The CFR will shift substantially over time and location as testing changes. I’m not sure how you would reliably use this information. IFR should not change much and tells you how bad it is for you personally to get sick.
I wouldn’t call the model Zvi links expert-promoted. Every expert I talked to thought it had problems, and the people behind it are economists not epidemiologists or statisticians.
Regarding back-of-the-envelope calculations, I think we have different approaches to evidence/data. I started with back-of-the-envelope calculations 3 months ago. But I would have based things on a variety of BOTECs and not a single one. Now I’ve found other sources that are taking the BOTEC and doing smarter stuff on top of it, so I mostly defer to those sources, or to experts with a good track record. This is easier for me because I’ve worked full-time on COVID for the past 3 months; if I weren’t in that position I’d probably combine some of my own BOTECs with opinions of people I trusted. In your case, I predict Zvi if you asked him would also say the IFR was in the range I gave.
I clicked through to the tweet you mentioned, which contains a screencap of a chart purporting to show “An Approximate Percentage of the Population That Has COVID-19 Antibodies.” No dates or other info about how these numbers might have been generated.
Fortunately, Gottlieb’s next tweet in the thread contains another screencap of the URLs of the studies mentioned in the chart. I hand-transcribed the Wuhan study URL, and found that while it was performed at a date that’s probably helpful (April 20th) it’s a study in a single hospital in Wuhan, and the abstract explicitly says it’s not a good population estimate:
Here, we reported the positive rate of COVID‐19 tests based on NAT, chest CT scan and a serological SARS‐CoV‐2 test, from April 3 to 15 in one hospital in Qingshan Destrict, Wuhan. We observed a ~10% SARS‐CoV‐2‐specific IgG positive rate from 1,402 tests. Combination of SARS‐CoV‐2 NAT and a specific serological test might facilitate the detection of COVID‐19 infection, or the asymptomatic SARS‐CoV‐2‐infected subjects. Large‐scale investigation is required to evaluate the herd immunity of the city, for the resuming people and for the re‐opened city.
I’d need to know more about e.g. hospitalization rates in Wuhan to interpret this.
The New York numbers seem to come from a press release, with no clear info about how testing was conducted.
All of these are point estimates, and to get ongoing infection rates, I’d need to fit a time series model with too many degrees of freedom. Not saying no one can do this, but definitely saying it’s not clear to me how I can make use of these numbers without working on the problem full time for a few weeks.
You’ve nonspecifically referred to experts and models a few times; that’s not helpful and only serves to intimidate. What would be helpful would be if you could point to specific models by specific experts that make specific claims which you found helpful.
I’m not trying to intimidate; I’m trying to point out that I think you’re making errors that could be corrected by more research, which I hoped would be helpful. I’ve provided one link (which took me some time to dig up). If you don’t find this useful that’s fine, you’re not obligated to believe me and I’m not obligated to turn a LW comment into a lit review.
Given that it apparently took you some time to dig up even as much as a tweet with a screen cap of some numbers that with quite a lot of additional investigation might be helpful, I hope you’re now at least less “confused” about why I am “relying on this back of the envelope rather than the pretty extensive body of work on this question.”
If you want to see something better, show something better.
Because of false positives, seroprevalence is massively overestimated everywhere that there hasn’t been a massive outbreak. In those places the IFR is 1-2%. But can we extrapolate to normal outbreaks? If, as widely believed, an overrun medical system has worse mortality, then maybe the normal IFR really is only 0.5-1%. But if your meta-analysis directly measures that, it is not well-done.
The CFR will shift substantially over time and location as testing changes. I’m not sure how you would reliably use this information. IFR should not change much and tells you how bad it is for you personally to get sick.
I wouldn’t call the model Zvi links expert-promoted. Every expert I talked to thought it had problems, and the people behind it are economists not epidemiologists or statisticians.
For IFR you can start with seroprevalence data here and then work back from death rates: https://twitter.com/ScottGottliebMD/status/1268191059009581056
Regarding back-of-the-envelope calculations, I think we have different approaches to evidence/data. I started with back-of-the-envelope calculations 3 months ago. But I would have based things on a variety of BOTECs and not a single one. Now I’ve found other sources that are taking the BOTEC and doing smarter stuff on top of it, so I mostly defer to those sources, or to experts with a good track record. This is easier for me because I’ve worked full-time on COVID for the past 3 months; if I weren’t in that position I’d probably combine some of my own BOTECs with opinions of people I trusted. In your case, I predict Zvi if you asked him would also say the IFR was in the range I gave.
I clicked through to the tweet you mentioned, which contains a screencap of a chart purporting to show “An Approximate Percentage of the Population That Has COVID-19 Antibodies.” No dates or other info about how these numbers might have been generated.
Fortunately, Gottlieb’s next tweet in the thread contains another screencap of the URLs of the studies mentioned in the chart. I hand-transcribed the Wuhan study URL, and found that while it was performed at a date that’s probably helpful (April 20th) it’s a study in a single hospital in Wuhan, and the abstract explicitly says it’s not a good population estimate:
I’d need to know more about e.g. hospitalization rates in Wuhan to interpret this.
The New York numbers seem to come from a press release, with no clear info about how testing was conducted.
All of these are point estimates, and to get ongoing infection rates, I’d need to fit a time series model with too many degrees of freedom. Not saying no one can do this, but definitely saying it’s not clear to me how I can make use of these numbers without working on the problem full time for a few weeks.
You’ve nonspecifically referred to experts and models a few times; that’s not helpful and only serves to intimidate. What would be helpful would be if you could point to specific models by specific experts that make specific claims which you found helpful.
I’m not trying to intimidate; I’m trying to point out that I think you’re making errors that could be corrected by more research, which I hoped would be helpful. I’ve provided one link (which took me some time to dig up). If you don’t find this useful that’s fine, you’re not obligated to believe me and I’m not obligated to turn a LW comment into a lit review.
Given that it apparently took you some time to dig up even as much as a tweet with a screen cap of some numbers that with quite a lot of additional investigation might be helpful, I hope you’re now at least less “confused” about why I am “relying on this back of the envelope rather than the pretty extensive body of work on this question.”
If you want to see something better, show something better.
The director of NIAID publicly endorsed that model’s bottom line.
Because of false positives, seroprevalence is massively overestimated everywhere that there hasn’t been a massive outbreak. In those places the IFR is 1-2%. But can we extrapolate to normal outbreaks? If, as widely believed, an overrun medical system has worse mortality, then maybe the normal IFR really is only 0.5-1%. But if your meta-analysis directly measures that, it is not well-done.