[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%
≥ 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:
Cheers - have taken this point out.
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:
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.