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Comment by Retric | Hacker News Reader

DP excludes the extreme elderly and sick. Remember, people in nursing care don’t go on cruse ships. Further, it’s expected to see additional deaths. So, such simplistic estimates are completely worthless from their data.

What it does do is provide estimates of the rate of infection vs obvious and extreme symptoms. China’s CFR is over 4% and rising, but it’s known that they missed a huge portion of the total case load.

PS: Further to estimate for the US population you need further adjustments. China has a younger population than the US with only 10.8% of the population being 65 and older, but they also have more air pollution etc.

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I would just like to point out the nursing home facts below:

The length of stay data were striking:

the median length of stay in a nursing home before death was 5 months

the average length of stay was longer at 14 months due to a small number of study participants who had very long lengths of stay

65% died within 1 year of nursing home admission

53% died within 6 months of nursing home admission

Source: https://www.geripal.org/2010/08/length-of-stay-in-nursing-ho...

> DP excludes the extreme elderly and sick. Remember, people in nursing care don’t go on cruse ships. Further, it’s expected to see additional deaths. So, such simplistic estimates are completely worthless from their data.

My estimate above assumes that DP's demographics & health conditions represent >=30% of a country's population and it is a lower bound so additional deaths don't affect it.

Your paragraph quoted here does not negate the logic behind the estimate since "the extreme elderly and sick" and those in nursing care can easily be included in 70% of the country's population.

Calling something a minimum does not make it useful. I can find a minimum bound by taking the total number of deaths divided by total global population. But, while true that’s a worthless bound.

Also, if you’re using passenger demographics then you should use the number of passengers infected excluding the 145 infected crew average age 36.

The goal of establishing a lower bound is to inform decision-making. A lower bound of 0 is trivially true and doesn't help decision-making. A lower bound of 0.5% rules out some policy options, though -- in America, it means over a million people would have to die to achieve herd immunity, which is probably unacceptable to political leaders.

The higher the lower bound, the more policy options can be ruled out, and hence the more useful the bound.

I agree, however if you have two accurate lower bounds then it’s only the higher one that’s useful. Aka X > 5 is not useful if you know X > 10.

If you know of a lower bound that's higher than mine, based on as/more reliable data, you should mention/link to it. People are doubting the Lancet paper based on data source (I don't have a strong opinion on that), so this bound based on a different source of evidence should provide additional information for policymaking.

Your lower bound based on the population of earth is clearly not it.

We can argue about specific examples, but I think you can more easily accept errors in your estimate so:

First, there are 12 not 11 known deaths. https://en.wikipedia.org/wiki/Diamond_Princess_(ship)

Second your using both passengers and crew numbers to hit 712, but 145 of those where crew average age 36 and 567 occurred in passengers average age 69. Thus the demographic estimate of 30% also low.

Finally, for accuracy you need to adjust for the healthier than average population receiving better than average care. But, that’s more debatable.

I use data from worldometers.

https://www.worldometers.info/coronavirus/

You can make adjustments and get better estimates with more work. You are welcomed to and let us know the results.

My point is to invalidate some wildly optimistic estimates like 0.1-0.2% fatality rate which is still getting spread around. With some adjustments on my model, the point would still stand.

“All models are wrong, but some are useful.” —- George E P Box

It also lets you judge whether other models are useful. If you see a model claiming an IFR the lower bound of 0.5%, it's wrong.

China is also lying about their numbers. They quarantined late, had a potluck of 40,000 people in Wuhan, let Wuhan people travel all over, then had hundreds of millions of people travelling over Lunar New Year.

Instead of the expected rise in cell phone customers, there was a 21 million customer drop. You'd expect stagnation or even a rise as most other forms of communication were shut downn.

https://www.bloomberg.com/news/articles/2020-03-23/china-s-m...

The companies got censured by the Chinese gov. Next thing you know, they're spouting this stuff.

https://apnews.com/afs:Content:8717250566

But even if you account for all the edge cases they cite to an extreme degree, you still have millions of mystery cancellations.

In case of China it is helpful to see the breakdown of in-Hubei (epic-center where lockdown enforced after widespread infection) / outside-Hubei. In Hubei CFR is around 4.73%, outside Hubei ~0.86%. It roughly shows how many cases they missed.

Obesity plays a big role in Covid-19 deaths. 33% of Americans are obese, and 65% are overweight. There's a possibility that more obese people take cruises--it fits their lifestyles (unlimited food availability, and they don't have to move much). So the DP data may be tempered by more obesity even though it has less extreme elderly.

Also many of the crew got infected. So the amount of viral exposure may play a big part. People who had to come in contact with dozens of infected individuals every day may be the most vulnerable. This doesn't bode well for retail grocery workers.

Of the 712 DP infections, 145 occurred in crew average age 36 and 567 occurred in passengers average age 69. Far from every passenger was obese and few crew where obese. So, I doubt obesity was that far from US norms.

> Many obese people take cruises--it fits their lifestyles (unlimited food availability, and they don't have to move much).

Do you have actual data on that or is this just your prejudice speaking?

Anecdata: I've taken three cruises over the last twenty years, and the obesity rate on those was an order of magnitude higher than what I see in my everyday life as a coastal yuppie -- the only other place I've seen that many people who are that fat was at Disney in Orlando. But America as a whole has a way higher obesity rate than what I see normally, so cruises might just be representative of the country in general.

There have been some reports of the obese prefering cruise vacations because they can't fit on airplanes:

http://www.nbcnews.com/id/6661381/ns/health-fitness/t/obese-...

and

https://www.express.co.uk/travel/cruise/1263369/coronavirus-...

and

https://www.bariquins.com/newsletter/2016/august/03/cruising...

Perhaps you should examine your own "prejudice" before pointing fingers at objective, informed people stating facts.

Also, if the theory about BCG vaccination having a beneficial effect turns out to be true, USA never used BCG whereas China and India did (and UK between 1953 and 2008).

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