reinfection, because herd immunity would protect the population. Which is correct. Reinfections are less likely to result in severe symptoms. Even the CDC has acknowledged this: https://www.cdc.gov/coronavirus/2019-ncov/hcp/clinical-care/clinical-considerations-reinfection.html#:~:text=Symptoms%20during%20reinfection%20are%20likely,severe%20COVID%2D19%20during%20reinfection https://www.cdc.gov/coronavirus/2019-ncov/hcp/clinical-care/....
  - microtherion 4y ago
    > And the paper is still much closer than the 1-3% fatality rates often touted at the time. The paper you're citing shows that at the time (mid-2020) that Ioannidis wrote his paper estimating a 0.2% IFR, there were many regions in the world where a 1% fatality rate was well within the 95% confidence range. > Later research has put the IFR rate at 0.47% to 0.31% To be more precise, an IFR dropping from 0.47% in March 2020 to 0.31% in January 2021, mostly due to improved treatments. > [herd immunity would protect the population] is correct. Reinfections are less likely to result in severe symptoms. Yes, but this means you cannot claim 400 million infections as a denominator for a constant 0.2% IFR, because many of these are reinfections, so should have a lower IFR than whatever was estimated in mid-2020. As an aside, before Ioannidis started engaging in COVID-19 research, he estimated that the US would suffer 3.3 million infections and 10,000 deaths: https://www.statnews.com/2020/03/17/a-fiasco-in-the-making-as-the-coronavirus-pandemic-takes-hold-we-are-making-decisions-without-reliable-data/ https://www.statnews.com/2020/03/17/a-fiasco-in-the-making-a... His subsequent research seems to have been designed to justify the policy preference, rather than the other way around.