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"But, in a country like the US, the high risk population is much, much larger than the nursing home population. Age or health conditions means that probably at least 100 million people in the country would be high risk"

FWIW, 65% of the 9373 COVID19 deaths in Massachusetts occurred in Long Term Care homes [0]. This is a terrible thing that could have been prevented (or at least mitigated) and do I believe the Commonwealth has failed here. However it does seem to indicate that LTC home occupants are generally more at risk than people not in a LTC home (which seems to make some sense, if you're in a LTC home, it's probably for a reason -- e.g. significant health issues/frailty). We can do better.

[0] https://www.mass.gov/doc/covid-19-dashboard-october-10-2020/...

For sure. Massachusetts seems especially high as a percentage re nursing homes. I believe the national average is ~40%. So, absolutely, a priority should be to focus on nursing homes.

And that's also part of my point - nursing homes were known to be extremely high risk from the very beginning - remember that outbreak in Washington. Despite that early warning, we still, by and large, failed to protect these smaller, super high risk communities. If we can't even do that well, what chance do we have in protecting the much larger "high risk but not super high risk" populations?

If we can't protect the most vulnerable, how can we protect everybody then? Seems like focus should be in order.

I think this is misunderstanding the point. It's not that we can't protect the most vulnerable, it's that we can't protect them particularly well IF community spread is high.

We are partially effective in protecting the most vulnerable, e.g. through restrictions on nursing homes. But, infections in these communities still correlate with the level of community spread - e.g. if you halve the new case rate in nursing homes with restrictions, but you double the new case rate in the community, you may be back to where you started.

The point being, as you allow significantly more spread in lower risk populations, you'll have a really hard time keeping higher risk groups at their current level of transmission risk (which, in the US, is already a fairly high level of transmission risk).

Back in March, when the lockdown was issued in Massachusetts, just a couple of days after CA and NY, it was chaotic. People were lining up for their toilet paper, hand sanitizer, lysol chicken etc. Old people standing next to young, shelves stripped bare in the stores, people driving around from store to store and even going up to NH looking for the basics.

It was a free for all, pandemonium. It had to have been a super-spreading event in it's own right.

In May, Gov. Baker activated the Mass National Guard to help the nursing homes, there were troopers/medics going from home to home, administering COVID tests, checking vitals and, unfortunately removing bodies.

What we _should_ have done is activate the National Guard back in late January to blockade all nursing homes, hospitals etc. to make sure only approved people entered and that they were provided with some of the limited PPE we had and were using it.

Additionally, towns could have organized volunteers to deliver food, groceries and medicines to the vulnerable who were quarantining at home.

So yes, some focus and we could probably have cut the death rate by 80% in MA.

Another interesting question is, what is the economic impact of those high risk people not being able to leave their homes? What happens to the schools and workplaces where they normally serve?

> A comprehensive and detailed list of measures, including approaches to multi-generational households, can be implemented, and is well within the scope and capability of public health professionals.

That is a rather coy statement. They don't even give us a clue how that could actually work.

> Another interesting question is, what is the economic impact of those high risk people not being able to leave their homes? What happens to the schools and workplaces where they normally serve?

Exactly. This is something I've pondered in thinking through some of these proposals - if one wanted to pursue these policies and actually protect high risk folks, that alone would likely have significant impacts.

E.g. KFF estimated that a quarter of workers are at high risk of severe illness with Covid - https://www.kff.org/coronavirus-covid-19/issue-brief/almost-... - of course, some of these folks will be in jobs amenable to work from home. But, for the rest of them? That's a lot of the workforce either a) temporarily leaving the workforce (oh hello, economic consequences) or b) staying in, and accepting the increased risk from community spread (oh hello, more death).

> That is a rather coy statement. They don't even give us a clue how that could actually work.

Yep, lots of hand waving in that one. It's hard to take this declaration with any seriousness given the lack of substance.

My parents are 80, currently in good health, but definitely at high risk if they caught COVID. They're well-off and like to go out and spend money. Any kind of "focused protection" strategy means they have to stop going out and their spending will plummet. They're not alone; I believe "healthy affluent Baby Boomers" is actually a pretty large group of people in Western countries.

Fortunately we live in New Zealand so they can (and do) continue to go out and spend money without fear.

Restrictions on everyone seem to be the alternative (Madrid and Paris are restricting everyone again). That is going to be far worse for the economy.

”who's pretty infamous in the epi community for the fundamentally flawed Santa Clara antibody study - significant statistical issues, a highly flawed recruitment strategy, a side story of his wife recruiting people through a a non-approved side channel....Many of these flaws biased the study into showing a high infection incidence rate, and thus a much lower fatality rate, in the same ballpark as the flu.”

You skipped the part about how it was right. Current IFR estimates are on par with the flu:

https://www.cdc.gov/coronavirus/2019-ncov/hcp/planning-scena...

Moreover, Michael Ryan of the WHO estimates 750M infected globally, which would put the IFR at around 0.1%:

https://apnews.com/article/virus-outbreak-archive-united-nat...

> You skipped the part about how it was right. Current IFR estimates are on par with the flu:

For some reason, I assumed that once we got to hundreds of thousands of dead in the US, that people would move on from this.

Even the CDC puts the IFR around 0.65%. And, for example, you've got multiple boroughs in NYC with hundreds of deaths each that have exceeded a population fatality rate of 0.5%. You've got entire countries that have passed 0.1% population fatality rates. And, of course, none of these places have actually hit 100% of their population being infected.

> Moreover, Michael Ryan of the WHO estimates 750M infected globally, which would put the IFR at around 0.1%

This is only useful if you also estimate deaths, otherwise you are comparing confirmed deaths against estimated cases. Given the level of missed covid deaths, this only works to downplay the fatality rate.

”Even the CDC puts the IFR around 0.65%. And, for example, you've got multiple boroughs in NYC with hundreds of deaths each that have exceeded a population fatality rate of 0.5%.”

No, it doesn’t. You’re cherry-picking old data, and ignoring the links I gave you, which tell you the updated numbers:

https://www.cdc.gov/coronavirus/2019-ncov/hcp/planning-scena...

ages 0-19: .003%

ages 20-49: .02%

ages 50-69: .5%

ages 70+: 5.4%

The old reported numbers in New York are not a counterargument: we now know that number of cases was drastically under-counted, and the inferred IFR, if you use the right denominator, was a fraction of a percent.

You clearly know this, because you’re trying to talk about the rates in “boroughs” which are higher. But sample heterogeneity matters: I’m sure if you compute the IFR in Maimonedes ICU, it’s really high, too. The fact is, the fatality rate estimated by the Santa Clara study has been shown to be correct, and you’re simply spreading misinformation, because these new facts aren’t as scary.

These things are facts. Downvoting and brigading don’t change them.

> Downvoting and brigading don’t change them.

Just to be clear, I did not downvote, nor brigade.

> No, it doesn’t. You’re cherry-picking old data, and ignoring the links I gave you, which tell you the updated numbers:

I think you may not have understood the numbers in your link.

> ages 0-19: .003% ages 20-49: .02% ages 50-69: .5% ages 70+: 5.4%

Calculate the overall IFR from these numbers given the age breakdown of the population of the US, and you'll end up a bit above 0.6%, which is what I said (when referencing 0.65%). Your link explicitly confirms what I said.

> The old reported numbers in New York are not a counterargument: we now know that number of cases was drastically under-counted, and the inferred IFR, if you use the right denominator, was a fraction of a percent.

You misunderstood this. When I referred to NYC boroughs and countries, I was referring to population fatality rates. Those are independent of case rates - it's a measure of what percentage of the entire population of an area has died, independent of infection spread. They help set baselines.

> You clearly know this, because you’re trying to talk about the rates in “boroughs” which are higher. But sample heterogeneity matters: I’m sure if you compute the IFR in Maimonedes ICU, it’s really high, too. The fact is, the fatality rate estimated by the Santa Clara study has been shown to be correct, and you’re simply spreading misinformation, because these new facts aren’t as scary.

That's fine. We can look at entire states. 0.18% of the entire population of New Jersey has died from Covid. 0.17% of the entire population of New York has died from Covid. And does anyone think nearly the entire population of NJ or NY has been infected already? No. Even in NYC, we've only seen a couple of boroughs that have shown around 50% antibody positives, with most in the 20-30% range. And, of course, incidence rates in the rest of New York state are lower than NYC. What does this mean? With 0.17% of the entire population of NY state dead from Covid, with incidence rates generally at 1/5 to 1/3 of NYC, with incidence rates lower in NY outside of NYC, the implied IFR for NY state is north of 0.5%.

With all that said.. you sourced a CDC resource that puts the IFR of Covid-19 above 0.6%. You used it to support a claim of Covid having a similar IFR to flu. Given that your source actually confirmed the IFR I mentioned, does that change your stance?

”That's fine. We can look at entire states. 0.18% of the entire population of New Jersey has died from Covid. 0.17% of the entire population of New York has died from Covid. And does anyone think nearly the entire population of NJ or NY has been infected already? No....And, of course, incidence rates in the rest of New York state are lower than NYC. What does this mean?”

Any number of different things: it could mean that the deaths are overcounted (which seems likely in any case; we keep finding examples of “covid deaths” that are better described as “deaths with covid”). It could mean that there’s uncertainty on the IFR estimates, and it’s a bit higher than the point estimate. It could mean that New York City has a higher percentage of elderly and poor people who died at higher rates than average, which you can’t extrapolate to other populations. It could mean that the virus burned through the most vulnerable population already. It could mean that policy and medical decisions made early on in the pandemic were tragically misguided.

Or, it could be some combination of the above.

Point being, the Santa Clara study was a lot closer to right than wrong, and dismissing it, and the people involved, because of politics and methodological mistakes is a cheap shot.

When it came out, the media and “experts” were routinely claiming that this virus had a fatality rate as high as 3-5%. So I don’t particularly care if they were off by a factor of 3 (~0.5%); they were much closer to correct than anyone else at the time.

> Any number of different things

Yes, it can mean lots of different things, potentially, which is why it is useful to look at factors that may have impacted implied IFRs. E.g. is the population you are studying older than the national average (NY: about average for percent above age 65). Is the population unhealthier than average, e.g. looking at obesity rates (NY: significantly lower obesity than the national average). You want to look at how many deaths were in nursing homes vs not, to see if policy decisions meant disproportionate impact in high risk populations (NY: one of states with the lowest percentage of deaths linked to nursing homes). So many factors were either neutral for NY, or suggest that NY's IFR would be lower than the national average. But, one also considers that NY was stretched thin, which no doubt meant non-ideal care for many people, so that likely countered some of the advantages NY had had.

You also then want to compare with other serology studies, and the implied IFRs from those studies - are we looking at outlier numbers, or does it generally align? Outliers can help uncover factors that were missed, or flaws in the study itself. And that's the thing, all of the NY numbers have suggested an IFR above 0.5%, potentially upwards of 1%, which is broadly in line with other IFR calculations from other places.

> Point being, the Santa Clara study was a lot closer to right than wrong, and dismissing it, and the people involved, because of politics and methodological mistakes is a cheap shot.

> When it came out, the media and “experts” were routinely claiming that this virus had a fatality rate as high as 3-5%. So I don’t particularly care if they were off by a factor of 3 (~0.5%); they were much closer to correct than anyone else at the time.

This is just flatly wrong. You've conflated case fatality rates with infection fatality rates. The general consensus around the time of the Santa Clara study was that the IFR for the US was most likely in the 0.5% to 1% range. The 3-5% numbers were referring to case fatality rates.

In April, I wrote this - https://news.ycombinator.com/item?id=22969994 -

  "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)."

You'll note April is also when the Santa Clara antibody study was released.

> they were much closer to correct than anyone else at the time

If the best science at the time of the Santa Clara study was estimating an IFR of 0.5-1%, and the Santa Clara study estimated 0.12-0.2%, and the current CDC estimate is 0.65%, who was closer to correct?

Given this, does that change your stance?

The Oxford University Centre for Evidence-Based Medicine puts IFR at under 0.35% worldwide, with a high variance between countries.

https://www.cebm.net/covid-19/global-covid-19-case-fatality-...

The COVID IFR is very strongly dependent on age. The U.S. median age is 38, the UK median age is 40, and the world median age is 30.

8-10 years difference in median age is an enormous difference in the proportion of 70+ in the population, and an even bigger difference in 80+. This is a huge part of the reason that you haven't seen the death toll be nearly as high in developing countries as in developed countries. In general, developing countries have a much younger population.

Sure that's a factor, but how do you account for Ecuador with a median age of 28 and a higher per capita death rate than the UK or USA? Ecuador has less healthcare capacity but that can't account for the whole difference. Conversely, Sweden has a median age of 41 and a lower death rate.

It's a statistical association, not a rule. I can't say specifically why those two countries have had the results that they have, but the age dependence holds both at the individual and country levels overall. That also doesn't mean that you can't find a 100 year old survivor and a 20 year old that dies from it.

This is an odd resource, at least with respect to IFR. I didn't look to see if the CFR section was more reasonable. They don't date the commentary in the IFR section, but it seems like it's mostly March/early April? If so, we obviously know a lot more since then.

> We could make a simple estimation of the IFR as 0.35%, based on halving the lowest boundary of the CFR prediction interval in Europe.

I mean, sure, maybe an ok guess way at the beginning.

> In Swine flu, the IFR ended up as 0.02%, fivefold less than the lowest estimate during the outbreak (the lowest estimate was 0.1% in the 1st ten weeks of the outbreak).

We simply weren't doing the surveillance needed for the 2009 H1N1 pandemic during the pandemic to have reasonable estimates for quite a while. We are doing much more of that for Covid - we have serology studies, and we have fairly accurate death numbers (at least in some countries), the key pieces to put together more robust IFR estimates. We didn't have those for H1N1. I also find it a little odd this page just generically references it as Swine flu, vs being specific about it being the 2009 iteration of it. I'd expect that from a news outlet, not an Oxford associated group.

> In Iceland, where the most testing per capita has occurred, the IFR lies somewhere between 0.03% and 0.28%.

Iceland contained their outbreak (yay!), and had very few cases overall, and just 10 deaths. Their CFR is 0.28%. Given their massive amounts of testing, it's hard to understand where the 0.03% comes from (and, obviously, is wildly low). But mostly, there were so few cases in Iceland that's it's hard to draw many fatality rate conclusions.

But mostly:

> Antibody testing will provide an accurate understanding of how many people have been infected so far, and permit a more accurate estimate of the IFR.

> *Estimating CFR and IFR in the early stage of outbreaks is subject to considerable uncertainties, the estimates are likely to change as more data emerges.

Seems to be consistent with this having been written up in March/early April. Since they haven't updated it, and the reasoning even at the time was fairly poor, this doesn't seem a useful resource (at least for IFR).