Hauke Hillebrandt

Wiki Contributions


[Years of life lost due to C19]

A recent meta-analysis looks at C-19-related mortality by age groups in Europe and finds the following age distribution:

< 40: 0.1%

40-69: 12.8%

≥ 70: 84.8%

In this spreadsheet model I combine this data with Metaculus predictions to get at the years of life lost (YLLs) due to C19.

I find C19 might cause 6m - 87m YYLs (highly dependending on # of deaths). For comparison, substance abuse causes 13m, diarrhea causes 85m YLLs.

Countries often spend 1-3x GDP per capita to avert a DALY, and so the world might want to spend $2-8trn to avert C19 YYLs (could also be a rough proxy for the cost of C19).

One of the many simplifying assumptions of this model is that excludes disability caused by C19 - which might be severe.

Very good analysis.

I also thought your recent blog was excellent and think you should make it a top level post:


Cruise Ship passenger are a non random sample with perhaps higher co-morbidities.

The cruise ships analysed are non-random sample: "at least 25 other cruise ships have confirmed COVID-19 cases"

Being on a cruise ship might increase your risk because of dose response https://twitter.com/robinhanson/status/1242655704663691264

Onboard IFR. as 1.2% (0.38-2.7%) https://www.medrxiv.org/content/10.1101/2020.03.05.20031773v2

Ioannidis: “A whole country is not a ship.”

Thanks Pablo for your comment and helping to clarify this point. I'm sorry if I was being unclear.

I understand what you're saying. However:

  • I realize that the Oxford study did not collect any new empirical data that in itself should cause us to update our views.
  • The authors make the assumption that the IFR is low and the virus is widespread and find that it fits the present data just as well as high IFR and low spread. But it does not mean that the model is merely theoretical: the authors do fit the data on the current epidemic.
  • This is not different from what the Imperial study does: the Imperial authors do not know the true IFR but just assuming a high one and see whether it fits the present data well.
  • But indeed, on a meta-level the Oxford study (not the modelling itself) is evidence in favor of low IFR. When experts believe something to be plausible then this too is evidence of a theory to be more likely to be true and we should update. An infinite number of models can explain any dataset and the authors only find these two plausible.
  • By coming out and suggesting that this is a plausible theory, especially by going to the media, the authors have gotten a lot of flag for this ("Irresponsible" - see twitter etc.). So they have indeed put their reputation on the line. This is despite the fact that the authors are prudent and saying that high IFR is also plausible and also fits the data.
It looks more like you listed all the evidence you could find for the theory and didn't do anything else.

That was precisely my ambition here - as highlighted in the title ("The case for c19 being widespread"). I did not claim that this was an even-handed take. I wanted to consider the evidence for a theory that only very few smart people believe. I think such an exercise can often be useful.

I don't think this is actually how selection effects work.

The professor acknowledges that there are problems with self-selection, but given that there are very specific symptoms (thousands of people with loss of smell), I don't think that selection effects can describe all the the data. Then he just argues for the Central Limit Theorem.

That the asymptomatic rate isn't all that high, and in at least one population where everybody could get a test, you don't see a big fraction of the population testing positive.

There's no random population wide testing antibody testing as of yet.

I do not think that can be used as decisive evidence to falsify wide-spread.

This is a non-random village in Italy, so of course, some villages in Italy will show very high mortality just by chance.

That region of Italy has high smoking rates, very bad air pollution, and the highest age structure outside of Japan.

By the end of its odyssey, a total of 712 of them tested positive, about a fifth.

Perhaps other on the ship had already cleared the virus and were asymptomatic. PCR only works for a week. Also there might have been false negatives. I disagree that the age and comorbidity structure can only lead to skewed results by a factor of two or three, because this assumes that there are few asymptomatic infections (I'm arguing here that the age tables are wrong).

In my post, I've argued why the data out of China might be wrong.

Iceland's data might be wrong because it is based on PCR not serology, which means that many people might have already cleared the infection, and it is also not random.

That's true and that's what they were criticized for.

They argued that the current data we observe can be also be explained by low IFR and widespread infection. They called for widespread serological testing to see which hypothesis is correct.

If in the next few weeks we see high percentage of people with antibodies then it's true.

In the meantime, I thought it might be interesting to see what other evidence there is for infection being widespread, which would suggest that IFR is low.

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