3 ms·

Screenshot of the chart: https://pbs.twimg.com/media/E-86fSrXIAgmddQ?format=png&name=900x900 https://pbs.twimg.com/media/E-86fSrXIAgmddQ?format=png&name=... Sc

by nanis 5y ago

Screenshot of the chart: https://pbs.twimg.com/media/E-86fSrXIAgmddQ?format=png&name=900x900 https://pbs.twimg.com/media/E-86fSrXIAgmddQ?format=png&name=... Screenshot of Biden's claim of 1/160,000 hospitalization rate among fully vaccinated: https://pbs.twimg.com/media/E-4NQBFWEAMzATQ?format=png&name=small https://pbs.twimg.com/media/E-4NQBFWEAMzATQ?format=png&name=... Current rate of known non-fatal hospitalization from COVID-19 among the fully vaccinated is 5.1/100,000 which is 8 times the rate Biden mentioned. It is also 3 times what it was on 6/1. Current rate of known deaths from COVID-19 among the fully vaccinated is 1.2/100,000. Biden did not make a claim about that number. However, the fatality rate is 4 times what it was on 6/1. Therefore, known hospitalizations and deaths from COVID-19 among the fully vaccinated is rising faster than the current number of fully vaccinated individuals. However, this is hard to interpret because there is a lag between arriving at "fully vaccinated" status and getting infected. Then, there is another lag before one might be hospitalized. Then, there is another lag before one might die. From the way the rates have been rising, it seems to me we have not arrived at a point where we can deduce a decent guesstimate of real life probability of hospitalization and death among fully vaccinated. It seems we'll be lucky to have the hospitalization rate stabilize around 1/10,000 and death rate stabilize around 3/100,000. Note the phase3 trials lasted about 5-6 months[1,2] and they did not enroll a lot of people at risk. Now, it seems like, there is consensus that whatever protection the vaccines confer doesn't last more than five or so months past achievement of "fully vaccinated" status. As for being able to compare with "unvaccinated", we need proper data for that, but there is not much out there. Statements like "98% of hospitalizations since January 1 were among the unvaccinated" do not mean anything as the composition of the population is not constant lumping everything together removes information needed to properly evaluate what's happening. [1]: https://www.pfizer.com/news/press-release/press-release-detail/pfizer-and-biontech-conclude-phase-3-study-covid-19-vaccine https://www.pfizer.com/news/press-release/press-release-deta... [2]: https://www.npr.org/sections/health-shots/2021/02/19/969143015/long-term-studies-of-covid-19-vaccines-hurt-by-placebo-recipients-getting-immuni https://www.npr.org/sections/health-shots/2021/02/19/9691430...

MatteoFrigo 5y ago
Data from the UK can help with the decent guesstimate you are looking for. See https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/1016465/Vaccine_surveillance_report_-_week_36.pdf https://assets.publishing.service.gov.uk/government/uploads/... pages 15--17. (The rest of the report is interesting too.) The probabilities in the UK are clearly marked as probabilities per unit time, where the unit time appears to be four weeks. In contrast, it appears that the CDC data is cumulative since the Big Bang, which I think is less useful to your purpose.
- nanis 5y ago
  > Data from the UK can help Thank you for the link. This is actually very good & sober analysis. > is cumulative since the Big Bang, which I think is less useful to your purpose. In a Law of Large Numbers sense, if there is a stable probability, we might converge to it ... except that there may not be enough time for convergence before boosters.
  - MatteoFrigo 5y ago
    > if there is a stable probability, we might converge to it Yeah, that's the thing I can't figure out from the literature. For example, people say that vaccine X is 90% effective. This means that they ran two groups for some number of weeks, and observed one tenth of infections in one group vs. the other. But what does this mean in terms of individual probabilities? Assume for simplicity that the vaccine immunity lasts forever without decaying. There are still two extreme possibilities consistent with the data, as well as multiple intermediate options. In one extreme, the vaccine sets an immunity bit in each individual, and the unlucky ones will get infected when exposed. In the other extreme, the vaccine somehow increases everybody's activation energy for infection so that, under normal conditions, the probability that an exposure causes an infection is reduced by a factor of 10. In the former case, the steady state is that 10% of the population gets infected eventually, and that's it. In the latter case, you keep going forever with infections per unit time being 10% of what they used to be.
    - 5y ago

by nanis 5y ago

MatteoFrigo 5y ago

Data from the UK can help with the decent guesstimate you are looking for. See https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/1016465/Vaccine_surveillance_report_-_week_36.pdf https://assets.publishing.service.gov.uk/government/uploads/... pages 15--17. (The rest of the report is interesting too.) The probabilities in the UK are clearly marked as probabilities per unit time, where the unit time appears to be four weeks. In contrast, it appears that the CDC data is cumulative since the Big Bang, which I think is less useful to your purpose.

nanis 5y ago
> Data from the UK can help Thank you for the link. This is actually very good & sober analysis. > is cumulative since the Big Bang, which I think is less useful to your purpose. In a Law of Large Numbers sense, if there is a stable probability, we might converge to it ... except that there may not be enough time for convergence before boosters.
- MatteoFrigo 5y ago
  > if there is a stable probability, we might converge to it Yeah, that's the thing I can't figure out from the literature. For example, people say that vaccine X is 90% effective. This means that they ran two groups for some number of weeks, and observed one tenth of infections in one group vs. the other. But what does this mean in terms of individual probabilities? Assume for simplicity that the vaccine immunity lasts forever without decaying. There are still two extreme possibilities consistent with the data, as well as multiple intermediate options. In one extreme, the vaccine sets an immunity bit in each individual, and the unlucky ones will get infected when exposed. In the other extreme, the vaccine somehow increases everybody's activation energy for infection so that, under normal conditions, the probability that an exposure causes an infection is reduced by a factor of 10. In the former case, the steady state is that 10% of the population gets infected eventually, and that's it. In the latter case, you keep going forever with infections per unit time being 10% of what they used to be.
  - 5y ago