True, but Diamond Princess is full of oldies, and, despite South Korea massive testing, there might be selection bias—I guess people would only get tested if they had some symptom or contact with other infected persons (perhaps you’re referring a more specific study?). Notice that, if the science study claiming 86% of the cases in Wuhan were undocumented were right, this would already imply a fatality rate of about 0.6%, below South Korea estimates.
Yet, I agree the fatality rate is surprisingly low, and it’s just a statistical model.
Diamond princess is important because they did 100% testing so it gives us an idea of asymptomatic : symptomatic ratio. The result was roughly 1:1, nothing like 50:1 or whatever this paper suggests. The science study with 6:1 is at least plausible if you account for symptomatics who weren’t identified.
If South Korea hadn’t managed to test the majority of their cases then it is unlikely that they would have managed to reduce their infection rate so dramatically—their quarantine measures aren’t massively strict although I think the population are self-enforcing good practice pretty well. I doubt that Wuhan death rates could be below South Korean rates due to the acknowledged overcrowding in Wuhan. Again, 0.6% is kind of plausible, the model here (0.1%) isn’t.
I’m sorry, I’m not sure if I understood the relevance of asymptomatic : symptomatic ratio here. I think what’s at stake in this article is the ratio undocumented : documented cases; it’ll include not only asymptomatic, pre-symptomatic or mildly symptomatic people, but people who got really sick but couldn’t be tested until Hubei had largely improved their testing capabilities.
I do think a 50:1 rate is surprising, though not impossible.
If 50% of the cases in South Korea are asymptomatic and so don’t get tested, their true death rate would be ~0.4-0.5%; if you add people who got sick before their testing capability was improved, etc., it may be lower. But again, I really prefer to be pessimistic in my death rates.
If there is a 1:1 symptomatic:asymptomatic ratio and 2,000,000 odd infections then there are 1,000,000 symptomatic people out there and only 40,000 identified. Of that 1,000,000 we expect 200,000 to require hospitalisation and 50,000 to require ICU.
If this was true I would expect someone to have noticed.
There might be another explanation for the figures that I’m missing but, as I said, I think it’s up to them to explain what they think is going on.
The percentage of asymptomatic cases on the Diamond Princess was even lower than 50%. It was only about 18%. (I trust this figure because the paper has author overlap with the paper that gave a higher figure initially, and it’s written by the same author who made the 0.1% estimate and we’d expect this person to – if anything – have a bias toward expecting a larger number of asymptomatic cases).
About the age distribution on the Diamond Princess: I tried doing age adjustment for it here. ((Edited because I revised some estimates.))
I believe this might be a confusion between asymptomatic and pre/mildly symptomatic—but whatever: the claim at stake is that there’s a ton of undocumented cases out there, not that they’re asymptomatic
They write “at the time of testing.” The study I cite followed up with what happened to patients.
Also relevant: In the last 5 days, 3 more people who had tested positive on the Diamond Princess died. And one person died two weeks ago but somehow it wasn’t reported for a while. So while my own estimates were based on the assumption that 7 / 700 people died, it’s now 11 / 700.
I noticed CDC claims 9 deaths from Diamond Princess, but I didn’t find support in their source. WHO is still counting 8 deaths. I guess you’re right, but I’d appreciate if you could provide the source.
They write “at the time of testing.” The study I cite followed up with what happened to patients.
I know that. If you follow this discussion up to the beginning, you’ll see that all I’m claiming is that the number of documented cases has been affected by selective bias, because asymptomatic / pre-symptomatic etc. cases are unlikely to be diagnosed.
Finally, I believe we both agree the current IFR is underestimating the true death rate, because many patients are still fighting for their lives. Actually, the authors of the preprint are not complete morons and estimate the “time-delayed IFR” in 0.12% (which I agree is too low), and make the following remark to explain the higher mortality in Wuhan:
These findings indicate that the death risk in Wuhan is estimated to be much higher than those in other areas, which is likely explained by hospital-based transmission [32]. Indeed, past nosocomial outbreaks have been reported to elevate the CFR associated with MERS and SARS outbreaks, where inpatients affected by underlying disease or seniors infected in the hospital setting have raised the CFR to values as high as 20% for a MERS outbreak.
I’m not saying this study is right. I’m just saying that, unless someone points a methodological flaw, “their conclusion is too different” is not a reason to discard it.
A Canadian man in his 70s died on 19 March, making him the ninth coronavirus-related death from the ship.[102][46] Two Japanese passengers in their 70s died on 22 March.[47]
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I know that. If you follow this discussion up to the beginning, you’ll see that all I’m claiming is that the number of documented cases has been affected by selective bias, because asymptomatic / pre-symptomatic etc. cases are unlikely to be diagnosed.
Okay. I feel like the discussion is sometimes a bit weird because the claim that there are a lot of undocumented cases is something that both sides (high IFR or low IFR) agree on. The question is how large that portion is. You’re right to point to some sampling biases and so on, but the article under discussion estimates an IFR that it at least a factor 5 below that of other studies, and a factor of 4 (or 3.5 respectively) below what I think are defensible lower bounds based on analysis of South Korea or the cruise ship. I don’t think selection bias can explain this (at least not on the cruise ship; I agree that the hypothesis works for China’s numbers but my point is that it conflicts with other things we know). (And I already tried to adjust for selection bias with my personal lower bounds.)
I’m not saying this study is right. I’m just saying that, unless someone points a methodological flaw, “their conclusion is too different” is not a reason to discard it.
It depends on the reasoning. We have three data sets (there are more, but those three are the ones I’m most familiar with):
South Korea
The Diamond Princess
China
How much to count evidence from each data set depends on how much model uncertainty we have about the processes that generated the data, how fine-grained the reporting has been, and how large the sample sizes are. China is good on sample size but poor in every other respect. The cruise ship is poor on sample size but great in every other respect. South Korea is good in every respect.
If I get lower bounds of 0.4% and 0.35% from the first two examples, and someone writes a new paper on China (where model uncertainty is by far highest) and gets a conclusion that is 16x lower than some other reputable previous estimates (where BTW no one has pointed out a methodological flaw either so far), it doesn’t matter whether I can find a flaw in the study design or not. The conclusion is too implausible compared to the paucity of the data set that it’s from. It surely counts as some evidence and I’m inclined to move a bit closer to my lower bounds, all else equal, but for me it’s not enough to overthrow other things that I believe we already know.
True, but Diamond Princess is full of oldies, and, despite South Korea massive testing, there might be selection bias—I guess people would only get tested if they had some symptom or contact with other infected persons (perhaps you’re referring a more specific study?). Notice that, if the science study claiming 86% of the cases in Wuhan were undocumented were right, this would already imply a fatality rate of about 0.6%, below South Korea estimates.
Yet, I agree the fatality rate is surprisingly low, and it’s just a statistical model.
Diamond princess is important because they did 100% testing so it gives us an idea of asymptomatic : symptomatic ratio. The result was roughly 1:1, nothing like 50:1 or whatever this paper suggests. The science study with 6:1 is at least plausible if you account for symptomatics who weren’t identified.
If South Korea hadn’t managed to test the majority of their cases then it is unlikely that they would have managed to reduce their infection rate so dramatically—their quarantine measures aren’t massively strict although I think the population are self-enforcing good practice pretty well. I doubt that Wuhan death rates could be below South Korean rates due to the acknowledged overcrowding in Wuhan. Again, 0.6% is kind of plausible, the model here (0.1%) isn’t.
I’m sorry, I’m not sure if I understood the relevance of asymptomatic : symptomatic ratio here. I think what’s at stake in this article is the ratio undocumented : documented cases; it’ll include not only asymptomatic, pre-symptomatic or mildly symptomatic people, but people who got really sick but couldn’t be tested until Hubei had largely improved their testing capabilities.
I do think a 50:1 rate is surprising, though not impossible.
If 50% of the cases in South Korea are asymptomatic and so don’t get tested, their true death rate would be ~0.4-0.5%; if you add people who got sick before their testing capability was improved, etc., it may be lower. But again, I really prefer to be pessimistic in my death rates.
If there is a 1:1 symptomatic:asymptomatic ratio and 2,000,000 odd infections then there are 1,000,000 symptomatic people out there and only 40,000 identified. Of that 1,000,000 we expect 200,000 to require hospitalisation and 50,000 to require ICU.
If this was true I would expect someone to have noticed.
There might be another explanation for the figures that I’m missing but, as I said, I think it’s up to them to explain what they think is going on.
More points in favor of a higher IFR:
The percentage of asymptomatic cases on the Diamond Princess was even lower than 50%. It was only about 18%. (I trust this figure because the paper has author overlap with the paper that gave a higher figure initially, and it’s written by the same author who made the 0.1% estimate and we’d expect this person to – if anything – have a bias toward expecting a larger number of asymptomatic cases).
About the age distribution on the Diamond Princess: I tried doing age adjustment for it here. ((Edited because I revised some estimates.))
Maybe CDC screwed their data, but they say 46.5% of the Diamond Princess cases were asymptomatic when tested: https://www.cdc.gov/mmwr/volumes/69/wr/mm6912e3.htm?s_cid=mm6912e3_w
I believe this might be a confusion between asymptomatic and pre/mildly symptomatic—but whatever: the claim at stake is that there’s a ton of undocumented cases out there, not that they’re asymptomatic
They write “at the time of testing.” The study I cite followed up with what happened to patients.
Also relevant: In the last 5 days, 3 more people who had tested positive on the Diamond Princess died. And one person died two weeks ago but somehow it wasn’t reported for a while. So while my own estimates were based on the assumption that 7 / 700 people died, it’s now 11 / 700.
I noticed CDC claims 9 deaths from Diamond Princess, but I didn’t find support in their source. WHO is still counting 8 deaths. I guess you’re right, but I’d appreciate if you could provide the source.
I know that. If you follow this discussion up to the beginning, you’ll see that all I’m claiming is that the number of documented cases has been affected by selective bias, because asymptomatic / pre-symptomatic etc. cases are unlikely to be diagnosed.
Finally, I believe we both agree the current IFR is underestimating the true death rate, because many patients are still fighting for their lives. Actually, the authors of the preprint are not complete morons and estimate the “time-delayed IFR” in 0.12% (which I agree is too low), and make the following remark to explain the higher mortality in Wuhan:
I’m not saying this study is right. I’m just saying that, unless someone points a methodological flaw, “their conclusion is too different” is not a reason to discard it.
I read about the new deaths on the Wikipedia article.
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Okay. I feel like the discussion is sometimes a bit weird because the claim that there are a lot of undocumented cases is something that both sides (high IFR or low IFR) agree on. The question is how large that portion is. You’re right to point to some sampling biases and so on, but the article under discussion estimates an IFR that it at least a factor 5 below that of other studies, and a factor of 4 (or 3.5 respectively) below what I think are defensible lower bounds based on analysis of South Korea or the cruise ship. I don’t think selection bias can explain this (at least not on the cruise ship; I agree that the hypothesis works for China’s numbers but my point is that it conflicts with other things we know). (And I already tried to adjust for selection bias with my personal lower bounds.)
It depends on the reasoning. We have three data sets (there are more, but those three are the ones I’m most familiar with):
South Korea
The Diamond Princess
China
How much to count evidence from each data set depends on how much model uncertainty we have about the processes that generated the data, how fine-grained the reporting has been, and how large the sample sizes are. China is good on sample size but poor in every other respect. The cruise ship is poor on sample size but great in every other respect. South Korea is good in every respect.
If I get lower bounds of 0.4% and 0.35% from the first two examples, and someone writes a new paper on China (where model uncertainty is by far highest) and gets a conclusion that is 16x lower than some other reputable previous estimates (where BTW no one has pointed out a methodological flaw either so far), it doesn’t matter whether I can find a flaw in the study design or not. The conclusion is too implausible compared to the paucity of the data set that it’s from. It surely counts as some evidence and I’m inclined to move a bit closer to my lower bounds, all else equal, but for me it’s not enough to overthrow other things that I believe we already know.