4 ms·

The NY study does align fairly well with similar studies done in Santa Clara County and Los Angeles County in California, though. All three indicate a significa

The NY study does align fairly well with similar studies done in Santa Clara County and Los Angeles County in California, though. All three indicate a significantly lower IFR than previously expected.

koeng 6y ago
That would only be true if their tests work well. In my experience, IgG and IgM suck big time, and I wouldn’t trust results. Would love to see one of those randomized studies with qRT-PCR tests.
- pmoriarty 6y ago
  The problem with RT-PCR is that it only pick up viral RNA from a currently active infection, while IgG and IgM (antibody) tests will tell you if the person tested had been infected in the past. Also, depending on how effective the detected antibodies are in fighting off the infection, we might get insight in to how much immunity people have and how long it lasts. RT-PCR will not tell you any of that.
  - koeng 6y ago
    You’re making a massive assumption there - that the IgG and IgM tests tell you if the person has been infected in the past. To which my experience says: no, it doesn’t. Both false positive and negative rates are very high in comparison to people previously negative / positive with RT-PCR tests.
btilly 6y ago
There are major statistical problems with the Santa Clara and Los Angeles studies though. Namely selection bias, and using a test that is inaccurate enough that all reported positives are plausibly false positives. That said, IFRs in the 1% or less range have been projected for some time, and everyone who pays attention to the numbers knows that reported dramatically understates reality (the only debate is over how much).
- deleted 6y ago
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rallison 6y ago
> All three indicate a significantly lower IFR than previously expected. 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). For example, the Imperial College model used 0.9% IFR as an input. Additionally, a 10x confirmed cases to actual cases ratio is in the range most experts were assuming. The two CA studies were outliers (and, had significant and substantive critiques), and suggested an IFR as much as 10x lower than the NY study suggests. I wouldn't call those two studies as aligning with the NY study.
- rbritton 6y ago
  Do you have any links handy discussing the issues? I have not come across anything like that in my reading and would like to read more on the critiques.
  - mikeyouse 6y ago
    A lot of the discussion is happening on twitter. One such thread: https://twitter.com/wfithian/status/1252692357788479488 https://twitter.com/wfithian/status/1252692357788479488 > I have been corresponding with the authors of the well-known Santa Clara County COVID-19 preprint, and I am alarmed at their sloppy behavior. The confidence interval calculation in their preprint made demonstrable math errors - 'not' just questionable methodological choices. .. > The errors are not debatable and can be seen in these two screenshots of the supplement: 0.0034, the standard error meant to measure uncertainty about prevalence pi, is not the square root of 0.039, and the variance of a binomial estimate of proportion depends on the sample size. Another critique: https://twitter.com/jjcherian/status/1251272333177880576 https://twitter.com/jjcherian/status/1251272333177880576 > Ok, so what's wrong with the confidence intervals in this preprint? Well they publish a confidence interval on the specificity of the test that runs between 98.3% and 99.9%, but only 1.5% of all the tests came back positive! > That means that if the true specificity of the test lies somewhere close to 98.3%, nearly all of the positive results can be explained away as false positives (and we know next to nothing about the true prevalence of COVID-19 in Santa Clara County) > They report a 95% confidence interval for the prevalence of COVID-19 in Santa Clara County that runs from 2.01% to 3.49% though! That seems oddly narrow, given that they have already shown that it is within the realm of possibility that the data collected are all false positives!
deleted 6y ago
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koeng 6y ago

That would only be true if their tests work well. In my experience, IgG and IgM suck big time, and I wouldn’t trust results. Would love to see one of those randomized studies with qRT-PCR tests.

pmoriarty 6y ago
The problem with RT-PCR is that it only pick up viral RNA from a currently active infection, while IgG and IgM (antibody) tests will tell you if the person tested had been infected in the past. Also, depending on how effective the detected antibodies are in fighting off the infection, we might get insight in to how much immunity people have and how long it lasts. RT-PCR will not tell you any of that.
- koeng 6y ago
  You’re making a massive assumption there - that the IgG and IgM tests tell you if the person has been infected in the past. To which my experience says: no, it doesn’t. Both false positive and negative rates are very high in comparison to people previously negative / positive with RT-PCR tests.

btilly 6y ago

There are major statistical problems with the Santa Clara and Los Angeles studies though. Namely selection bias, and using a test that is inaccurate enough that all reported positives are plausibly false positives. That said, IFRs in the 1% or less range have been projected for some time, and everyone who pays attention to the numbers knows that reported dramatically understates reality (the only debate is over how much).

deleted 6y ago
[deleted]

rallison 6y ago

> All three indicate a significantly lower IFR than previously expected. 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). For example, the Imperial College model used 0.9% IFR as an input. Additionally, a 10x confirmed cases to actual cases ratio is in the range most experts were assuming. The two CA studies were outliers (and, had significant and substantive critiques), and suggested an IFR as much as 10x lower than the NY study suggests. I wouldn't call those two studies as aligning with the NY study.

rbritton 6y ago
Do you have any links handy discussing the issues? I have not come across anything like that in my reading and would like to read more on the critiques.
- mikeyouse 6y ago
  A lot of the discussion is happening on twitter. One such thread: https://twitter.com/wfithian/status/1252692357788479488 https://twitter.com/wfithian/status/1252692357788479488 > I have been corresponding with the authors of the well-known Santa Clara County COVID-19 preprint, and I am alarmed at their sloppy behavior. The confidence interval calculation in their preprint made demonstrable math errors - 'not' just questionable methodological choices. .. > The errors are not debatable and can be seen in these two screenshots of the supplement: 0.0034, the standard error meant to measure uncertainty about prevalence pi, is not the square root of 0.039, and the variance of a binomial estimate of proportion depends on the sample size. Another critique: https://twitter.com/jjcherian/status/1251272333177880576 https://twitter.com/jjcherian/status/1251272333177880576 > Ok, so what's wrong with the confidence intervals in this preprint? Well they publish a confidence interval on the specificity of the test that runs between 98.3% and 99.9%, but only 1.5% of all the tests came back positive! > That means that if the true specificity of the test lies somewhere close to 98.3%, nearly all of the positive results can be explained away as false positives (and we know next to nothing about the true prevalence of COVID-19 in Santa Clara County) > They report a 95% confidence interval for the prevalence of COVID-19 in Santa Clara County that runs from 2.01% to 3.49% though! That seems oddly narrow, given that they have already shown that it is within the realm of possibility that the data collected are all false positives!

deleted 6y ago

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