Preliminary test results suggest 21% of NYC residents have Covid antibodies | Hacker News Reader

Preliminary test results suggest 21% of NYC residents have Covid antibodies

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> In his press conference today, Governor Cuomo revealed the preliminary results of a first-phase antibody test that surveyed 3,000 New Yorkers over two days in 19 counties at 40 locations that included grocery and big-box stores. The sample suggests that 13.9% of New York State residents have the antibodies, meaning they had the virus at one point and recovered. Of the regions tested–Long Island, NYC, Westchester/Rockland, and the rest of the State–New York City had the highest positive rate at 21.2%. The potential good news to come to light is that the death rate may be far lower than some estimates, at 0.5%.

This still is not a random sampling. It only samples from shoppers at grocery stores and big-box retailers. Imagine doing the same study, but of people who ordered groceries online only. Would you expect to see big differences in exposure rate? I think so.

The study itself isn't linked anywhere, nor have I seen it elsewhere. Science is all about the details. It's not hard to imagine half a dozen ways that the bottom line result of this study could have been skewed by decisions made by the study authors and ground team.

This was my thought exactly. Some of my friends in the Bay area got COVID-19 (confirmed positive tests) after quarantining for over two weeks at home and taking all of the precautions. The only place they had been was the grocery store, so they suspect that they got it there.

Are they not taking public transit? Your friends must be some of the most unlucky people ever. The Bay Area has had under 3k cases since the start of April outside group living settings (nursing homes, homeless shelters, etc.). That's ~1/2000 chance of getting covid from anywhere outside.

Remember that’s not a count of cases. That’s a count of the under 3000 people that came up positive after permitted to take a limited availability test.

Take total case deaths, divide by CFR, that’s how many likely total infected there were 3 weeks ago, and then compound daily by the daily spread rate to find how many likely today.

The incubation period runs out to around 27 days, though above 2 weeks is uncommon. It seems possible one of them already had it before quarantine, and just happened to be one of those who had a long incubation period.

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

I obviously don’t know whether that’s the case here but its also possible that some of these anecdotal stories (I’ve heard similar from colleagues) involve someone in the same household having had an asymptomatic case acquired prior to quarantining and transmitting it to others in the same household who then get symptoms after a week or two.

I'm in the East Bay and was quarantining for almost a month before my symptoms showed. I also assume I got it at the grocery store since that was my only outing.

The death rate of 0.5% falls right in the range of what modelers have been using, by the way. Not exactly very positive news.

Yeah, still 150,000 death toll if everybody gets infected. And this is not considering the permanent lung and other tissue damage that's getting reported from "recovered" patients.

You are one order of magnitude too low at 150,000. It's much worse than that.

Yes, you're right.

Right. Really this study is just confirming (with higher error bars than we'd like) what we already strongly suspected.

Note that as others are pointing out, that 0.5% isn't really extrapolated correctly. The tested case deaths are looking like they're only 70% of the total or so. Also using a current death count with contemporary infection counts is a mismatch, because it forgets that ~35% of the people currently in the hospital won't ever leave, which is about 5k more deaths.

So a better calibrated CFR might be 1% or so, which is even closer to the range we've all been assuming.

Except this data is also lagging by three weeks, depending on what antibodies are being checked. Some deaths today have come from infections in April, so the net effect could be a wash.

And where are you getting a 35% death rate for "ever hospitalized"? It's realistically somewhere around 15%-20%, though with major age skew. (https://www.thelancet.com/journals/laninf/article/PIIS1473-3...)

Per https://covidtracking.com/data#state-ny there have been 57103 hospitalizations in the state (not city, so not quite the same population), of which 15021 are still hospitalized. So that's 42082 cases that have left the hospital, of which 15740 died, or 37%.

Obviously there's some apples/oranges here, both due to the population being different from the city and the fact that some of those deaths were outside of hospitals. But it's close.

It's been covered in the past that NYC's total fatality rate has been good but its ICU survival rate has been anomalously bad.

Perhaps a lot of those deaths were outside hospitals (nursing homes, etc.)? Here's a report for all hospitalizations in NYC from March 1 to April 4: https://jamanetwork.com/journals/jama/fullarticle/2765184

21% of patients who discharged or died died.

IFR, not CFR

It is very positive in that we can be more confident that our assumptions are close to reality. 0.5%*330M still equals 1.65M deaths, or (very roughly) 5M ICU beds. Spread out over 52 weeks, that's a lot of ventilators and ICU beds and PPE, but it is a target. A very big juicy target that we can and should aim directly at, and HIT.

It's more or less epidemiologically impossible that you will see an attack rate of 100% (as you just assumed). Once attack rate reaches >40~60%, the reproduction number drops below 1 because there just aren't enough uninfected hosts.

It will probably be significantly higher than 60%, since COVID-19's R0 without lockdown seems to be pretty high. The CDC has estimated it at 5.7 [0]. Herd immunity threshold = 1 - 1/R0, so the threshold would be 82.4%.

[0] https://wwwnc.cdc.gov/eid/article/26/7/20-0282_article?deliv...

That R0 assumes we are taking essentially no measures to stop the virus though, right? I don't no much about it but it seems like the R0 would drop significantly if we keep doing social distancing, expand testing, make people wear masks, etc.

It does, but the point of getting to herd immunity is that we don't want to do any of that other stuff.

The 5.7 number comes from wuhan, during lunar new year, and before viral identification.

So having killed 1.6 million in the US alone and run ICU at 100% for a year, you’re just going to hope people remain immune and the virus doesn’t mutate having passed through billions of people worldwide?

The alternative being...? We know that covid-19 will likely mutate, just like other coronaviruses, H1N1 and its mutations are still around every year; are you suggesting we must remain in quarantine until we both create a vaccine for covid-19 and then every fall quarantine until we're able to develop a vaccine for that year's bout of viruses?

The issue is you didn’t factor in immunity fading and requiring it again. So those that survive will need to roll dice at a random later date.

So if 100% of the USA gets it at 330 M people that amounts to:

1,650,000 deaths.

It is unlikely 100% will catch it from a single wave however.

Which is about 1/2 of what die in a normal year. The real question is how much do those circles overlap.

Are we saving lives, or are people now dying in June that would have died in November anyway?

There's a metric called something like "estimated years of life lost" based on mortality rate by age and life expectancy, but you'd also want to factor in individual life expectancy—a lot of the people dying from this already had other health conditions.

Yes more people will live if they have access to care because the system isn’t overwhelmed. People in the later stages will also benefit from the institutional knowledge hospitals gain from treating patients. There will also be more PPE and essential medication that will make treatment more effective. Time is our ally.

This is like the third or fourth spot I've seen on this very site where people are postulating that covid deaths would have really died in "months" anyway. Why is this so weirdly consistent?

To be clear: there is absolutely no evidence for a number like that, none at all. Typical at-risk patients with comorbidity like advanced age or obesity are routinely expected to live for decades. Actual conditions with a death-in-months prognosis are extremely rare, and tend to require hospital care already. Think advanced heart failure or stage 5 cancer.

If you look at the median age of covid fatalities in Italy at age 81.5, this question is entirely valid. Some of the numbers I read pointed to a 16-20% all cause mortality rate for that group irrespective of covid. So by definition the circles overlap to a meaningful extent.

Can you link to the sources you read?

Also, that 16-20% mortality rate is surely quoted per year, meaning that the expected life expectancy would be 5+ years, not a few months!

Just some numbers:

In Sweden, which only has a voluntary lockdown, over 30% of covid19 dead come from elderly homes[0]. Swedish elderly care is highly focused on keeping the elderly independent (at home) as long as possible, so the people in elderly homes are the ones in the worst state - 20% don't even survive one month from arrival[1] (I saw another statistic that 40% don't survive 6 months, but I can't find that source, google search results are now overwhelmed with covid19 news)

That may point to an upper bound of 30% on the people dying from this that would have died "within months/a year anyway"

[0] https://www.svt.se/nyheter/inrikes/kan-finnas-morkertal-over...

[1] https://www.svt.se/nyheter/val2014/allt-farre-far-plats-pa-a...

There are some bayesian tripwires in that analysis, though I think the data is good and it fits roughly with my intuition that bulk of covid deaths are in the "basically healthy and will live for many years" population.

The fact that a large-ish fraction of the entrants to the care homes die soon means that the people who don't die soon are going to be over-represented in the population. So if you pick a bunch of people from these homes at random, you probably (depending on the total population and total number of such deaths, which isn't cited) don't find people at the edge of death.

So I buy the upper bound, but think the actual number is likely to be much lower.

Stated more intuitively: there just aren't that many people "within months of death" at any time, and there are a lot of people dying to covid.

The vast majority of deaths in Canada (for example since I live here) are those who are 80+ and around half lived in nursing homes. Given average life expectancy in our country is only 82, the vast majority of coronavirus victims' probability of death any given year was quite high.

Herd immunity will cap the total %! The second wave will be less contagious where current infection rate is higher.

You don't get herd immunity until you hit at least 70%so that isn't very reassuring

Currently almost 100% of Americans have not caught and recovered from covid-19. Herd immunity is a long ways off.

Sure all of these studies are potentially flawed but they're all generally pointing in the same direction. There are many more infections than we know about and the CFR is not anywhere close to the actual IFR.

NYC is an outlier with a 21% infection estimate but for the rest of NY (outside NYC metro/Westchester/LI) the estimate is 3.6%. Santa Clara estimate was 3%. LA County estimate was 4%. Seems like a trend is developing.

>There are many more infections than we know about and the CFR is not anywhere close to the actual IFR.

That is the optimistic takeaway.

The pessimistic takeaway is that even the hardest hit areas are nowhere near herd immunity levels and that we are either going to be isolating until a vaccine is created or we can expect to see a lot more death once nonessential people are forced back to work.

> The pessimistic takeaway is that even the hardest hit areas are nowhere near herd immunity levels and that we are either going to be isolating until a vaccine is created or we can expect to see a lot more death once nonessential people are forced back to work.

The third option is, when you take into account that it's approximately as bad as the flu for folks under 40, we let out the young and keep the older folks and the vulnerable inside. This will boost our progress towards herd immunity without materially increasing the death counts.

I have never heard of any healthy person under 40 dying from purely the flu. COVID-19 is certainly less dangerous to the young than the old, but there are plenty examples of it killing young and otherwise healthy people.

See: https://www.cdc.gov/flu/about/burden/2018-2019.html

Search for "Table 1: Estimated influenza disease burden, by age group — United States, 2018-2019 influenza season"

I skimmed through that so please point it out if I missed it, but the numbers don't seem to account for comorbidities. The question was whether COVID-19 is more dangerous than the flu to healthy young people not all young people.

Just because you've not heard about it, doesn't mean it doesn't happen. Seemingly healthy young people die from the flu each year.

Since Feb 1, 204 people from 15-34 have been confirmed to have died from COVID-19 in the US. 162 people from age 15-34 have been confirmed to have died from influenza in the same period.

I don't think you'll find either statistic broken down by comorbidities.

You mind sharing the source on those numbers because the total deaths is somewhat meaningless without the number of infections which I imagine is much higher for the flu? Even those absolute numbers show total deaths being just over 25% higher for COVID-19 than the flu.

Table 2: https://www.cdc.gov/nchs/nvss/vsrr/covid19/index.htm

The statement you differed with is "The third option is, when you take into account that it's approximately as bad as the flu for folks under 40," You're ignoring data in order to make a wishy-washy statement that it must be much worse for people under 40 based on anecdotes you hear.

I don't agree flu infections were higher for those 2 months, either. Distancing has been spectacularly effective against influenza, since it has a lower Rt in the absence of controls. Under 1% of influenza surveillance tests are positive right now, which is a level usually only seen in the middle of summer.

Our best guess for overall infection fatality rate is about 0.3%, double or 3x influenza (because of the very high death rate in the elderly), but COVID-19 deaths overall have been 4x influenza in that time period, indicating that COVID-19 prevalence is higher.

First off, all those numbers are once again absolute deaths and not related to case numbers so my prior complaint still stands. If you look at the first chart, you will see that COVID-19 deaths didn't start becoming a problem until March. Distancing practices were not widespread until mid-March. Meanwhile flu deaths weren't dramatically cut until the most recent timeframe. So taking into consideration both the first chart and the second chart that breaks out the numbers by age range, there seems to be an implication that COVID-19 deaths are much higher for young people in recent weeks. Therefore the absolute numbers would look totally different if March 1st was the starting point. I really don't see anything here that demonstrates the flu is as dangerous as COVID-19 for young people.

We've said approximately as dangerous. It clearly is in a very similar ballpark. We don't have enough data right know to know if it's twice as dangerous for people under age 30, or half as dangerous.

(We do already know it's -way less dangerous- for people under age 18).

Ok, maybe this dispute is based on different definitions. I don't think 2X is "approximately", but I do agree that we don't have enough data right now to know the exact difference so I can't say for sure that COVID-19 is more than 2X as dangerous.

I also agree that COVID-19 does appear to be way less dangerous for the under 18 group. However practically none of the under 18 group is able to isolate themselves away from the 18 and over group so there is no real societal benefit to this distinction.

> I don't think 2X is "approximately", but I do agree that we don't have enough data right now to know the exact difference so I can't say for sure that COVID-19 is more than 2X as dangerous.

I think we have enough data to exclude more than 2x. The range of potential danger has influenza's risk right in the middle of it.

> I also agree that COVID-19 does appear to be way less dangerous for the under 18 group. However practically none of the under 18 group is able to isolate themselves away from the 18 and over group so there is no real societal benefit to this distinction.

There's a huge societal benefit to this distinction, because if children are at relatively low risk, and also do not seem to be index cases very often for how COVID-19 is spread to households, schools can reopen. This is different from most diseases, where schools are often a key mechanism of disease spread. e.g. see https://www.medrxiv.org/content/10.1101/2020.03.26.20044826v...

That's pretty bad considering only perhaps 5% of the population has been penetrated with covid-19 vs 100% for the flu. Assuming a 70% eventual population infection rate, using your data we should expect to see around 2900 deaths in that age cohort. Annualized flu numbers would be around 500. So covid-19 by your data is 6x more fatal.

100% of the population was not "penetrated" with the flu between February 1 and now. Evidence implies a similar proportion of the population was "penetrated" with COVID-19 and the flu in that time. This implies a similar death rate for COVID-19 and influenza in those age groups, as the poster said.

Indeed, influenza has been greatly impacted by distancing. The surveillance numbers for influenza are lower than you'll ever see for this time of year.

I can't exclude it's twice as bad as the flu (or half as bad). But even those broadly agree with the poster's statement.

Not sure why you added the lower limit at 15. The two high risk age groups from influenza are 65+ and under 5. If I recall correctly, pediatric flu deaths are the only ones tracked by the CDC (for other age groups they just use statistical estimates).

There is data on comorbities with the flu from prior years. This article says half of deaths had no preexisting medical conditions.

https://pediatrics.aappublications.org/content/141/4/e201729...

And the deaths among healthy young people from both kinds of viruses is commonly blamed on the cytokine storm:

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4711683/

I picked 15-34 because it corresponds with the data table I cited from the CDC, which lists confirmed deaths from each for the time period. It's already cited and linked, but here's it again: table 2, https://www.cdc.gov/nchs/nvss/vsrr/covid19/index.htm

Note that a few of the deaths are -both- COVID-19 and influenza.

Well even with your qualifier of "no comorbidities", the evidence is pretty unanimous that Covid and Influenza are not even the same ballpark. Worrying about a healthy young person dying of Covid is more akin to worrying about a healthy young person dying of cancer than it is to bacterial meningitis or something.

As I've said elsewhere, Influenza is defined by being deadly to the very young and very old, and so-so to those in the middle. Meaning that healthy young people do regularly die of the flu (it's still rare in absolute numbers, but it happens WAY more than covid).

Unfortunately, and I know people read this stuff and their quack heuristics start firing, the reason people are so afraid about Covid's impact on young people is because the mainstream media has intentionally promulgated a narrative that "young people are at risk too" because they fear that otherwise young people wouldn't submit to glorified house arrest for months straight.

Yes, young people get _infected_ by sars-cov-2, but they do not develop deadly cases of covid in any appreciable numbers. You really should be worrying far more about the flu as it pertains to a college-aged demographic.

EDIT: I do need to find some sources for you though. I saw some NY data broken down by comorbidites but am having trouble finding it.

> Unfortunately, and I know people read this stuff and their quack heuristics start firing, the reason people are so afraid about Covid's impact on young people is because the mainstream media has intentionally promulgated a narrative that "young people are at risk too" because they fear that otherwise young people wouldn't submit to glorified house arrest for months straight.

They've reported that because young people who very likely won't die from COVID-19 (they may still need to be put on oxygen and may have lifelong effects from it, but they likely won't die) can still spread it to those that will. Further, they'll put more strain on the health system, and the ability to care for COVID-19 patients is a big determinant in the fatality rate. Everyone needs to stay practice social distancing and shelter, because everyone can carry and spread.

Young people are also drastically less likely to get severe disease, and drastically less likely to be hospitalized, see FIGURE 1. [1] I find it very suspicious that young people who don't go to the hospital with severe disease would require oxygen or end up with life-long lung injuries.

> Everyone needs to stay practice social distancing and shelter, because everyone can carry and spread.

Sweden has demonstrated at national scale that's not the case. [2,3]

The reality is about 60% of us are going to get it one way or the other, so let's control which 60%, and in what order, and on what timeline before everyone stops listening and just walks out.

[1] https://www.cdc.gov/mmwr/volumes/69/wr/mm6915e3.htm

[2] https://www.bloomberg.com/news/articles/2020-04-19/sweden-sa...

[3] https://theconversation.com/coronavirus-are-we-underestimati...

> I find it very suspicious that young people who don't go to the hospital with severe disease would require oxygen or end up with life-long lung injuries.

https://www.washingtonpost.com/opinions/2020/04/09/my-near-d...

Or I don't know, Google ventilator effects. Lung scarring. Young people get this, and it hurts them, maybe forever. It's not common, but it's absolutely not never, and you can still spread the virus if you're asymptomatic. Seems like people should try and avoid getting it!

> Sweden has demonstrated at national scale that's not the case.

Sweden is so unlike the US in so many ways, this is a worthless anecdote. Imagine the differences in demographics, density, culture, literally everything.

> The reality is about 60% of us are going to get it one way or the other, so let's control which 60%, and in what order, and on what timeline before everyone stops listening and just walks out.

It's by no means certain 60% of us will get it. Especially if we do what we should do and build a competent testing and tracing system. The attitude that this is inevitable is lazy and puts thousands of lives in jeopardy.

Permanent restrictions, drastic social change, and large contact tracing infrastructure might keep it under the herd immunity threshold of ~60%. It might not, too.

But you can't let up at any point, because it'll still be endemic and ready to explode up to that herd immunity threshold.

I prefer approaches that get us to that 60% in an orderly fashion-- staying short of healthcare overload, and preventing as much infection in the vulnerable as possible.

I found the link I was looking for. Here’s some COVID numbers: https://www1.nyc.gov/assets/doh/downloads/pdf/imm/covid-19-d...

I’m on mobile right now so won’t be able to look up the influenza numbers, but perhaps you could find some quality data on influenza mortality in those without comorbidities?

You can't have it both ways. If IFR is low, then that means R0 is way higher than we thought, which also means isolating only the vulnerable will not work.

The fact is, the number of deaths is too high in NYC to be able to cherry-pick your way to an argument that supports your view. Either R0 is really high, and we need to shutdown to prevent it from infecting the entire population in a very short time span, or the IFR is way higher than the flu and we need to shutdown to prevent it from killing a lot of people.

The only sane way to open things back up at this point is to implement widespread testing and contract tracing.

> If IFR is low, then that means R0 is way higher than we thought, which also means isolating only the vulnerable will not work.

No. If you miss a constant percentage of cases, you get the same shape of exponential curve.

That is, a virus with an R0 of 2 and 1 case doubling to 2 and 2 cases doubling to 4.... looks the same when you miss 99% of infections and see 1 of 100 infections doubling to 2 of 200 doubling to 4 of 400.

This fallacy has been common in the response to this data, but it makes no sense. Large numbers of missed cases shift the curve forward and backward in time, and don't change the shape of it.

All of the current findings are still consistent with R0 in the range of 2.0 to 2.5.

> If you miss a constant percentage of cases

We are talking about 1 serological study at a single point in time. We have no idea what percentage of cases we were missing before or after that point.

There's no reason to interpret the serological study to think a higher R0 is implied, which is what you said.

The only way that missing cases affects estimates of R0 from time series is if we're missing a much bigger proportion of cases now than we were in the past. All the evidence I've seen leans the other way-- more testing, better testing, more cases diagnosed by "presumed" criteria.

So, it actually implies the opposite of what you're saying, and argues for lower R0.

I am not interpreting it that way, I believe in the consensus estimates. The parent commenter (articbull) thinks these serological studies (SC, LA, and to a lesser degree NY) prove that the IFR is much lower than we thought because the actual infection numbers are much higher. The NY study, using reasonable assumptions, looks like the IFR might be between 0.5% - 1.0%, but the SC study was claiming 0.1-0.2%. And I am arguing, in the hypothetical case that the SC study is correct (I think it is flawed), then we have under estimated R0.

Considering we have been under lockdown for over a month (which greatly impacts effective transmission), and considering our estimates of latency hasn't changed dramatically, I think my above assumption is fair.

> And I am arguing, in the hypothetical case that the SC study is correct (I think it is flawed), then we have under estimated R0.

I don't necessarily believe the Santa Clara County study, but there are plenty of other alternatives, including a pretty likely one: gross undercounting at the start of the epidemic and/or earlier cryptic spread in communities. This is something that SCC public health officials / Dr. Sara Cody has stated in recent days is her belief-- I am not sure I agree.

At this point we're accumulating a lot of evidence IFR is 0.3-0.5%.

I don't think this a binary outcome. Getting closer to herd immunity also makes things better. Take NYC. Now 1 out of 5 people you see can't infect you (assuming having antibodies means immunity). The closer we get, the better it is. Having 21% "immune" leads to far different outcomes than .1%.

> The closer we get, the better it is. Having 21% "immune" leads to far different outcomes than .1%.

It depends on how long the immunity lasts. If it's permanent, this is indeed great. If it lasts only a few months, this 21% won't make any difference for another wave of infections this fall.

We use the term "immunity" but we should remember that there is a difference between the presence of active antibodies and the presence of immune "memory cells". The latter hang around long-term, even if the former disappear. So at a minimum if one does develop infection they will recover far sooner and with better outcomes. And likely will reach a far lower peak viral load which might bring down transmission.

That is not well established.

It's likely enough, but we don't gain long term immunity to every virus that we successfully clear.

It is well established.

https://en.wikipedia.org/wiki/Memory_T_cell

You are conflating active circulating antibodies with the memory T cells. Which is literally what my entire comment was about.

Of course, there's other confounds. Like the somewhat recent discovery that we end up with antigen experience / "memory" of diseases that we've never contracted. (Perhaps from viral fragments in the environment??)

I'm no expert here, so someone can correct me if I am wrong. But I believe as long as each infected person spreads it to more than 1 other non-infected person the disease will continue to spread until there is a herd immunity level of infections. That spread rate will decrease as more people have antibodies, but it seems unlikely to get below 1 since even all the stay at home orders haven't been able to get that number much below 1.

Sustained spread of the infection stops when percentage of people having immunity is 1-1/R0. So if R0 for Covid-19 is (hypothetically) 3, you need 67% immunity to reach herd immunity. For highly contagious diseases like smallpox that number is very close to 100%

Yes, but there's a world of difference between Rt=1.5 and Rt=2.5, both in the degree of controls necessary to bring Rt close to 1 and in how far it can get out of hand if your controls are somewhat insufficient.

The 1-1/R0 is the point that the virus starts to burn out but there is still a long tail of infections. The total population that is infected will be higher than 1 - 1/R0. Also if the estimates of IFR were too high than the estimates of R0 were too low.

At this point people advocating the position you’re advocating for are in a state of denial (this is my opinion, not a matter of fact, obviously). Your assumption is that we can effectively prevent the majority of the nation from exposure via lockdown.

Not only does evidence seem to point against that, but when you do the math on mortality due to suicide and overdose it’s not clear that containment would even save more lives in the long run.

Here’s how you can tell people’s philosophical positions: if they talk about fear of a “second wave” they are Containers, since that implies the initial “wave” will not infect the majority; ie the virus is successfully contained (EDIT: See https://news.ycombinator.com/item?id=22961927 for the caveat here).

Ironically, leaders like Fauci are verbally saying that containment is not the strategy, yet every word that he says and the IMHE model everyone is relying on are all the result of a Containment ideology.

The alternative is what I would call Pareto mitigation. The vulnerable portion of the population self isolates, while the rest of us are _allowed_ to resume working and living more or less normally (still no large gatherings presumably).

I'd like to take this moment to put out a brief PSA that the serological data coming out, while not 100% reliable, is all telling more or less the same story. Let's look at these IFRs (the second link is CFRs but for Italian healthcare workers who presumably are all getting tested so I'm treating it as a de-facto IFR):

https://old.reddit.com/r/COVID19/comments/g4tqvk/dutch_antib...

https://old.reddit.com/r/COVID19/comments/g6nmtf/update_on_i...

(I'm linking to the reddit comments instead of the actual study because they're really nice tables and the links are still there for anyone who wants to double check)

As others have said, for those around age 45 or less, Covid is equally or less dangerous than Influenza. And particular for those under 30 the flu is an order of magnitude more deadly at least.

In the general population overall, Covid is undeniably more deadly than the flu, but only about 3-5x (and I think 3x personally right now).

Recall that the flu is characterized by deaths in the very young and very old, while being less harmful to those "in between", purportedly due to the "cytokine storm" which is a scorched earth reaction of the immune system. Covid is very different, it is extraordinarily deadly to the very old, extraordinarily non-deadly to the very young, and about the same as the flu to those in between.

A disease with such a "spiky" (highly variable) mortality rate based on your risk factors is precisely the kind of disease that is most effectively treated with risk-informed self quarantine rather than a national lockdown.

Unemployment is correlated with a 2-3x higher chance of suicide, of which perhaps half can be explained away by mental health confounds [1]. There's unique factors in play here - rampant social isolation and widespead fearmongering, propagated even by health experts and "trusted" news sources at times - that lead me to believe that the spike in suicide and overdoses will actually be much higher than predicted by just unemployment alone.

We're currently at 50,000 suicides per year in the US as a base rate, it is not unimaginable that we would see at least 50,000 _extra_ suicide deaths attributable to a mixture of lockdown and the general socioemotional environment.

I haven't even gotten to the philosophical battle of "freedom versus security". I am, ideologically, someone who drank the koolaid and really believes in freedom and civil liberties over "security" (which I view as illusory anyway), but _even just viewed through the lens of reducing mortality_, the evidence is stacking up that lockdown is going to do more harm than good.

Is the evidence fully settled? Of course not. But it's shocking to me how many people seem to be operating off of the projected CFR's we had in early February, shouting from the rooftops about "1 in 20" people dying (random recent case in point: https://news.ycombinator.com/reply?id=22952764&goto=threads%...). I don't know whether it's just that a large swath of the population already had clinical anxiety which is further magnified by social isolation and social media and news headlines, or whether something else is at play, but I'm very concerned about the state of discourse in the United States right now, and more broadly, the entire world. In fact, ironically I feel a bit luckier to be in the US than some of these other countries because in the US _every_ issue is partisan, which while entirely irrational means that roughly half the country will be in favor of ending the lockdown at any given time (the position I am advocating for, within reason, insofar as hospitals are not overwhelmed), as opposed to other places where you can get given a $1600 ticket for driving a car by yourself, based off of a superstition that _being outside_ causes Covid as opposed to exposure to infected respiratory droplets...

EDIT: Lastly I should mention that in a perfect world we could have voluntary variolation; I would love to be able to expose myself to a controlled dose of SARS-CoV-2 and self isolate for several weeks to ensure that I can never pass on Covid to someone else. Unfortunately that would be very hard to make a reality due to the political environment, even though I am advocating for it to be totally voluntary. I was heartened to see this recent paper toying with a variant of that approach: https://www.medrxiv.org/content/10.1101/2020.04.12.20062687v... (I don't agree with an "Immunity Card" for ideological reasons but I'm glad we have a paper attempting to model it out which does show benefit of voluntary self exposure)

[1] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1732539/pdf/v05...

> when you do the math on mortality due to suicide and overdose it’s not clear that containment would even save more lives in the long run.

I'm waiting for people to do the analysis of mass unemployment from lockdown leading to people losing their healthcare, just in time for the pandemic to widen or return this summer or fall.

You're exactly right to consider all of the externalities of our current approach.

I hadn't thought of it specifically in terms of our broken employer-based healthcare system. That's a really good point.

I quit my job Feb 7, before all this unfolded. And COBRA costs me $612.54 per month.

For the same reason I was able to quit my job, I will be totally fine. I have over 5 years of living expenses in liquid assets, so I will be fine.

But for someone who was living paycheck to paycheck and lost their job because it's not the type of job that can work from home, how the hell could they possible afford healthcare?

Along those lines, I do really think that part of the reason so many people don't see the harm in locking down for the next several months is because, like me, they work in the tech industry and really have not been affected by this as far as employment goes.

https://www.usatoday.com/story/opinion/2020/04/21/behind-pro...

Hey, I'm in broad agreement with you, but, a nit ...

> Here’s how you can tell people’s philosophical positions: if they talk about fear of a “second wave” they are Containers, since that implies the initial “wave” will not infect the majority; ie the virus is successfully contained.

Here in the SF Bay Area we pretty effectively blunted the first wave with public health controls, which meant health care was not stressed much. Despite the Stanford serosurvey saying that we have 3% with a history of infection, the real number is probably more like 1%. Daily case counts are decreasing, so the initial "wave' did not infect the majority.

It's not enough to decrease Rt noticeably at all. We need to figure out how to loosen up controls in a way that provides economic benefits, keeps a nice constantish burn towards herd immunity, but with enough safety margin to prevent catastrophe that New York got too close to.

Instead, our public health seems to be further tightening instead of experimenting with small measures to relax the controls. There's a loud contingent advocating what would effectively be permanent controls.

So, that's definitely a good point to raise. I agree and should have been more clear. I think there should not be a second wave because I think we need to resume more or less normal society and let people naturally get exposed (since voluntary self exposure will not ever be tenable in the US I fear).

But given the way that California aggressively locked down early into this, I agree that we have been sloping down and thus there is guaranteed to be subsequent waves.

Basically, the waves are real but are arbitrary and are caused by our own misguided interventions.

> We need to figure out how to loosen up controls in a way that provides economic benefits, keeps a nice constantish burn towards herd immunity, but with enough safety margin to prevent catastrophe that New York got too close to.

100% agreed. Now the argument I do somewhat agree with is that it's very hard to get that balance right when dealing with exponential transmission. Which is why on balance I'm leaning much more towards "we'll cross that bridge when we get there" (because the alternative is that we have to stay permanently locked down).

Also the portion of the population I am advocating should be allowed to return to work is precisely the portion of the population that produces very low hospitalization rates.

I strongly believe that anyone who has been reading CNN the last few weeks would be _shocked_ to learn that we get 1 hospitalization for every 500 20-29 year olds infected (and again, these numbers are not fully settled but they're at least in the right order of magnitude IMO)

> There's a loud contingent advocating what would effectively be permanent controls.

Yup, this is what has me really scared. The widespread belief being that it is actually plausible to avoid ever getting infected and therefore any infections that follow a softening of lockdown introduce deaths that never would have occurred in the hypothetical alternate universe.

So, thanks for raising that point, I fully agree. The TL;DR is that implicit in "we need to watch out for the second wave" is the notion that "we need to fight these waves and halt their spread" which I strongly disagree with.

EDIT: And just to be clear, if we "re-open" we'll still all be wearing masks and keeping arbitrary distance between each other so it's not like we're all running around exchanging bodily fluids willy nilly. But I really do think that the shutdown has been, in some part, effective in curtailing spread, and thus naturally I would expect a higher infection rate following a re-opening.

EDIT 2: Removed the part about the political leanings of those advocating for long-term lockdown because it's going to set off people's defense mechanisms and potentially cause them to draw the wrong impression of what I'm saying

In San Diego people under 30 are close to 20% of total confirmed and over 5% of hospitalized cases. About 7% of confirmed are hospitalized for this age group. Our positive test ratio is under 7%. Tests are constrained by policy but not dramatically so. https://www.sandiegocounty.gov/content/sdc/hhsa/programs/phs...

Another data point is the US military. I believe they test much more widely due to their living conditions. So far the cfr is about 0.4%.

> In San Diego people under 30 are close to 20% of total confirmed and over 5% of hospitalized cases.

People under 30 are over half the population in San Diego, so you've just said there's 1/10th as many of them in the hospital as we'd expect if the total risk were equal.

The serology data tells us that an even greater portion of cases in the young are missed-- they are probably actually half the number of infections in San Diego, but a much smaller proportion of hospitalized cases.

The current data implies something close to what he said: about a 1 in 500 to 1 in 1000 chance of hospitalization for people under age 35.

Young people interact with more individuals, and engage in more behavior that exchanges microbes such as unprotected sex, protected sex, sharing pipes/vapes, sharing utensils, etc.

We should expect that the proportion of people with Covid should be heavily biased towards the young for that reason, as it is for any disease that does not have a biased transmission.

Most indications I've seen is that the young are not harmed much by covid, but can still get infected just as easily, thus why we see:

"X% of hospitalizations are young people" as opposed to the statement that people actually interpret it as: "X% of young people infected with covid are hospitalized".

Does that make sense?

For any test that is not given randomly or universally, there is a selection bias. A test given based on severity of symptoms bias heavily against patients with light symptoms, presumably younger people. But to get from 7% (hospitalized to confirmed) to 1 in 500 is a factor of 35. BTW the city of Canton (Guangzhou) just tested all their high school seniors in preparation for school reopenning and found zero positive case so far. And they did find cases among other groups.

I don't think anyone enjoys lockdown. At a minimum we need sufficient testing, sufficient ppes, sufficient surge capacity and training for standard supportive care. We are still quite a ways from that. Even if death is unavoidable people deserve to have reasonably good care.

> But to get from 7% (hospitalized to confirmed) to 1 in 500 is a factor of 35.

This is broadly in the range implied by a number of measures across the entire population. 20x for randomized population sampling (Iceland), 20-80x from serology studies, etc.

In this analysis, https://www.medrxiv.org/content/10.1101/2020.04.18.20070821v... 35x is actually the exact factor that cases are estimated to be under-reported by in California. :P

> I don't think anyone enjoys lockdown. At a minimum we need sufficient testing, sufficient ppes, sufficient surge capacity and training for standard supportive care.

Here in the SF Bay Area, ICU usage peaked 3 weeks ago, and controls have been tightening. We're well below seasonal norms for ICU usage at this point. Surely the original controls that caused the original trend down in disease would be good enough now, and could even be loosened slightly further to have more parity with other jurisdictions that are successful.

Thanks for this comment, puts into words what I have been casually thinking. Also, this is the strategy in Sweden, seems to be working out fine for them.

Thanks, I've been trying to find the words as well, which is really difficult when expressing a position that runs counter to "we need to lock down for the next 18 months" is basically characterized as wanting to kill granny.

I generally have to spend more time prefacing the ideas with "I'm not a trump supporter and I think covid is real and I think it's more deadly than the flu and..." than talking about the actual ideas themselves. Especially on Reddit...

I agree with some of what you said here, but there is one specific point I want to disagree with. All the projections for unemployment related deaths are based on society functioning as it previously did. I think many of the people who are suggesting we are in for long term isolation are also suggesting a much bigger increase in the social safety net to help people through these difficult times.

Right, but I think some of that suicide rate comes from the lack of "purpose" (it's silly that we rely on our jobs for purpose but we really do), or more broadly the desynchronization of one's internal schedule that many of us have experienced (which leads to worse sleep and therefore higher mortality).

I also think that given we know about the level of competence of our government, it will be very difficult for "real" safety nets to be put into place. That's not even getting to the partisan divide inherent to our system.

So in my eyes, we had one big problem, covid, and turned it into two big problems, covid and an economy in ruin. And these two problems affect a greater set of individuals than either one alone. Since a huge number of deaths from Covid are those who weren't working because they're 70+.

Lastly, one of the most "famous" social safety nets in America is social security, which is a farce that is known to redistribute money from the poor to the rich. (It is an eternal frustration of mine that the Left in America is so heavily in favor of social security despite it being flawed from its inception) [1]

[1] See Milton Friedman's excellent take https://www.youtube.com/watch?v=rCdgv7n9xCY

I will grant you the lack of purpose being a motivator for suicide, but none of us know how that would transpire during a global pandemic. I know there is debate over the specifics over Maslow's hierarchy of needs nowadays, but generally speaking I think a lot of us will focus less on these internal issues in times of external danger.

Also if we are going to use government incompetency as an argument here. I would throw it right back at you and say I have little faith in the government implementing a reopening plan that doesn't either kill people or damage the economy long term.

Lastly it is a false dichotomy to present a choice between protecting people from COVID-19 or protecting the economy. If we refuse to do the latter, we are going to ruin the economy anyway. Hundreds of thousands of people dying would certainly put a damper on demand. And while there are certainly people protesting about reopening sooner, I don't think think movie theaters would be selling out if they opened tomorrow.

You are missing the point in that if nothing was done in terms of trying to (at least temporarily) contain COVID, then the economy would be just as bad off.

We're currently at 50K deaths in the US, largely in a four week period. I think it's safe to say that the shutdown has cut the death rate in half, if not more. If 100K people die in 4 weeks in the US, the people will shut the economy down whether the government initiates it or not.

Either way we're screwed. And I think your data is far off in terms of IFR and CFR. Imagine the IFR being a magnitude higher for COVID versus influenza. I personally think this is a safe guess based on what we've seen in a wide variety of environments, complicated by different testing methodologies etc etc. So we'll use 1% for the IFR.

Now current thought is the R0 is between 2 and 5. Hopefully closer to 2 since R0 dramatically affects the percentage of infections required to reach herd immunity. But to be simple, we'll assume we need 50% to reach herd immunity. So we'll use 164M as the number of Americans needing to be infected before we can "return to normal."

With 164M cases, and an IFR of 1%, 1.6M Americans will die. An additional 9% will need hospitalization, but survive. That's 14.8M.

Say we're off by a lot and "it's just a flu" with an IFR of 0.1%. You'll have 164K fatalities, and we'll still have the 14.8m hospitalizations. Say the hospitalization rate is off by a magnitude, it's still 1.48M.

There's no way our country can operate "normally" with these types of numbers.

It's not like business was going to go on as normal when people started filling up hospitals.

The lock down was a choice between what we are going through now and a much worse disaster if the spread continued through a couple more doublings. Of course the extreme stay at home orders should be continued for as short a time as possible, but what we do next also doesn't have to be exactly what we were doing before (and realistically won't be).

My impression at the moment is that containment isn't going to happen, but mostly because people don't want to bother, not because it is the more costly alternative or impractical. Given the lack of appetite to test and isolate, we had better all start doing things like wearing masks.

Note that ~80% of American's favor the stay at home orders right now. It's not a partisan issue, it's an issue where a loud minority is getting some attention from the media.

> As others have said, for those around age 45 or less, Covid is equally or less dangerous than Influenza. And particular for those under 30 the flu is an order of magnitude more deadly at least.

Can you share the specific numbers you used to reach this conclusions? The reason I ask is because I'm seeing a lot of people in this thread comparing:

1) Overall influenza fatality rates to age stratified Covid fatality rates, and

2) Symptomatic influenza fatality rates to symptomatic + asymptomatic Covid fatality rates.

Both of which are highly flawed comparisons for reasons that should be obvious.

I’ll try to dig up some sources later today. But briefly, on second thought “under 30” was sloppy of me to say and so that statement is probably false. I would expect for those 25-30 years old they should fall outside the range where Influenza kills a lot. So I think the order of magnitude is almost certainly true for those under 18, but I am now doubting it holds all the way up to age 30.

Following up here:

I came across https://bmcmedicine.biomedcentral.com/articles/10.1186/1741-.... It's not clear if it's referring to CFR or IFR but I imagine it must be CFR given how high some of the numbers are (500 deaths per 1000 in some cases).

I agree that finding properly stratified data that is comparing IFR and not just symptomatic CFR is quite tricky.

At a minimum though, if we assume the chart I linked is CFR and compare to the Covid 5-20% CFRs we've seen, we still get that order of magnitude difference for the very, very young. But like I said in my previous comment here, I don't think the statement for all under 30's holds. It'd have to be <18 years old (and to be clear while https://bmcmedicine.biomedcentral.com/track/pdf/10.1186/1741... is helpful, I still haven't proven anything)

>In the general population overall, Covid is undeniably more deadly than the flu, but only about 3-5x (and I think 3x personally right now).

I don't see how you get close to 3X more deadly than the flu. If 14% of New York state residents have been infected, 20M population, 15000 deaths + another 3000 that are infected now and will die (this disease takes a long time to kill people) you get an IFR of 0.64. If the IFR for flu is 0.05, that makes covid 12X more deadly than the flu.

Lots of people reporting an IFR of 0.5 based on the NY serological data; that is "right censoring" the deaths. It's got to be a bit higher than that. Either way you have covid with 10X the infection fatality rate of the flu. If worst flu years have IFR of 0.1 then covid is still 5X worse than the worse flu seasons. And as contagious as the worst flu seasons too.

So twice as contagious as the flu, 10X deadly. Much more likely to put you in the hospital, and thus overwhelm the hospital system, causing many ancillary deaths. This NYTimes article sums it up: https://www.nytimes.com/2020/04/06/well/live/coronavirus-doc...

At the peak in NY, hospitals stopped seeing heart attacks and strokes, because those people were too afraid to go to the hospital. Many of those people died at home, as supported by the overall death rates in NY.

The narrative put out there by those that look at the recent serology results and say "this proves that this disease was really just a bad flu all along; we can reopen the economy without fear" is just not supported by the data. An IFR of 0.64 and hospitalization rate of double that, like 1.2%, for a disease this contagious, shuts down the economy until we get a vaccine or effective treatment or a Korea-like testing/tracing regime in place.

And one more thing: the Great Depression was great for public health. https://www.pnas.org/content/106/41/17290

Yes suicides go up. But this is more than compensated for by other benefits. Overall, people may well be much healthier in a depressed economy. We will certainly see a decline in car accidents.

Excellent summary. Thanks for taking the time to write it up.

The CFR will be much higher than the IFR. For example, approximate flu IFR is 0.05%.

Exactly, CFR is always expected to be higher than the IFR. Most people getting sick don't go to the doctor. The CFR is an indication of how many people feel they need to go to the doctor and get treatment due to how severe the illness is. And of those that seek treatment how many die.

In comparing the COVID-19 IFR to the flu IFR, it is important to remember that flu vaccines are widely available and limit the spread of influenza. For example, CDC retrospectives for 2018-9 estimate that 35M Americans[1] got influenza over the flu season, or less than 12%.

By contrast, the current COVID-19 infection rate in New York (from these data) is already higher than 12%. So COVID-19 has the potential to infect a larger proportion of the population than the flu usually does.

(If the CDC data is correct, the flu shot may save around a hundred thousand lives per year. Don't skip it!)

1: https://www.cdc.gov/flu/about/burden/2018-2019.html

That point is entirely true but doesn't really change the policy implications unless you believe that we can actually contain this thing indefinitely (i.e. that a given individual can avoid being exposed to it indefinitely)

It's actually important to point out, the CDC is very explicit that they are estimating flu illnesses, in their own words "an estimated 34 million people had symptomatic influenza illness"

Many people are attempting to extrapolate asymptomatic COVID to estimate an infection fatality rate and then making comparisons to CDC flu data for symptomatic illness, which is completely wrong. Actual flu sero studies suggest a large fraction of the population gets flu every year[1] and IFR is more like 0.02->0.05%, so even taking NYC numbers at face value COVID looks 10-50x more fatal than flu.

[1] https://journals.plos.org/plosbiology/article?id=10.1371/jou...

Yes this is happening all over this thread and is absolutely not a valid comparison. I'm also seeing people compare age-stratified Covid rates with overall flu fatality rates which is also invalid for similar reasons.

>IFR is more like 0.02->0.05%, so even taking NYC numbers at face value COVID looks 10-50x more fatal than flu.

Exactly, my only point in mentioning approximate flu IFR was to demonstrate that it's significantly lower than flu CFR. And so the NY COVID-19 serological study suggests a CFR well above 1%.

Is there any actual evidence that the flu vaccines indeed work? Given that 'flu' is actually a very broad term for a disease that can be caused by =many different viruses that also mutate each year.

> Sure all of these studies are potentially flawed but they're all generally pointing in the same direction

That's unfortunately not the way statistics works. Combining multiple bad tests just makes the results incorrect or highly uncertain; the devil often is in the details. The Stanford/Santa Clara County study is a good example of how the details can really undermine a study.

Things do indeed seem to be converging to the idea that the CFR is not near the IFR, but none of this is new news, and the IFR remains very close to what most epidemiological predictions indicated early last month. If anything, it confirms that COVID is a difficult beast to tame.

We don't have perfect studies and perfect data but calling these tests "bad" seems slightly unfair. They give us an imperfect but useful snapshot of what's going on. But thanks for the condescension. I'd expect nothing less on HN.

The study is dramatically over-sampling exactly the people who have the most potential exposure to COVID-19, and dramatically over-sampling the people who don't.

This could easily be way off. It's not testing the people who aren't home at all, and it has a low chance of only testing the people who leave home rarely, only when strictly necessary. It's mainly finding the people who leave home a lot.

How do you know how often these people are leaving their homes? Everyone has to leave their home to go to the grocery store eventually or someone in their family does. Yes there are delivery services but the vast majority of people aren't using those.

The more often they go, the more likely you encounter them.

True, but if you encounter one daily shopper, two weekly shoppers and twenty monthly shoppers, it will skew the results. Same if the numbers are reversed.

I don’t get your point. Maybe we agree. If in one city there are 1000 30-year-old daily-shopers, 1000 50-year-old weekly-shoppers and 1000 70-year-old monthly-shoppers it will not be a good idea to estimate the age of the population by sampling people who enters the store. The average age is 50, not 34.

Most people in NYC are almost certainly not shopping monthly because of storage constraints. Even bi-weekly shopping is likely to exhaust storage space for city-dwellers in small apartments.

But you won't, the opposite will happen; you'll proportionally encounter way more daily shoppers than infrequent shoppers, for the simple tautological reason that the daily shoppers are going more often.

This is a sampling bias, and it's the exact same problem as if you ran a survey trying to ascertain how many people have landlines ... by calling random phone numbers.

I think the overwhelming majority of people are visiting grocery stores to buy food. (Or have someone in their household doing it)

If the studied presume this then it makes more sense.

You can't assume that. I know plenty of people who haven't left their apartments in a month plus, because they're having groceries ordered in.

And you completely missed the point that people who are going outside a lot more often are both (a) more likely to have been exposed to COVID-19, and (b) more likely to be encountered by your survey. If there are e.g. 700 daily shoppers and 700 weekly shoppers going to a given store, then if you go on one day and sample every shopper you're not going to get half and half by population as you'd need to for a true random sample; instead, you're going to get 700 daily shoppers and only 100 weekly shoppers! This is a hugely biased sample!

Are you describing the actual study or the study as you're imagining it? Because I imagine the statisticians have thought of these concerns and attempted to correct for them.

I find it interesting that this study corresponds to an IFR around 0.5%, similar to the IFR arrived at by other studies using very different methods. Perhaps the statisticians who conduct these studies actually know what they're doing better than Hacker News commenters.

And perhaps it's time to accept the politically inconvenient scientific truth these studies are revealing.

>similar to the IFR arrived at by other studies using very different methods

With the wild range of IFR from so many studies, you could trivially cherry pick supporting studies for a bunch of different numbers, especially if you throw in am error range.

The study wasn't one day but one week, and even then, I assume they wouldn't be stupid enough to collect blood from the same person over and over.

There's more than one store in NYC. People shop at a variety of places. Daily shoppers are likely not going to the same store over and over every day. Even during this pandemic I've hit up several different grocery stores and shops, because some things are hard to find.

I’m starting to believe that the difference in opinions on whether these studies are legitimate or not hinges heavily on personal behavior.

For my wife and I and kid, we live in SF and order food when we can from grocery. When we can’t, I put on an N99 mask and gloves and buy 2 weeks worth of groceries. Then go through a ritual to be as safe as possible entering the home and bringing groceries in.

If someone asks me at the grocery store to take the covid test, no way. How would I even do that safely???

I live in San Francisco though and am worried about the fact that the grocery is 70% instacart workers.

Everyone I know in the city (nyc, sf, Boston, Chicago etc.) is trying to use 3rd parties to get their groceries.

Everyone I know in the suburbs is going to the store.

I believe the reason people read this study differently is that their experience is different. The whole foods in downtown sf would be scary to be in without a mask. And I view it as the riskiest place I have to go.

It’s full of gig workers who are taking way more risk than the average person.

We will know more when we see the results of the study, but I think we need a more random sample before thinking this NYC study is even remotely close to showing that 20% of NYCers previously had it

I'm afraid the population who has these options (and spends its time on hackernews) is called the 1% for a reason, and probably aren't going to bias the stats significantly.

This doesn't make any sense. Grocery and food delivery is quite affordable. It's not only available to the top 1%. These options are being widely taken advantage of by a large number of people right now way down the economic spectrum. It's not a tiny minority of the wealthiest people patronizing them.

At any given point of time, a disproportionate number of shoppers in a store are super-shoppers - people that shop more often than the average person and for longer periods of time. Those people are also more likely to have caught Coronavirus before. Without even getting into online ordering, false positives, etc...this phenomenon alone may confound the test results.

Couldn't you get a bad study that reverses the sampling bias to get a decent picture of social distancing strategies using the two bad studies? That alone seems far more useful than knowing the IFR because its actionable information.

Or better yet, do a simple survey and ask the participants how frequently they go to the grocery store. And if you see a correlation between frequency and infection rate, then try and adjust your weights accordingly.

These bias issues are well known among survey designers. There are ways that you can try and account for them. It seems like maybe the medical community is too used to having control groups in their studies?

Most people go to the grocery store. There seems to be this attitude on HN of immediately dismissing research. No human research is ever perfect, flaws and limitations will always be present, and in the present case, study designs are being expedited in order to be timely. Each study is a piece, and together can provide convergent evidence.

OP's whole point is it's not a good sample because people were recruited while grocery shopping...I can't think of a more tech industry-oriented view of the world; most people work from home on zoom and use instacart right??

No. I'd expect people who order deliveries online and can do their entire job on Zoom/slack all day to be a minority to the point where including them in the study would be covered in a sampling error.

You’re completely missing the point. The selection effects would be things like able-bodied / non-immunocompromised / already-recovered people being selected way higher, which would inflate the apparent infected population size estimate and underestimate the death rate among those infected.

This is not some snooty tech point of view - this kind of flaw in statistical studies matters a lot. The more important the policy implications are, the more important the validity of statistical methods.

It’s anything but parochial.

I don't think anyone disagrees that the sample has bias. On the other hand, since the majority of people have no choice but to grocery shop in person, I do not think the sample is as tilted as you imply.

And even if it were--- it's notable that it's this easy to find a subgroup with cleared infections this far above what was anticipated to be found across NY/NYC.

And-- it's remarkably consistent with statistical estimates made through a variety of other means, e.g. https://www.medrxiv.org/content/10.1101/2020.04.18.20070821v...

> “ since the majority of people have no choice but to grocery shop in person”

This is deeply flawed.

I live in NYC and have been making donations to have food delivered to elderly people since this started.

Just the other day, de Blasio announced that taxi and rideshare drivers would be paid by the city to deliver food - and city agencies would be serving as overflow dispatchers and tech support for it.

Many, many people do not have to go to the grocery store, or they have their healthy younger family member go in their place, etc.

To boot, as Gelman has written on his blog, these studies do not agree with a wide range of other studies, and in most cases the test specificity is on par with the incidence rate itself, making incidence rate estimates from _all_ of the studies very unreliable for correlated reasons, so that pooling the studies does nothing to overcome the huge uncertainty they suffer in incidence rates.

> and in most cases the test specificity is on par with the incidence rate itself,

????? We can't exclude that specificity may be 97% (it's unlikely, based on our data, but it's at the edge of the confidence interval), which is unfortunately on par with the studies returning 2-4% positives... but can't exactly explain a return of 21%. Subtract off 4% worst-case false positives from 21%, and where are you?

Since you want to appeal to authority with Gelman, this is what he said about this on his blog: "– Those California studies estimating 2% or 4% infection rate were hard to assess because of the false-positive problem: if a test has a false positive rate of 1% and you observe 1.5% positive tests, your estimate’s gonna be super noisy. But if 20% of the tests you observe are positive, then the false-positive rate is less of a big deal." ... "– In any case, the 20% number seems reasonable. It’s hard for me to imagine it’s a lot higher, and, given the number of deaths we’ve seen already, I guess it can’t be much lower either."

> so that pooling the studies does nothing to overcome the huge uncertainty they suffer in incidence rates.

The case count multiple from the serological study for New York state (and indeed, the California counties) is right in line with what's expected with a variety of statistical measures that were made without relying upon the serological data. So if you even peeked at my source you'd not be making this argument. https://www.medrxiv.org/content/10.1101/2020.04.18.20070821v...

From the same Gelman article:

> First off, 3% does not sound implausible. If they said 30%, I’d be skeptical, given how everyone’s been hiding out for awhile, but 3%, sure, maybe so.

This is a convenience sample no doubt, but convenience is important here, as timeliness is important. It is easy to naysay, but why don't you suggest a less biased convenience sample? Because unless you mandate whole population testing, samples will always be biased in some way. Those who have phones, those who have time, those who are motivated, etc etc. Given all the potential ways it could be biased, choosing a convenience sample at locations almost universally used is not bad at all, imo

> Combining multiple bad tests just makes the results incorrect or highly uncertain

But it's not just, say, taking a bunch of faulty studies and average them and saying voila. They are in agreement. They'd have to be bad in similar ways in order to converge like that.

> That's unfortunately not the way statistics works.

It absolutely is. That's why we have things like confidence intervals.

> Sure all of these studies are potentially flawed but they're all generally pointing in the same direction.

How on earth could you differentiate this from the sample bias?

All three studies created their sample groups differently. Are all three methods flawed in some way? Sure. Is it more reasonable to completely ignore the picture all three are painting or to consider there might be something to the trend?

> Are all three methods flawed in some way? Sure.

They're not just all flawed in some way -- along at least one dimension they are flawed in the same way, that is, they all have a biased sample of the population that seems more likely to be infected than a true random sample.

So, sure, they're "created differently", i.e., they're not all facebook ads, or grocery stores, but all those methods bias towards higher infection rates, don't they?

Given that, you'd expect high precision, low accuracy -- these tests all paint a trend for sure, but we don't know how close that trend is to reality.

These studies have completely different methodologies, and the only way you can plausibly call them similar is by arguing that anyone willing to get serological testing is substantially more likely to be infected than those who are not. That's little more than a fancy way of disregarding any testing result you don't happen to like.

We've been systematically biasing toward testing the old and infected for months now. Why should rates derived from PCR testing with known severity bias considered a strong prior, yet rates derived from serological surveys with hypothetical severity bias be disregarded for the same reasons?

At this point, we have many different studies from different parts of the world -- each with its own methodological flaws and biases -- and yet all are pointing in the same direction: a systematic under-count of cases, and an IFR somewhere between 0.5% and 1%, with a bias toward the lower value.

> arguing that anyone willing to get serological testing is substantially more likely to be infected than those who are not.

Yes, which is clearly true, no?

> That's little more than a fancy way of disregarding any testing result you don't happen to like.

We all have biases. I'm not sure how pointing out that adverse selection is a thing, though, is a bias.

Any stats can be quibbled with, and I have no personal skin in this game -- if anything, I would love (like almost everyone) for the IFR to be <0.1% and for hard immunity to already be a thing. But I'm also aware of (some of!) my own biases.

> and yet all are pointing in the same direction: a systematic under-count of cases

We already know we're undercounting, because we're only explicitly testing people with symptoms, and not even all people with symptoms! We didn't need these studies for that.

These studies would ideally be true random samples so that we can know what the true infection rate is. Some locales are doing that kind of testing, and I'll be much more interested in those reports than studies in which respondents can opt into participation.

"Yes, which is clearly true, no?"

No. As far as I can tell, approximately 100% of people in New York want to be tested, for many different reasons. You're assuming something and projecting it as truth.

"These studies would ideally be true random samples so that we can know what the true infection rate is."

But by your own logic, there can be no "random sample"...we can't tie people down and force them to be tested, so we have to ask them. This means we're back to the self-selection problem that apparently ruins the sample.

The truth is, the New York study did pretty much what we always do to get a random sample: asked a bunch of people at random to participate in the program.

> No. As far as I can tell, approximately 100% of people in New York want to be tested, for many different reasons. You're assuming something and projecting it as truth.

In NY, the issue was not about wanting to be tested, it was about "are you there when they're picking people to test"; quoting from the article, which is very thin on info, people were selected "at 40 locations that included grocery and big-box stores". (In Santa Clara, with the Facebook ads, people "wanting to be tested" by going to a testing center is not anywhere close to 100%.)

The assumption that people who are at grocery and big-box stores are representative, infection-wise, of the population of a whole is not obviously true to me. Many other posters in this thread point out reasons why they might be more likely to be infected.

> But by your own logic, there can be no "random sample"...we can't tie people down and force them to be tested

This approach may go against American sensibilities, but other states (SK, DE, IT) have had better luck with the approach of sampling everyone in a town/region/area. Or at least pick people truly at random, not just a random sample of people who happen to show up in a public place in the middle of a pandemic with a shelter-in-place order in effect. (Also, I'm not sure how "my own logic" makes any claims about the impossibility of a random sample, could you elaborate?)

Even in America, you can at least try to account for the ways in which your sample is likely to be different from a truly random sample. The study by Stanford in Santa Clara county I know at least tried to do this, but of course you can't account for "thinks is infected" as a demographic attribute because that's the dependent variable; I don't know if the NYC study did this type of accounting, but I assume they tried to too.

> The truth is, the New York study did pretty much what we always do to get a random sample: asked a bunch of people at random to participate in the program.

You clearly know about sample bias, so I'm not sure why you think going to a single location (or even 40 single locations) and randomly asking people to be sampled is a good way to get a random sample. Think about going to every Costco and Whole Foods in the state and asking "people at random" for their political views. You're going to get a biased sample, even though you're nominally asking people at random. You can try to account for bias, based on demographic factors you observe in your sample that correlate with political views, right?

But we don't know enough about infection rate to be able to effectively account for infection rate given that people who are showing up at grocery and big-box stores on a random day are more likely to be exposed (they're at a store!) and thus more likely to have been infected than the population as a whole? Literally the only info you have is that they're more likely to be infected -- there aren't well-established demographic correlates with infection.

"In NY, the issue was not about wanting to be tested, it was about "are you there when they're picking people to test""

It's the same critique. If you pick the people anywhere other than from their home, the argument is that they're not at home, therefore they're more likely to have it.

OK, so how do you get them in their homes? If you solicit them on the internet, then there's sample bias because you're telling them they'll be tested. If you knocking door-to-door, it's the same thing: you have to tell them what you're going to do, so you're "selecting for people who want to be tested". You can't win.

"Or at least pick people truly at random,"

You can't force people to take blood tests against their will. You have to tell them what you're doing, and why you're doing it, and they must consent to participate. The same objection always applies to any "random" selection of humans: it's biased towards the people in the place at the time of selection, who agreed to be selected.

"you can at least try to account for the ways in which your sample is likely to be different from a truly random sample."

Right, as all legitimate researchers in this area do.

As I said before: this is all just a highbrow way of rejecting studies for lowbrow reasons. You will never find a survey without some form of sample bias. You control for it and move on.

We are now seeing multiple independent serological surveys with different methods pointing in the same direction. It isn't a methodological error.

> > you can at least try to account for the ways in which your sample is likely to be different from a truly random sample. > Right, as all legitimate researchers in this area do.

Yeah, obviously we agree here. So: how do you effectively control for them in this case? That's my point. It's hard, and you haven't offered any mechanism for this particular case, just assurances that people who know what they're doing do in fact know what they're doing.

Are you one of those people? Please fill us in on what they're doing to address sample bias. They "try to control for it" -- how in this case? So far you've offered nothing specific, just that experts control for it, and you're implying that it's illegitimate to question whether there might be a systemic bias because all the cited studies seem to select populations more likely to be infected.

> OK, so how do you get them in their homes? If you solicit them on the internet, then there's sample bias because you're telling them they'll be tested. If you knocking door-to-door, it's the same thing: you have to tell them what you're going to do, so you're "selecting for people who want to be tested". You can't win.

Am I understanding correctly that you're saying that because you can't get a perfectly random sample you shouldn't try to minimize selection bias? You can't "win", but you can get closer than these studies did. There's a clear difference between a Facebook ad "Stanford seeks people for COVID-19 tests" and "Your number was chosen at random and we'd like to test you for COVID-19 in the interest of science."

One should absolutely try to minimize selection bias, in addition to controlling for its inevitability. In Germany, as you propose, they are selecting people at random from a central database of residents, which is not correlated with whether they have symptoms, feel comfortable shopping in public, etc. That is better than showing up at a grocery store, clearly, right?

As for people who agree to be selected, yes, there's nothing we can do about that in liberal societies. I'd like to know what this rate is. In Germany, I recall that it was low.

> this is all just a highbrow way of rejecting studies for lowbrow reasons

OK, this is the second time you've implied that I'm uninformed, or am acting in bad faith or with bad motives (or whatever "lowbrow reasons" means). None of these are true. I've ignored the personal nature of your vague dismissals up to now in the interest of conversation, but I'm done doing so.

"Am I understanding correctly that you're saying that because you can't get a perfectly random sample you shouldn't try to minimize selection bias?"

No, you're not. I was explaining why your argument is a truism in disguise.

"OK, this is the second time you've implied that I'm uninformed, or am acting in bad faith or with bad motives (or whatever "lowbrow reasons" means). None of these are true. I've ignored the personal nature of your vague dismissals up to now in the interest of conversation, but I'm done doing so."

I don't know if you're doing it in bad faith or not, but you're definitely making a highbrow lowbrow dismissal. You've set up an argument that can never be refuted, for reasons I've explained.

> You've set up an argument that can never be refuted, for reasons I've explained.

I'm arguing that the selection mechanisms they're choosing to use are bad compared to what they could and should be using, namely, random sampling as was used in Germany. Yes, those still have problems as you've mentioned, but they are much better overall. I'm sure there are reasons these studies didn't use those mechanisms -- some bad, like that it's hard to get a good random sample, and it's easy to run facebook ads or camp out at a grocery store, and probably some good, like that it's easier enough that it makes results available sooner.

I'm also arguing that because of this bias, and because we are now seeing a few of these studies with similar biases despite different methodologies, and because, of the papers we could read (namely just Stanford's), the accounting for selection bias is weak, it's risky to make statements about trends indicated by these papers.

You could refute my argument by explaining how these not-very-random selection mechanisms could be effectively accounted for post-selection. I've asked you to do this several times, and you have not done so.

I am not making an argument that can never be refuted. I'm asking you to refute it, and I've given you one potential argument, and you have chosen not to do so, repeatedly.

I'm sorry, but in this exchange, the person putting forth arguments that amount to vague dismissals that can't be refuted was, um, not me.

Anyone worth arguing with acknowledges the limitations of the PCR testing. You are essentially suggesting that we ignore the limitations and biases of these serological surveys. If a handful of internet denizens can quickly point out methodological flaws, maybe the researches should try to address or account for those. I fully understand the urgency of the matter, and that urgency can lead to mistakes. But these studies are going to be used in policy making and could potentially impact millions of lives. The difference between 1% and 0.5% is huge, that is over 1.5 million in the US. And powerful people are trying to use these studies to make a case for re-opening the economy. I think it is completely reasonable to push back on these flaws and biases in light of that.

"You are essentially suggesting that we ignore the limitations and biases of these serological surveys."

I am not. I am suggesting that you are preferring a rate based on an even more biased method, because the method yields a bigger number.

"If a handful of internet denizens can quickly point out methodological flaws, maybe the researches should try to address or account for those."

If a handful of internet denizens can quickly point out "methodological flaws", it usually means that the "methodological flaws" they have discovered are well-known and accounted for. In this particular case, sample bias is not a new statistical phenomenon, and we've been doing serological surveys for a long time.

> I am suggesting that you are preferring a rate based on an even more biased method, because the method yields a bigger number.

What method? I merely stated that PCR has limitations, I never said I was using it to calculate IFR.

For the record, my assumptions are that IFR is around 1%. That is largely based on the Diamond Princess data. The original paper suggested it was 0.5%[1], but at the time there were only 7 fatalities. The current number is 13. The raw IFR is now up to 1.8%. Crudely adjusting for demographics based on the ratio in the paper puts the IFR around 1%. This is the only population that was both comprehensively tested and occurred long enough ago for most of the cases to resolve (although some cases are still active/critical[2]). And I fully acknowledge that this approach has limitations. It is a small population, it happened earlier in the crisis when we knew less about treatment, etc... But I sill think it is the best data point we have.

> If a handful of internet denizens can quickly point out "methodological flaws", it usually means that the "methodological flaws" they have discovered are well-known and accounted for.

Except the Santa Clara study did not do that. They acknowledged potential bias, but did nothing to adjust for it. From the source:

"Other biases, such as bias favoring individuals in good health capable of attending our testing sites, or bias favoring those with prior COVID-like illnesses seeking antibody confirmation are also possible. The overall effect of such biases is hard to ascertain."

The LA and NY papers have not been published yet.

[1] https://cmmid.github.io/topics/covid19/diamond_cruise_cfr_es... [2] https://www.worldometers.info/coronavirus/

> Is it more reasonable to completely ignore the picture all three are painting or to consider there might be something to the trend?

The reasonable conclusion would be no conclusion. The comforting conclusion is to extrapolate a trend from noise.

They are all flawed in the SAME way - they sampled people who they found outside their homes.

People 65+ are NOT leaving their homes.

"Preliminary test results suggest 21% of NYC residents have Covid antibodies"

Should be re-written as:

"Preliminary test results suggest 21% of of people approached in a crowded grocery store and who would agree to give blood have Covid antibodies"

All of these tests suffer the same sample bias, and that sample bias is massive.

People who are already in a grocery store (risky behavior), who are willing to give a blood sample (believes they may have been previously exposed) are not the same as people who are leaving their house only for very limited purposes and ordering food online.

If we assume that the % of people shopping in grocery stores and willing to take the test are in risk group A, and the people not shopping are in risk group B. We can run the following quick analysis.

First, assume risk group A is 5 times as likely to have had COVID than Risk group B (but plug any assumption in there necessary)

Then assume that risk group A is only 10% of the population, then rerun the numbers as follows:

For every 100 people in risk group A who tested, 21 were positive.

Risk group A is 100 people, and risk group B is 900.

Risk group A's 100 were 21 prior positive. Risk group B's 900 could be estimated that 4.2 people per 100 were positive, so in total of the 900 people 37.8 were positive.

21+37.8 = 59 people of 1000, or 5.8%.

Plug any numbers you want in the analysis, but the assumptions drive huge variability in the % of New Yorkers infected. Without a less biased sample, we really don't know much other than that way less than 21% of New Yorkers have anti-bodies.

> Preliminary test results suggest 21% of of people approached in a crowded grocery store and who would agree to give blood have Covid antibodies

Would be useful to know how many deaths from the same zip codes are people who visited crowded grocery stores.

NYC is not suburbia. I believe a higher percentage are still working outside of their homes (and, in many cases, that also means travelling on public transport), and a smaller percentage have the financial and transport resources to significantly reduce the frequency of their shopping trips. Apartment living means a greater exposure to your neighbors whenever you do enter or exit. The sampling in NYC, while far from ideal, may therefore be better than the other regions, in that shopping behaviour may not be as dominant a risk factor as you suppose.

For the same reasons, plus the likelehood that the virus arrived in NYC early and frequently, NYC's figures cannot be extrapolated to the rest of the state or country.

I would like to see how these figures match up to the sewage-sampling method of estimation.

As a (40-something) NYC resident, I don't believe that is accurate. Half of the people I know are working from home and stocked up on 1-2 months of food at the outset of this. The other half left the city and are now staying with friends/family in other states or have rented homes elsewhere.

Those that did stay and still need food (ie produce) are having it delivered.

People who are shopping in stores are absolutely a very biased sample.

Very specifically, this bias excludes older people who are locking themselves away very very judiciously.

Thanks for your observations. If, however, behavior in NYC is more or less the same as elsewhere, then what might account for the difference in the results between store-customer sampling in the city and elsewhere? The results may well be biased from sampling shoppers, but if the bias is assumed to be from shopping behavior, and that behavior is essentially the same everywhere, why does it not wash out?

One explanation might be that it got an earlier start in NYC, but both the hospitalizations and fatalities seem to have started showing up on about the same dates in the city and the surrounding suburbs.

There seems to be a significant ethnic variation, but ethnicity is such a correlate to other issues that it is not likely to be informative on its own.

Just out of curiosity, what studies are you asking the OP to compare?

I’m a resident of SF downtown and previously DC and Boston. Everyone I know has stopped leaving their apartments/condos except for fresh air and are trying not to go to any stores. When possible are having food delivered. My friends in NYC have all left except for one who has a large apartment and no family without taking a flight.

I think people see these studies of places they visited once and determine that those areas are way more dense and that they have seen the news and that all those deaths are due to tons of people having it.

That is just not true. The studies are biased and likely this will go on for a long-time. We know this is way more deadly than the flu. That is obvious when you look at simple examples like the princess cruise in Japan. How often do 7+ people die on a cruise ship on a single voyage?

Please stop using “everyone I know” as a data point, especially when criticizing someone else’s sampling bias.

I am suffocating under the irony.

My point is that another set of pop exists that is not possible to test using the grocery store method.

> Half of the people I know are working from home and stocked up on 1-2 months of food at the outset of this.

This surprises me if for no other reason than it takes a lot of space to store 1-2 months of food. Might be true, but is certainly different than what is going on in suburban Connecticut based on my social network.

Your sample of NYC residents is very likely non-representative...or biased

> Half of the people I know are working from home and stocked up on 1-2 months of food at the outset of this.

Seriously? Your personal network is evidence the this study is useless because of sampling bias?

Everyone I know voted for Hilary Clinton and yet here we are.

> People who are shopping in stores are absolutely a very biased sample.

People who have jobs that allow them to work from home are a very biased sample. How many grocery stockers are there in NYC compared to let’s say, software developers? I suspect you haven’t a clue, since by your own admission you don’t know a single person who needs to leave their home to work. So maybe your mental model if who makes up the population is skewed.

> People who have jobs that allow them to work from home are a very biased sample.

Yes, exactly. ...just like the study's data that excludes them.

That's my point.

Sure, I agree with you, excluding them is biased as well. A more appropriate phrase to be used to describe "people who have jobs that allow them to work from home" is "a small minority".

And let's be real, biasing your sample by not including a small minority doesn't affect the outcome of a study nearly as much as doing the opposite and sampling mostly from that small minority. Does it make the results less accurate? Yes. Does it make the results nearly as inaccurate as it would be if they excluded most of people but that minority? Not at all.

Since false positives can be low single digit percentages, anything outside the NYC data is pretty meaningless, and a bunch of meaningless data is still meaningless.

Death counts in NYC, combined with a 1% CFR, indicate that the number of infected was ~1.5M 3 weeks ago; probably 2.2M now. That's about 25%.

Nope, because they're all designed to skew higher. People who are out shopping, people who will go out of their way on a weekend to be tested, healthcare workers... all of them are likely skewed high. One might as well conclude (with as much evidence) that the purpose of the tests is to show higher infection rates. Still, nobody doubts that we're under testing by a factor of 3-5x to catch a majority of cases.

What's disturbing is that if you take the estimated statewide data of 2.7M infected (out of ~20M) at face value and only the current 21k dead you still get 0.7-0.8% fatality rate.

In support of my statement, it is now noted that the lead professor's wife sent out misleading emails to find people who wanted to return to work, if they tested positive. The professor says he knew nothing of it and because they are wealthy they would skew low rather than high.

BuzzFeed News: A Stanford Professor’s Wife Recruited People For His Coronavirus Study By Claiming It Would Reveal If They Could “Return To Work Without Fear”. https://www.buzzfeednews.com/article/stephaniemlee/stanford-...

> New York City had the highest positive rate at 21.2%. The potential good news to come to light is that the death rate may be far lower than some estimates, at 0.5%.

How do they get to 0.5%? According to NYC data if you include "probable" cases, 15,400 people, or 0.18% of the population, are already dead. Wouldn't that imply 0.9% fatalities if the whole city was infected?

It would not be ruled as a significant factor leading to death in many of those cases, and for the rest, NYC residents might be more at risk due to higher average viral dose (subways). Also there is a lag in IgG antibody production, the test has imperfect sensitivity, and some portion of exposed people may never develop detectable antibodies. Some of these confounding variables are probably baked into the estimate.

> some portion of exposed people may never develop detectable antibodies.

Interesting. Would those people still develop some form of immunity/resistance?

I think so, but I'm not an expert. See discussion: https://news.ycombinator.com/item?id=22958660

Maybe that’s including the large number of deaths that are presumed COVID but weren’t officially confirmed by a test?

The data that NYC publishes daily is super interesting since it breaks down cases/deaths by race, gender, age (group), and presence of underlying health conditions [1].

The gist of it is that the majority of deaths from COVID-19 were suffered by people who had some kind of other health issue. As of the most recent data (April 22 at 6pm), 61 deaths were from people with no underlying health issues, 7,474 from people known to have underlying health issues, and 2,755 from people with unknown status [0]. The elderly in poor health are much more likely to die than the young in poor health. But when healthy, the elderly have pretty close to the same fatality rate as the young [2].

[0]: https://www1.nyc.gov/assets/doh/downloads/pdf/imm/covid-19-d...

[1]: Bottom of this page: https://www1.nyc.gov/site/doh/covid/covid-19-data.page

[2]: Cross reference this with [0]: https://www1.nyc.gov/assets/doh/downloads/pdf/imm/covid-19-d...

The majority of people are unhealthy

> This still is not a random sampling. It only samples from shoppers at grocery stores and big-box retailers. Imagine doing the same study, but of people who ordered groceries online only. Would you expect to see big differences in exposure rate? I think so.

There's a larger, related problem with this: It's way over-sampling people who go outside a lot and make unnecessary trips, and undersampling people who don't. I'm in NYC and I'm only going to one store a week (the grocery store). So I'd have much less chance of being included in this study than someone who hasn't changed their habits and is still shopping much more frequently. And there's a direct correlation between how often you're going shopping and how likely you are to have been exposed to COVID-19.

So yeah, it's very far from a random sampling, and it is specifically over-sampling people who are more at risk. So I'd take it with a grain of salt; it could actually be way off.

They should've activated the jury selection tool for this and gone to people's apartments to draw blood. That way they'd correctly sample the people who aren't leaving home at all, or only do so rarely.

I'd expect people who still do their own grocery shopping and go to big box retailers to be a better sample of the population than hacker news' audience definition (ordering from instacart/whole foods, working on Zoom/slack all day).

The vast majority of the population does not have the privilege that many people in the tech industry do. Going to a grocery store is a pretty basic activity in most of the population. People who get their groceries delivered online and can maintain their livelihood being on Zoom calls all day are probably statistically insignificant enough to be covered in the sampling error.

It's not black and white. A person who goes to the store more often has a greater chance of being tested, than someone who goes infrequently.

There's also the issue that people are trying to avoid other human beings right now, including the human beings administering tests. People who are social distancing more effectively are less likely to agree to a test.

Sure, but it's still data for the groups that they sampled. It would be better to have data for truly random samples.

But even then, you'd want demographic data for each subject. And then you could compare appropriate subsets of the results with this study.

With such data, you could explore risk factors, effectiveness of quarantines, and so on.

Except your hypothetical "never leave house" population is probably <5% of the population - every single person I know is still going out to public places at some time during the week.

I literally made the same comment a few days ago but here goes: every thread about a study such as this one is has criticisms concerning the scientific accuracy even though all these studies point to the same conclusion and not one of them provide evidence to the contrary.

This isn't exact science, we're in the middle of a pandemic. But all these antibody studies are pointing towards the same conclusion.

Do they all use the same testing mechanism? If they do, a flaw in that mechanism could result in them all being incorrect or misleading in the same way(s).

I can't believe people are still screaming false results at this point every single random sampling has been the same or point to more infections. At this point based on everyone's method of random sampling that should merit the badge of random sampling, but people argue it on every post.

I agree. I'm not a conspiracy nut but it's getting hard to ignore the feeling ulterior motives are at work here.

The biggest problem looking at these tests is that they are completely on the opposite side compared to the mortality estimated using South Korea numbers. We know for a fact that South Koreans tracked pretty much every single person with covid given that their new cases are increasing by a single digit per day. We know that as of today they have 10708 cases and 240 deaths, with still 1967 active cases. This give us a lower bound for the mortality at 2.2%. I think this is the best way to estimate mortality and I think that a lot of studies are giving numbers for the infected that are just out of touch with reality. I remember an Oxford study that was saying that half the UK was infected at the beginning of April. Those numbers were simply insane. I still don’t have a perfect explanation why the mortality numbers from the antibody tests seem so low, but I think that the accuracy of these tests or the way they select the people to be tested may be involved. Do you see any flaw in using South Korea numbers to calculate the mortality?

South Korea apparently has conducted 11500 tests/ 1m population. It's ridiculous to claim that they detected every covid case and to suggest that their cfr is a lower bound for the ifr.

They tested everyone that was in contact with a reported case, I never claimed that they tested the whole population. How do you explain that they are showing only a single digit increase of new cases per day if, as you assert, there is a huge number of unaccounted cases?

Probably by implementing strict quarantine measures at the start of the pandemic. They likely had many undetected cases in the beginning as well, the quarantine reduced the pool of infected people a lot. Given that the number of infected people is relatively low right now, their testing capacity probably allows for fairly good tracing of cases, which is one of the primary reasons why their numbers remain low, in addition to factors such as social distancing and high hygiene standards. But the large number of undetected cases in the beginning would would reduce the ifr significantly.

This study doesn't appear to suffer from the same methodology problems as the Stanford study of a few days ago. In that study, they recruited people through Facebook, and reported an infection rate that was low enough that it could have been caused by false positives.

Here, it's closer to a random sample, but more importantly it shows really high rates. Those rates overwhelm any error due to false positives.

Also, it shows numbers that are in line with our intuition. It shows higher infections in NYC, and higher infections among blacks. That reflects what the hospitals are seeing.

This study may well be really good news.

What's weird is that the Stanford study was suggested to have exaggerated the results, since those who suspected themselves as having the virus at one time were more likely to volunteer (allegedly). However, those results were nowhere near as big as this (I think like 2%-3% versus the 20%!)

Roughly 1/1000 New Yorkers have died of coronavirus. At a 1% IFR, that suggests at least 10% of the state has gotten the virus. Probably more, because deaths are undercounted and many people who will die of the virus already have it but haven't died yet.

In the Bay Area, almost no one has died of coronavirus so the infection rate should be next to nil.

I was astonished by your 1/1000 figure and had to look it up... If anything, its too low. 15k deaths (proven+probable) in 8M people is almost 2/1000.

https://www1.nyc.gov/site/doh/covid/covid-19-data.page

I was referring to the state -- but, yeah, it's even worse in the city :(

and 2/1000 people * 1% IFR => 20%

that's crazy to me, what are the demographics of NYC? Is the population more susceptible to dying of Covid19 somehow? I still can't get my head around why NY has suffered so disproportionately.

It's a small state with a lot of people packed next to each other. Lots of people use public transit... subways are less than six feet apart.

If the NY subway is anything like London, people will be less than 6 inches apart at rush hour. I imagine that will have played a big part in the spread in both of those cities.

Yes, the subway is the correct answer. That's how this virus spread like wildfire in NYC. Second reason could be the elevators in the tall buildings (there are plenty of those).

A counter argument is that the same didn't happen (or hasn't happened yet) in cities that also have massive public transportation systems like Tokyo or Seoul. There is probably many confounding factors, such as mask use, no talking on the subway, etc, to make pointing to one particular factor very hard.

EDIT: typo

Those cities see more widespread use of masks generally. And specifically once COVID-19 was common knowledge, so was the use of masks.

Widespread mask usage doesn't happen in NYC, same as the rest of the U.S. It's very recent this is practiced in the U.S., whether voluntarily or by order of a handful of local governments. And in my local area where it's not mandatory I only see about 3 in 4 using masks.

And Japan has a culture of talking quietly which greatly reduces droplet transmission even beyond the practice of wearing masks.

NYC has a culture of yelling.

Do you get yelling on the subway in NYC? Passengers in London famously don't even make eye contact.

Yep, I agree with you. I was trying to make the point that massive transportation is not the only factor at play. Like you said, there are many others that should be considered.

I don't think that fully explains it. There are plenty of European cities of similar density to NYC that haven't been hit nearly as hard. Naples Italy, for example.

population density. you have the density of Asia without the propensity to wear masks.

There are many close-knit communities in NYC and the surrounding area where people live much more densely (i.e. large family in a single house) and congregate more often with one another than do West Coast types. Jewish communities in New York seem to be especially hit by this; not sure if this is because they did not follow social distancing guidelines, or if they're not possible to follow given unavoidable physical constraints.

> Jewish communities in New York seem to be especially hit by this

From my understanding they are not hit any harder than other groups, they are just way more visible.

Yes, if you look at Israel and Montreal, Ultra-Orthodox communities were hit very hard. Based on infections numbers, not deaths(since it's mostly very young population). Lack of communication and distrust of local authorities (only praying to G-d will help...)

The most likely answer is that NYC was just a good place for it to spread which had plenty of travelers to get things started fast, but that that the death rates aren't particularly high -- everywhere that has fewer deaths has just had less of their population infected ... so far.

Germany has about as many cases in total as NY, yet less than 1/2 of the deaths. The death rate seems high in NY, maybe because hospitals could not cope?

Germany has about as many diagnosed coronavirus cases; the difference could be wholly explained by Germany testing more people. (Germany has administered 2 million tests, New York State [I didn't see a figure for the city], about 700 thousand.)

In my opinion it is more likely that the main cause is that the infection got here very early, and that the mitigation and containment measures started very late in comparison with cities of similar size and density (Seoul for example).

A dense, mega-city is the worst case scenario for an infection to spread.

> I still can't get my head around why NY has suffered so disproportionately.

Compared to California, NY locked down much later and did it in slow, incremental steps.

But we already know the actual infection rate is higher in NYC than CA. The main point that the antibody tests help uncover is death rate.

NYC and standord are markably different scenarios. But in each case the percentage of baseline infection to deaths is similarish, even though the percent compared to total pop is quite different.

Nope, the Stanford study had a much lower prediction of the infections to deaths ratio, but that's because its analysis of the data was just wrong.

What's interesting is if you look at the rest of NY numbers (-NYC,LI,Westchester). That has infection at 3.6%. Santa Clara was estimated to be 3% and LA county 4%. So it's in strong agreement with those areas.

But the number of deaths does not agree.

Can you elaborate?

Percentage of people with antibodies is similar (if you take those studies at face value).

Percentage of people dead is not.

There also appears to be a difference in the strains. The NYC strain seems to mostly come from the European branch, while the CA infection comes directly from China (and likely much earlier in 2020). There appears to be some difference in virulence between the strains.

https://www.medrxiv.org/content/10.1101/2020.04.08.20056929v...

and

https://nextstrain.org/ncov/global

I’ve never looked into that (I know of the hypothesis, though) but IFR in South Korea also seems to be around 1% (or higher), not 0.1%

Exaggerated in that the California study predicted a flu like IFR (Ioannidis, one of the authors, has been pushing that as his pet theory for over a month). This shows it is more likely several times that.

it doesn't contradic Ioannidis at all. He might be right in the end, and that would be an incredibly bitter pill to many.

We are getting closer and closer to the flu fatality rate.

from 5% to 3%, then 1% now 0.5% - smart money (and common sense) would bet that the rate will continue to drop,

I would expect that people living in healthier environment than NYC will fare even better. No way NYC IFR is the upper bound for the rest of the country. You could just as well expect it to be 10x higher than other places.

the flu rate is 0.1%, thus we already hit the order of magnitude.

NYC is a younger, healthier city than the national average. NYC also has one of the best medical systems not just in America, but in the world.

Roughly 0.2% of everyone in NYC has already died of COVID-19. So 0.2% is pretty close to a hard lower bound on the IFR for COVID-19.

And I don't think anyone serious suggests every NYC resident has had COVID. I don't think anyone seriously suggests even half of NYC has had COVID.

All I am saying people there breath the NYC air every single day. Can't possibly be good for them, especially in the light of some chronic pulmonary inflammation induced deaths.

The point on the lower bound of 0.2% is informative. I did not know that.

That lower-bound argument is overly simplistic.

There are huge error bars on that ratio, because "the population of NYC" is not something easily defined, and the death count (at this time) includes a lot of "excess deaths" that almost certainly have nothing to do with the virus (e.g. untreated cardiac arrest).

That it's a lower bound for IFR in NYC. With how widely that changes based on age alone, it could also vary based on location. Such that NYC could conceivably have the highest value for that in the US.

NYC is younger and healthier than the US average so these are lower bounds for national average outcomes.

A datum that is hard to square when the deaths are still dominated by the older population. If you had convinced everyone over seventy to move out of NYC last year, their CFR would be a fraction of what it is now.

So the IFR will be lower in the rest of the country than in NYC because... they are going to send everyone over seventy to the Moon or something?

No. I was not claiming it would be lower. Apologies if the framing said it that way. I was just pointing out that we really don't have bounds on this anywhere else.

I'm not sure how that matters. We have a lower bound for an optimistic demographic representation of the country. If NYC is younger and healthier than average and is at 0.2%, why would the lower bound for the country as a whole be lower?

I am actually having a hard time squaring the claim that they are younger than the average. The number of people over 70 that have died in NYC is above the number of people over 40 in many cities across the nation.

That all said, my point is that we don't know the bounds. Period. It could be higher. It could be lower. That is why I said it is conceivable. Probably it is about that value in most places. I am interested in where the data falls.

NYC is on average younger than the broader US population.

And the deaths are concentrated on the older population. They do have younger people. They also have more nursing homes and assisted care. They literally have more of everything.

We're looking at per capita rates though so doesn't matter if they have more of something in absolute terms.

Could. If the agitating factor to severe cases is car exhaust, as an example. There are usually tipping points to that kind of thing.

Look, I agree that I don't know. Just trying to get that uncertainty in the counter claims, as well.

I’m talking about having more number of nursing homes because they have “more of everything”, not making an argument about density and pollution or anything like that.

Ah, yes, I misstook your argument. That said, I do suspect having more nursing homes means they have more people over 60 than most places. Which will skew them to have more deaths, period.

Consider, from all that we have seen, elementary schools could get 100% infection rate on the same population size that NYC nursing homes have and not see the number of deaths they have had. It is not controversial that the IFR is dependent on how many people over 60 you have in your population.

To drive that home some, here in WA, fully 92% of the deaths have been in people over the age of 60. It is quite ridiculous how deadly this is if you are older.

3.4% was never stated to be anything but the case fatality rate. The WHO's statement was:

>Globally, about 3.4% of reported COVID-19 cases have died

Ioannidis acknowledged that in his original STAT article:

>Reported case fatality rates, like the official 3.4% rate from the World Health Organization, cause horror — and are meaningless.

Maybe because he had an editor. But I saw him in a recent video claim the WHO said 3.4% of people who get infected would die. A blatant lie:

https://www.youtube.com/watch?v=cwPqmLoZA4s&t=12m54s

That's some major revisionism. No credible source was ever suggesting a 5% IFR. For example the Imperial model was using 0.9% given UK's age distribution. That looks likely to be spot on.

And Ioannidis? He was quite certain that the CFR was going to be a little higher than 0.1%. Yes. CFR, not IFR. So he's off by two orders of magnitude.

At various points in interviews and articles he used Diamond Princess, South Korea, Germany, Iceland as strong evidence of miniscule fatality rates, in every single case selectively ignoring that deaths lag symptoms which was already well known at the time. All of them had their death rates double or more after he used them, and it was easily predictable based on recent exponential growth and death lag.

In his stat article he was saying it is conceivable if we didn't know about it we wouldn't have been able to even detect it in the death numbers after it ran its course (he has since walked that back).

Even the other day after his serology preprint he was saying it doesn't seem to have a higher chance of killing you than seasonal flu for each person infected: https://www.youtube.com/watch?v=cwPqmLoZA4s&t=1h9m50s

And he claims the WHO said 3.4% of people who get infected would die:

https://www.youtube.com/watch?v=cwPqmLoZA4s&t=12m54s

But they actually said that was the case fatality rate at the time. Their actual quote was:

> Globally, about 3.4% of reported COVID-19 cases have died

His data from the Diamond Princess is completely outdated. He cited 7 deaths. We are now up to 13 with 7 more on critical condition. He has been completely wrong in each of his predictions.

I would respect him more if he just argued from am economic perspective that the economic damage is greater but his wild hypotheses about Covid being comparable to the common cold or flu have been completely refuted by all data.

> We are now up to 13 with 7 more on critical condition.

Minor note, but it appears we are up to 14 now - https://en.wikipedia.org/wiki/2020_coronavirus_pandemic_on_D...

> Another Japanese man in his 70s died on 14 April, making him the fourteenth fatality.

0.1% is the flu cfr. 0.6ish% seems to be the corona ifr. these are comparing chalk and cheese. how many people who get the flu never rock up to a doctor? the ifr for flu is closer to 0.01%.

the worst case scenarios are disproven it is true - but so is the idea that it's just a flu.

let us be grateful this trial run of a deadly global pandemic was only moderately bad.

first you say 0.1% is the CFR for flu then, in the same sentence you claim that it is probably closer to 0.01% because people don't go to doctors with the flu.

Are you arguing that after all this time we still don't know what the actual CFR for flu is? And that the reported CFR is a gross overestimation? - I find that hard to believe.

To me, this feels that once this disease hits the reported flu numbers people start arguing that oh wait, the flu is actually even less dangerous ...

Oh, wait. You don't understand that the CFR and IFR are not the same thing? That explains a lot. I thought you were just being disingenous when comparing the early CFR statistics to the current IFR estimates.

The CFR is, by definition, computed from known cases. It's thus trivial to determine exactly: just divide the confirmed deaths by confirmed cases. So we definitely know the CFR of flu. The problem is, of coures, that it's highly likely to be an over-estimate.

On the other hand, the IFR is hard to determine, since we don't know which cases we missed, nor whether the unresolved cases will end up living or dying. Which is why all we have is estimates.

.1% is flu IFR but if I remember right the number doesn't include true asymptomatics which are estimated at up to 75% (which could bring it down to 0.025%). I'm not sure on this, that was on a CDC page I saw.

I'm sorry but 0.1%-0.2% directly contradicts 0.6%-0.9% (or higher because that doesn't account for the lag between infection and death). That's a 3 to 9 times higher death rate.

I agree with everything you are saying. This does look like good news.

However, we should remember that this is probably a mild overestimate, as the study population was assembled from those who were out and about (shopping at the grocery store, etc). People who are more strictly staying home -- and thus less likely to have been infected -- wouldn't be included in the survey.

Another way to look at it is that 20% of people most likely to contribute to a high transmission rate already have had it, in turn flattening the curve by a considerable amount

Sure, but a person who goes to the store 7 times a week is ~7 times as likely to have been sampled compared to someone who goes only once a week. They are also ~7 times as likely to have contracted covid-19 at the grocery store. It's likely that they are also, in general, isolating less than the other.

This sampling would have a (possibly slight, but still unknown) bias towards people who are isolating less (who you would expect to then have a higher than average percent positive for antibodies, assuming isolating is helpful).

I don't think anyone knows whether or not this is a more representative sample than the stanford study.

Unfortunately, unless you go door to door knocking... you're gonna get biased data.

Or truly random sampling that includes appropriate proportions of sub groups, e.g, socioeconomic status etc. Otherwise you have to know on all of the doors. But it's still a lot: for a 95% confidence level and 2% margin of error, for NYC you would need to test about 2,400 people. Assuming a response rate of 10%, you need to knock on 24,000 doors.

Wouldn't the ability to refuse taking the test introduce bias? i.e. the 10% that do take it still may not be representative of the entire population. Likely better than the grocery store sample, but still not ideal

Yeah, that is still an issue. You can offer compensation to incentivize participation. And you can pre-select your sample (instead of random door-to-door) and require multiple follow ups with the selected persons to reduce nonresponse bias.

You can also do a separate phone survey in addition to surveying the test participants. Questions like "Do you think you have had COVID?" and "How many times per week do you leave the house?". If the responses for the test participants vary significantly from the phone survey participants, you can try and weight your data accordingly.

Sure, phone surveys for political purposes (presidential approval ratings etc.) have to deal with that all of the time. There are methods for estimating non-response impact. [0] One method of mitigating it that I've seen it to reach out again to non-responders. You then analyze their results to see how they differ from the baseline responders to estimate the non-responder population. If there's little/no difference, you can be fairly confident the risk of bias is low. It's called non-response follow up, and is a pretty common method.

There's also literature that suggests that you don't discard outlier values in the actual responders as they may help approximate the non-responder population, i.e., the non-outliers represent typical responders while outliers are more likely to represent non-responders [1]

[0]https://www.warc.com/content/paywall/article/jar/research_no...

[1] https://bmcmedresmethodol.biomedcentral.com/articles/10.1186...

Wow, downvoted for providing factual information on how researchers mitigate non-response bias. Didn't think there was anything controversial there. I'm willing to accept the additional downvoted for this comment though.

Not downvoted by me! Appreciate you sharing this information, it is helping me better understand how researchers mitigate non-response bias. Interesting stuff.

Even with door to door knocking, you're going to introduce a bias -- toward people who are around to answer the door, which would tend to undersample essential workers.

Or people who will try to talk to you from their balcony rather than answering the door.

going 7 times doesn’t actually increase the likelihood 7x but your point still stands that they would be far more likely to get it

Well, sort of. The increase in likelihood of being sampled due to going to the store N times more than the average person is a function of N and P (the probability of being sampled in any given trip) such that

f(N, P): (1 - ((1-P)^N)) / P

For a large P you're right, but as P gets very small (let's say that in any given trip you have a 0.01% chance of being sampled) then the increase in likelihood of being sampled approaches N times as much (in this example, it would be 6.999 times as likely). Of course, this assumes they're taking measures to ensure no one gets sampled twice.

Maybe, more rapid trips may reduce your odds of being there when the sample happens on a day. Such that you may have a lower p. :)

They also said people who went to the grocery store 7x as much were 7x as likely to be infected which is what I think this person was replying to.

otoh, it gives about the same ifr as the recent study in geneva. at the end of the day, it looks like the first wave will have an ifr of about 0.6% - more than ten times greater than the flu, but not the bubonic plague

that's not more than 10x...

Even with worst-case scenario of bias, this is still extremely good news. My worry was always with super spreaders who refuse to isolate but the data is suggesting that those spreaders will still soon achieve herd immunity among themselves. As long as the rest of the society behaves we will still hit zero cases relatively quickly.

(which wasn't always clear to me before since I initially predicted that this will take years to work out)

Superspreaders aren't connected to each other.

What?

All of these numbers are inline with what experts have been saying and modeling for more than a month. We will be dealing with this for at least another year. We will cycle through policy to loosen up and close back down a little. In the best case for NYC they are 1/4th of the way to herd immunity in numbers. With the drop in transmissions, this might be 1/8th of the way in terms of time. There will be more deaths in the future than have been recorded so far.

Look for the bright side in things, but zero cases is a pipe dream.

25% prevalence is absolutely not in line with what experts were saying on March 23rd. Nor was an IFR of 0.5%.

The CDC reports that flu has an IFR of ~.13% in the US (61,000 deaths out of 45 million cases). That makes 0.5% roughly 3.3 times worse, not 10.

Also, herd immunity does not require 100% having positive antibodies, it will show an effect on Ro starting around 65%.

It's not 25%. 0.5% versus 0.13% is not the only issue here in terms of how much worse it is. It's the long time in the ICU. The flu kills you fairly quickly or you get better fairly quickly, so you don't take up hospital capacity so long. Herd "immunity" does not require 100%, but that's a decent approximation. Sure, I'll grant that it "starts" to show an effect around 65%, but the effect is not so strong. 70%, much stronger. 80% very strong. Heck, you could probably do containment by then without waiting to get to 100%. Because inadvertent spreading would be so low.

Bear in mind that the CDC estimate of 45 million influenza cases[1] is the number of symptomatic cases, and therefore it doesn't really make sense to directly compare that with Covid-19 IFR rates calculated from antibody studies which include both symptomatic and asymptomatic cases.

[1] https://www.cdc.gov/flu/about/burden/2017-2018.htm

it would show an effect on r0 immediately, but around 65 is when r0 would hit 1 (since it's got 2/3 fewer chances to spread and starts at about 3)

Herd immunity doesn't mean 100% exposure. It means a high enough incidence of antibodies such that the effective R0 goes lower than one, meaning that new outbreaks tend to shrink over time and not grow.

With most endemic viruses, antibody incidence is somewhere around 30-50% I believe, but I haven't seen any modelling for what covid is expected to do specifically.

If there are a high number of asymptomatic/mild cases that are to infectious to others this means it will have to be more. i've seen numbers between 70% and 80%. Thats why i took 1/4th to mean 80%

> those spreaders will still soon achieve herd immunity among themselves

that's not what herd immunity means, unless we isolate those spreaders so they don't get in touch with "non-spreaders"

The non-spreaders are the people who are self isolating.

I think they're saying that people who go out and spread the disease will quickly catch it, recover, and be unable to catch it and spread it. That relatively small group of people who are refusing to self isolate, will gain herd immunity, causing the virus to die out in that group of people, preventing them from giving it to the non-spreaders that have been at home the whole time.

Whether or not it will work out that way, I don't know.

I don't think you are looking at this right. It will still take about a year (+/- a few months) for this to run through the population of the country. NYC spreads faster and was hit harder and earlier. Still only about 1/5 have had it. The remaining 4/5 (maybe 3/5 if there is some fraction of people that just are naturally immune/resistant) are going to get it. It's going to be a while to get there.

While this dynamic may exist in some form, I don’t think it’s a powerful enough affect to stop the spread. There is not a firm dichotomy of spreaders vs. isolators and the composition probably changes over time, such that the virus still has many opportunities to spread to previously-isolated groups.

In the other direction, people who are sick or have recently been sick may well isolate and avoid going to the grocery store, dragging the detection rate back down.

The major methodology problem with the Stanford study was not that they recruited participants through Facebook.

The major problem in the Stanford study was that they ran a test that has a 3% false positive rate, and found that 3% of the test-takers tested positive. (And that apparently the asymptomatic COVID-19 rate is 90+% - which does not square with the Diamond princess data).

They could have ran that same study back in 2018, and would have gotten the exact same garbage results.

Is there a write-up for this study? Or anywhere with data/methodology released?

Also, is this particularly good news? Using the raw numbers from the headline and the deaths from here https://www1.nyc.gov/site/doh/covid/covid-19-data.page we can make coarse fatality rate estimates. I assume population of NYC is 8,400,000.

10290 / (.212 * 8.4e6) = 0.6%

Including probable deaths (which makes the death count line up more closely with excess deaths)

15411 / (.212 * 8.4e6) = 0.9%.

So it seems consistent with a IFR of 0.5%-1%. However, this doesn't account for the lag between antibody presence and death. Also, we don't know what bias sampling from grocery stores introduces, it could affect the results in either direction. Hopefully, New York releases a paper so we can get more accurate estimates.

It's good to see a prevalence study with presumably less flaws.

Sorry, can someone spell out how this might be good news? 21% is still a long way from herd immunity, and NYC's hospital system has been severely strained getting to this point. On top of that, the hospitalization rate still seems disproportionate to someone getting the flu - maybe it's five times as fatal, but it's > 5x the flu hospitalization rate. It's not like the virus has become less dangerous, we're just realizing how dangerous it has been, what with the impact we've already experienced.

Besides, generally if a virus is less fatal than previously expected, it means it's more contagious, meaning that much harder to get to herd immunity.

Good news would be things like: evidence the virus has mutated into something less severe; evidence of an anti-viral treatment that improves outcomes for everyone so it's not as big a deal to catch it; evidence that community spread has halted in an area and the boundaries are controlled so people in that area can feel secure they won't catch it; evidence of an impending vaccine.

Is this good news just because we're finally establishing that people have caught it once can't re-catch it for now? I guess I can see that as good news but that is so expected that it's more like it would be horrendously bad news if we found evidence that recovered people didn't have antibodies. But generally I don't really see what policy impact this has, other than identifying a pool of workers that can go work in meat-packing factories without fear of catching it again.

I don't know if the hospitalisation rate is so out of whack vs the flu. It should be higher. But does 20% of the population catches the flu in a matter of weeks?

Much of the population gets a flu vaccine so would be tricky to compare.

My very point. It contaminates people at a slower pace, for that reason and cross immunity, higher R0, etc.

it is more like would the 20% population go to the hospital if they suspected they had the flu

if they did the hospitalizations would rise as well

one reason the hospitalizations are falling has less to do with the numbers of cases and more with doctors ability to better tell who might need it.

> Sorry, can someone spell out how this might be good news?

It's good news because it strongly suggests that mortality is much less than previously suspected. There were numbers floating around from anywhere between 10% to 3% a few weeks back. A mortality rate < 1% is very good news because it means fewer people will die in the long run.

> mortality is much less than previously suspected

Mortality remains pretty much as suspected already two months ago:

“Based on these available analyses, current IFR estimates10,11,12 range from 0.3% to 1%. Without population-based serologic studies, it is not yet possible to know what proportion of the population has been infected with COVID-19.”

https://www.who.int/docs/default-source/coronaviruse/situati...

The princess cruise ship study also gave an IFR (for China) of 0.5%, and an early epidemiological modeling study put the symptomatic CFR at 1.4% which would imply 0.7% IFR assuming it's 50% symptomatic.

https://www.nature.com/articles/s41591-020-0822-7

https://www.medrxiv.org/content/10.1101/2020.03.05.20031773v...

0.5% to 1% has been the most plausible for at least a month, and this NYC study seems to exactly line up with that.

Two weeks after that the WHO released a widely cited report claiming a fatality rate of 3.4%.

“Globally, about 3.4% of reported COVID-19 cases have died. By comparison, seasonal flu generally kills far fewer than 1% of those infected.”

https://www.who.int/dg/speeches/detail/who-director-general-...

CFR will be higher than IFR due to undercounting infections.

Yes, and then almost the entire US press spun the 3.4% figure as the real WHO-confirmed fatality rate and sub-1% numbers as a Trumpian lie as part of a stupid, cynical, partisan attempt to get Trump. There's been a lot of that. (The UK press, meanwhile, happily quoted the 1% figure - if I remember rightly, some outlets like the Guardian with both UK-facing and US-facing sides pushed both narratives to different audiences at the same time.)

In reality, the WHO number was just confirmed deaths divided by confirmed cases, which was of course almost completely meaningless.

Mid single digit percentage mortality rates were the numbers for case fatality rate, not infection fatality rates. Infection fatality rates have consistently been around 1%.

This comment is entirely wrong. The mortality rates are right in line with what was expected with an IFR of > .5% unlike the Stanford study which was claiming something much lower.

The only good news that I am getting out of this, is that NYC seems to have avoided the dreaded 3-5% fatality rate that was the presumed worse case scenario (massive community spread, overloaded healthcare system). But the lockdown likely helped a lot, and it seems like they are seeing around 1% IFR, so I am not sure this really changes anything. Our understanding of the disease and how to treat it may also be getting better, but it still seems like if we let this thing run wild through the population (as some people on here are proposing) local health care systems will collapse and we will have a IFR orders of magnitude higher than the flu.

And as you pointed out, if the study came back with an infection rate of 50%, I am not sure I would consider that good news either. That means it would be nearly impossible to isolate vulnerable populations. So while a 50% infection rate would mean the IFR is lower, it also means opening things back up and only isolating the vulnerable would not work to protect them.

If the infection rate were 50% we would be close to herd immunity (which I've read would require about 70% for this virus), so that would be better news in a sense.

Unfortunately 21% is a long way from 70%, and it's taken a massive amount of death to get to that point.

The herd gets substantial benefits long before you get to 70% or whatever rate for full herd immunity.

The spread starts to slow before that point. If you're walking around infected and 20% of the people you come in contact with cannot catch it from you, 20% less people are going to catch it, no matter how much you cough on them.

The R0 value impacts herd immunity %. So if NYC is already at 50%, then it means R0 is much higher than we thought, which means herd immunity would probably be as high as +90%. With numbers like that, not shutting down would result in the entire population getting infected in the span of a month or two.

The point I was trying to make, was that for the 'open back up' crowd, they are arguing that the IFR is similar to the flu, and only vulnerable populations are really impacted. So they say we should open up and just keep vulnerable population in lockdown. But they are ignoring the implications of the R0 value in their argument. i.e. if the IFR is really as low as they think (and consequently, the infected population is as high as they think), then nothing short of a total lockdown (or very aggressive testing and contract tracing) would stop vulnerable populations from getting infected.

Also, protecting the vulnerable sounds simple, but is incredibly difficult in practice.

21% sounds much closer (eg. months) to herd immunity than previous estimates (eg. up to a decade).

Every person who is infected that we don't know about lowers the estimated hospitalization and mortality rates and reduces the ability of the virus to spread (for as long as immunity lasts in the individual).

I couldn't find any detailed write up about their selection methods for participants, only the basics of where they found them. Without that, it is very hard to determine whether or not there's some flavor of selection bias: If it is voluntary, and they do not screen out people who report having had cold/flu symptoms, then they run the risk of attracting a disproportionate number of people who volunteer because they're curious if their prior symptoms were actually coronavirus. That would make it far from a random sample. The fact that they sampled only people actually leaving their house is also a form of selection bias: these are the people more likely to be exposed and may represent a disproportionately high infection rate as well. So I await further information.

That said, even if it's overall 0.6%, that is still 6x higher than flu, and higher than H1N1 which had a CFR around 0.1 for areas with adequate health care. (much higher when there was not adequate care, but that is not dissimilar to Covid.)

So no matter what, no one should be walking away from this study saying "See it's no big deal! Just the Flu/Cold etc!"

Flu is generally well under 0.1% of those infected on average. If you’re comparing them you want to either include or exclude asymptotic people from both populations. “Symptomatic Illnesses” https://www.cdc.gov/flu/about/burden/index.html

As to NYC deaths, many people currently infected will die in the future. You can make various estimates to account for this but a reasonable first approximation is to double current deaths based on NYC’s infection curve vs South Korea’s.

As an asymptotic* person, I resent your hyperbola.

(Sorry, couldn't resist).

Flu rates are based on all known knowledge, population testing, etc. We don't have that for covid-19. If you want a good apple-to-apples comparison of lethality of another pandemic, you need to find CFR numbers that were available during the pandemic. That is what I provided in my post. Here is the source I got them from [0] which gives the CFR at the 10-week mark for H1N1, somewhat similar to where we are now. Here's the relevant quote:

>"The overall case fatality rate as of 16 July 2009 (10 weeks after the first international alert) with pandemic H1N1 influenza varied from 0.1% to 5.1% depending on the country."

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

The amount of testing for influenza is nugatory. Most of the CDC data is collected by surveys, not tests.

There are near instant tests for flue in most every urgent care center I've been to (easier and faster than getting a dr appt.)

Whenever I have gone or brought my kids when sick, they perform the test to rule out any potential need for antibiotics. When I asked, they said it's systematically reported to the the CDC as part of their flu surveillance system, whether positive or negative. That surveillance network is significantly more robust than a survey.

0.6% this is the estimated ifr. comparing it to the cfr of flu is not right, which is about ten times less than the flu ifr - or about 60 times less than this figure. your conclusion is upheld, but even moreso

According to the CDC, the estimated prevalence of symptomatic flu last year was 45 million illnesses and 61,000 deaths.

For NY, assuming it’s evenly distributed, that would translate into 13.8% of 20 million = 2.8m symptomatic flu cases and 3,753 deaths.

COVID appears to be significantly more prevalent (roughly twice as prevalent in just 3 months as flu gets all year despite incredible efforts), and roughly 2-3x as fatal.

I think you need to rethink essentially all of your assumptions/math. COVID is not more prevalent than the flu and certainly more than slightly more fatal.

20% infection rates already puts SARS-CoV-2 at higher prevalence than the flu. And we're barely a couple months into the time period of significant spread.

Why would you think that SARS-CoV-2 would not be more prevalent than the flu? The fact that we have no natural immunity, combined with how virulent it is, indicates the endpoint infection rate will be significantly higher than an average flu season.

"In the U.S., for example, in recent years about 8.3% of the total population get sick from flu each season, a CDC study found; including people who carry the flu virus but show no symptoms, that estimate ranges to up to 20%."

The endpoint percentage of fatalities is a multiple of the infection rate times the fatality rate. Both numbers are equally important.

We've been hearing the "10% hospitalization" and low-single digit CFR numbers for months. At 20% prevalence, the NYC actually had 1.7 million cases, 36,723 hospitalizations, for a hospitalization rate of ~2.1%.

According to the CDC web site, those numbers are 35.5M flu cases and 34,200 deaths (https://www.cdc.gov/flu/about/burden/2018-2019.html), giving an IFR of more like 0.1%. Which would put COVID-19 at more like 5-6x more fatal than the flu. Combine that with the virtually complete lack of natural and vaccinated resistance compared to the flu, and it's clear that the potential fatality totals for COVID-19, absent drastic action, would be much, much higher than for the seasonal flu.

It seems the CDC has different data depending on where you look. The 45 million / 61,000 numbers are here, for the 2017-2018 season, see Figure 2:

https://www.cdc.gov/flu/about/burden/index.html

And on the page specifically for 2017-2018 season, they have different numbers still;

"CDC early estimates indicate that more than 900,000 people were hospitalized and more than 80,000 people died from flu last season."

https://www.cdc.gov/flu/spotlights/press-conference-2018-19....

I agree 100% it will be significantly more prevalent, due to the nature of the virus combined with the total lack of natural or vaccinated immunity.

However, it's not clear at all what our "drastic action" is achieving, aside from 20 million unemployed and trillions of dollars spent trying to hold everything together. 20% prevalence doesn't exactly speak volumes towards the efficacy of social distancing.

That 45 million in your first link is Symptomatic Illnesses which represent ~2/3 of total cases as Approximately 33% of people with influenza are asymptomatic. https://en.wikipedia.org/wiki/Influenza So, total infections would be closer to 67.5 million.

But, it’s important to keep things in context, another year lists 9.3 million symptomatic infections and 12,000 deaths. The average year is well below peak years.

Yeah, that lines up with the CDC saying flu prevalence is generally at most 20% including asymptomatic, and usually lower than that.

So, at least in NYC, SARS-CoV-2 is as prevalent today, if not more prevalent, than a bad flu season, and it’s not done yet.

"... it's not clear at all what our "drastic action" is achieving..."

It's no coincidence that the city that shows a much higher prevalence also experienced a much more acute infection, straining their medical infrastructure. That isn't evidence that social distancing doesn't work, it's evidence that the infection was already well on its way before social distancing was implemented there. NYC was the canary in the coal mine that triggered social distancing across the US, and as a result the experience in NYC has not been repeated elsewhere. Aside from the results, which should speak for themselves, the epidemiological connection between R0 and the prevailing rate of social interaction is clear, which is why it's accounted for in the models.

I actually agree with everything you said, but I still haven’t found the basis for the tremendously costly actions taken.

As we can see, NYC did not particularly flatten their curve, and they did not see excess deaths due to lack of available care. They already hit the endpoint prevalence that you would see with the flu in a very bad year.

It’s not clear how much further SARS-CoV-2 would go naturally — the S curve doesn’t ever go to 100%. But more importantly, without a vaccine, which we won’t see for 12-18 months, we are going to find out exactly how high the S curve goes.

We have a small degree of control over exactly when that happens by burning extremely large amounts of money. So, when do you want it to happen? Now, while flu is at its nadir, or after spending $10 trillion dollars perhaps we can see the final surge during the peak of the next flu season?

The fact is that there isn’t actually a known treatment for COVID, and even with treating the symptoms (invasive ventilation) 80% of people in the ICU will die. This isn’t a disease for which “flattening the curve” makes particular sense. That would require there actually be an effective treatment.

NYC did not particularly flatten their curve, and they did not see excess deaths due to lack of available care.

I'm pretty sure all the family members of the people who died would regard their loved ones as "excess deaths". Your technocratic language ascribes no value to the lives lost, in which case of course the actions seem excessive. For people who don't value the lives of others, it would be hard to understand all the fuss.

This isn’t a disease for which “flattening the curve” makes particular sense. That would require there actually be an effective treatment.

This is a self-contradictory statement. The whole point of flattening the curve is to buy time to develop exactly those effective treatments. It is also possible to flatten the curve to an extent that there are fewer deaths under it, and based on the continual downward revision of the IHME model fatality estimates, that appears to be exactly what is happening.

So the most charitable interpretation of my comment is to convince yourself I’m a sociopath?

Excess deaths is a term of art to describe a number of deaths over a baseline. It can be used in general, or specifically, as I was using it, to describe the repercussions of an overloaded health care system. Here, for example, is a Sky News story from today using the term in its headline. [1]

I think you owe me an apology, but I won’t hold my breathe.

“Flattening the curve” is absolutely not intended to buy time to develop a cure or vaccine. The timeline over which the economic devastation of flattening the curve can be sustained is measured in weeks not 12-18 months.

The purpose of flattening the curve is to be sure there are the necessary number of hospital beds and ICU beds to provide effective treatment. If effective treatment can not be provided it leads to “excess deaths”.

Bad projections based on a 10% hospitalization rate and based on assumptions that ICU ventilators wouldn’t kill 80% of patients, have led to disastrously bad public policy which is causing untold suffering throughout the country. This suffering, which many people seem to be blind to, is notably self-imposed rather than natural, and through logical and reasonable interpretation of factual data we can stop this suffering.

This is very much unlike the suffering caused by people who are dying from COVID, for which unfortunately we have no effective cure or treatment, and for which it is not reasonable to assume we will develop one in the timeline of this current pandemic.

This is the key takeaway from “flattening the curve”. It cannot and will not decrease the total number of people who are ultimately exposed at this stage of the pandemic. Once the health system capacity is high enough to handle the number of cases coming in with effective care, continuing social distancing causes extraordinary damage without any benefit.

Since effective care at this point has proven to be both simple and extremely scalable (non-invasive ventilation, antibiotics, and frequent repositioning) the resource curves for effective care are extremely larger than projected, back when they were based on availability of ventilators.

Simply put, the data has changed, and it’s time to update the policy. It’s extremely incompassionate to do otherwise.

[1] - https://news.sky.com/story/amp/coronavirus-englands-excess-d...

However you choose to define the term "excess deaths", the fact remains that there are tens of thousands of people alive today who would be dead if we all took your advice and ignored the notion of social distancing. Does that make you a sociopath? No, it simply makes you wrong, policy-wise, if you value human life. Unless you really are a sociopath, and advocate the policy precisely because you don't value those lives. I can either give you the benefit of the doubt morally, or intellectually. Which do you prefer?

You are correct that social distancing and flattening the curve protects the health care system from overloading. That doesn't mean that it is the only purpose, or effect. If we took a poll among health care policymakers about the benefits of flattening the curve, I'd be happy to bet any amount you'd like to wager that "buying time to develop effective treatments or a vaccine" would be on their list.

This is the key takeaway from “flattening the curve”. It cannot and will not decrease the total number of people who are ultimately exposed at this stage of the pandemic.

This is just mathematically untrue. There is clearly a point where the curve is so flattened that the number of deaths under it is less than a baseline "no action" scenario. If we could somehow get everyone in the US to truly isolate for three weeks, the virus would die out entirely. So clearly there is a spectrum of actions and their corresponding outcomes. The number of infected (and dead) is not a fixed number with only the duration of the outbreak changing.

If your premise were simply that the current measures are unsustainable, I couldn't agree more. Fortunately, those measures have bought us the time to take more focused, informed action based on the latest data. In a crisis this fast-moving, every day is time to update the policy.

I argue there is inherently less sampling bias in this study than testing people at the hospital who have shown up due to severe symptoms.

Those are the people who constitute the denominator for the CFR. It's a completely different metric. You would never try to impute population exposure from such cases. This study is trying to to impute population exposure. There isn't any comparison between the two in terms of which has more/less selection bias, they are completely different things.

Didn’t stop the news media from tabulating a death rate based on hospital cases alone.

That's because all of the statistical reporting is whacked up across the globe. The only thing that can reasonably be measured now is CFR. Without extensive, reliable serological testing with high specificity, IFR can only be guessed at.

Yes, the study is really good news for NYC. My thinking is that the state of emergency, currently in place until 5/15, will be extended at most once to 6/15. After that we'll be somewhat open for business. I imagine everyone will be advised to wear mask/gloves, to keep the social distance and to keep washing hands. Lots of people will continue to work from home, others will bike to work. Being summer, the virus will not be that contagious. When the November rebound is forecast to happen, the herd immunity will be much higher, hospitals better prepared, the medical world will have learned the most effective ways to treat this disease, so the fatality rate will go down to levels seen in Germany, or lower. Bottom line, I don't think we'll have another shelter-in-place in November-December.

> Being summer, the virus will not be that contagious.

I think that is still scientifically debatable, good overview: https://www.newscientist.com/article/2239380-will-the-spread...

Certainly it is still transmissible in hot countries.

It seems reasonable that lipid encapsulated viruses are fairly stable in a variety of temperature ranges, no?

On the other hand, I can see the additional environmental UV saturation and vitamin D having a noticeable effect.

Even a small decrease in environmental persistence should help, given its infectiousness.

Thanks for the link. It's a good read.

That said, science is enamored with significance levels, p-values, etc. Most people just do that because that's what they've learned, and think that's how it should be done. They don't think it comes from Bayesian inference, where you put a very low prior probability of a drug being useful (because the very vast majority of chemicals we could put in our bodies are either harmful, or have harmful side-effects). In this case, the Bayesian prior is that common colds in general become less prevalent during the Summer, and Covid19 is caused by a coronavirus, which is related to viruses causing the common cold. The burden of proof should be lowered many, many times, but I nobody who did these statistical surveys did that, because this is a total no-no in the field.

So, I'll state my conclusion, after reading your link: the infectiousness of Covid19 is more likely than not to decrease in the Summer, based on the studies that were performed. It will not decrease to zero, so that does not contradict your statement that it is transmissible in hot countries.

> the Bayesian prior is that common colds in general become less prevalent during the Summer, and Covid19 is caused by a coronavirus, which is related to viruses causing the common cold

MERS is also a coronavirus and does just fine in the warm climate of the middle east. I hope that warmer temps slow the spread, but it's far from certain.

It's not just far from certain. It is simply unlikely. The "hypotheses" for explaining seasonal variations in transmissibility are nothing but a wild-guess/hope.

Kinshasa, the capital of DR Congo, is a city with 11 million people. About the same size as Wuhan or New York City. According to [1] there were only 25 deaths due to Covid19 in the whole country. Why so few? 21 million people live in Lagos, Nigeria, yet the whole of Nigeria has seen only 31 deaths. On the other hand, I have to admit, there were quite a bit more deaths in Sao Paolo, Brazil, but it's still an order of magnitude less than in the New York State.

Maybe Africa and Brazil are faking the numbers, but Australia most likely not. Only 76 deaths so far. Argentina has recorded only 159 deaths.

You could say this is only circumstantial evidence. My point is that we are not trying to prove things "beyond any reasonable doubt". We are trying to form the most informed opinion. And there are quite a few factors that point in the direction of reduced infectiousness during the Summer. Schools closed? Check. People away for vacations? Check. More UV light which kills airborne viruses? Check. People having more vitamin D in their bodies? Check. Air more humid, so the tissue inside your nose is better protected? Sure thing. Better immune systems due to less stress due to the ambient light? Sure thing. Etc, etc.

You can question any of these things. But in a Bayesian framework they tilt the scales little by little. But that's just me.

[1] https://news.google.com/covid19/map?hl=en-US&gl=US&ceid=US:e...

Jakarta and Istanbul have a huge rise in deaths, similar to European countries, but they have suspiciously low recorded numbers of deaths officially due to Covid. http://archive.vn/vOoRp

It looks to me like you are cherry picking data, and wrapping it up to appear objective. Looking at “case” numbers from countries with poor heath systems is especially misleading. Australia could be an outlier. Jakarta and Istanbul are better proof that temperature is not that important.

My prior is that I have seen lots of people use the “temperature” argument because they want to believe it, which trumps your prior, wink.

Edit: Note that science is mostly about finding counter-examples to a hypothesis. Jakarta is a good counter-examples to the hypothesis “Covid is not much of a problem in hot temperatures”. Istanbul was not (it is actually tepid there in March). And this brings up some other counter-examples: https://thehill.com/homenews/coronavirus-report/494428-trump...

Which infectiousness rate are you talking about? How could your prediction account for the impact of increased immunity and other interventions?

My point is that the infectiousness will decrease in the summer because of these factors. There isn't much reason to think there is anything special about it being Summer.

Finally, it's a very tenuous link between common cold prevalence and coronavirus.

It will increase, because vastly more people will have already been exposed. The seasonal variation in the cold exists for many reasons, including SCHOOL is open, and people tend to congregate in enclosed spaces more. None of these things are relevant to COVID

It's really difficult to compare the currently reported fatality rates between different countries and regions since the amount of testing varies so greatly.

Compared to many other countries, Germany has likely confirmed through testing a higher proportion of its total number of cases in the country, lowering the reported fatality rate.

In countries where testing is less widely available only those already showing severe symptoms get tested disproportionately, so confirmed cases are more likely to be fatal even if they get good hospital care.

CFR has been steadily rising in Germany. Perhaps you want to cherry pick another country?

Why does recruiting through Facebook invalidate the results? If it is a representative sample it is a representative sample regardless of how people were recruited.

One critique was that the segment of the population who would respond to the ad and actually test may be more likely to have experienced COVID-like symptoms.

Also people reported sharing the Santa Clara link with others who might want the test (due to having had symptoms). In principle you could have the same problem with this survey: call your buddy and say “come on down to Costco—they’re doing free antibody tests”.

The obvious fix is to not tell people the results of their own tests. Not sure of the ethics/consequences of that approach.

The Facebook ad essentially said "get tested for COVID-19 here", so very plausibly infected people could be more likely to respond.

> Why does recruiting through Facebook invalidate the results? If

Because “people who both use Facebook and don't automatically discount every ad or other solicitation on Facebook not from someone they personally know, especially if it invokes a major news story, because of the risk of it being a scam looking to steal personal information or do something similarly nefarious” are not representative of “people”.

If a representative portion of the population didn't click on Facebook ads then Facebook wouldn't generate revenue from these ads. Testing doesn't rely on the person being intelligent either. Because it sounds like you're just saying "results are bad because only idiots would click on a Facebook ad".

> If a representative portion of the population didn't click on Facebook ads then Facebook wouldn't generate revenue from these ads.

No, if valuable advertising demographics didn't do that, Facebook wouldn't make money. Valuable advertising demographics and representative samples of the general population are very different things.

> If a representative portion of the population didn't click on Facebook ads then Facebook wouldn't generate revenue from these ads.

That doesn't make any sense. Ads can make plenty of money even if they're only seen by women, or people between the ages of 30 and 70, or people in zip codes divisible by 3.

This study is good news... I'd be interested to project the rate of infections WITHOUT shutting down the economy for what's now going on 6 weeks. Seems like it was all in vain.

(Reposting my comment again here)

This one's from a different company (BioMedomics) and it was a random test of people pulled straight off the streets. Happened at an entirely different geographical area (Chelsea, Massachusetts) as well. I don't want to be too optimistic but there are some signs that we are heavily under-counting the actual number of cases (at least in the US).

https://www.bostonglobe.com/2020/04/17/business/nearly-third...

The rates being this high actually casts doubt on the study. It’s very very implausible, and suggests almost surely a selection bias in the sample towards a population much more likely to have contracted it.

Actually in reading closer and seeing the data is collected at big box & grocery stores, it’s almost surely very biased.

This cohort would skew younger and wealthier, which correlates with better preexisting health and fewer risk factors, and would exclude populations who systematically left the city, or who have known heightened risk factors.

Comparing the death rate overall with the infection rate of this skewed sample would be likely to greatly underestimate the actual death rate of the virus.

This weekend San Francisco plans to do a hard test of about 6000 people in a couple of square blocks of the Mission district. They are going door to door to encourage people to get tested.

Still biased but probably better than anything so far.

Yup.. though it's unclear how much we'll clear the test sensitivity hurdle by.

I wonder about that. They are trying to test a neighborhood they think is most effected. One wonders if it's possible to follow up and retest positives to exclude false one.

Suspect the Bay Area is moving towards what Korea is doing. Intense testing and contact tracing.

Friend who works at a biomedical corp says scuttlebutt is they're separating their workforce into groups based on critical need and how well they can work remotely. Workers like him not expected to return to work onsite until next year.

Retesting positives won't work because we don't know whether it's independent. If there's some rare common cold coronavirus antibodies that some of the population has, that set off the test, they'll still have them later.

Not sure how well having the Bay Area do one thing, and large chunks of the rest of the country dash to herd immunity is going to work.

I think team herd immunity is going to have a rough time of it.

The alternative is pretty crummy, unless we get very lucky with a vaccine-- it basically requires permanent restrictions.

If you can get the infection rate down enough so it doesn't overwhelm your ability to do testing and contact tracing you can relax restrictions.

A little, but not anything close to what we consider normalcy. And you get to maintain them forever, or until a vaccine arrives.

Of course, our case count is so high right now. If you get Rt down to a miraculous 0.5, it's like 180 days before you can get to the point where contact tracing is effective. And you probably don't achieve an Rt of 0.5.

Why is it implausible? The resulting fatality rate seems close to other scientific results I've seen.

Only 60k more deaths to go until herd immunity.

What is the percentage of people with innate immunity to COVID-19?

And the specificity of the test might be worse than advertised but it wouldn't be credible that it could be making a big different with the 20% positive rate here.

What I learned from an article of a major German newspaper is that in order to really estimate the quality of testing, two criteria are important: sensitivity and specifity of the applied test.

If the real infection rate is still low throughout the population, a random sampling will not lead to reliable results (you could also toss a coin).

Although the article is in German, it is worth to have a look on the graphic in the middle of the page, it should be understandable.

https://www.sueddeutsche.de/gesundheit/corona-test-antikoerp...

> Here, it's closer to a random sample, but more importantly it shows really high rates. Those rates overwhelm any error due to false positives.

I'm not seeing that, at least not from what I've seen. Whether false positives skew results significantly is highly dependent on how accurate the antibody test they used is (in addition to how large a subset of the population is positive).

This guy has an interesting visual tool that helps you to see how much a study could be affected. [1] Also, he says "Here's an interesting relationship. When a test with 95% sensitivity and 95% specificity is applied to a population with <5% prevalence of disease, MOST of the patients with positive tests are FALSE POSITIVEs." I.e., positive rate shows up as greater than 10%, when it's actually less than 5%.

Do we know the sensitivity and specificity accuracies for the antibody tests used in NY?

You'd like to think they know what they're doing. But this Bayesian stuff can be tricky, especially if they're rushing something through (esp. regarding testing of accuracy of the antibody tests themselves). And the California studies, although they seem to have some competent people behind them, seem to inflate/exaggerate the lower bound of uncertainty in their projection. [2]

[1] https://sites.google.com/view/tgmteststat/home

[2] https://statmodeling.stat.columbia.edu/2020/04/19/fatal-flaw...

While this is great information, a few facts will still confound attempts to speculate based on this number:

- The production of IgG antibodies is mostly delayed until after an infection is cleared.

- This is a random sample of people who were outside, it's not representative of the total population.

- Non-trivial numbers of people may never develop detectable antibodies for SARS-CoV-2:

https://www.medrxiv.org/content/10.1101/2020.04.17.20061440v...

https://www.medrxiv.org/content/10.1101/2020.03.30.20047365v...

If I follow, your first and third points would indicate the number may actually be higher?

Additionally, does your third point also indicate that they will never be immune, or just that we won't be able to test for it?

All three could push the true total higher, but number two easily could push it lower instead. And I'm not a microbiologist but I think lack of antibodies only means that we won't be able to test for past infection. The seronegative patients (determined by a very precise method for measuring antibodies) in the second cited paper were still able to clear their infections.

Also don’t know if people with antibodies can still spread the virus to others or whether antibody levels (not just presence) determines whether you can be reinfected.

I'm sorry, I don't know much about medicine so maybe you could clear up what those studies found. If I understand correctly, they both looked at COVID-19 patients and the first one found 83% had detectable antibodies and the second one found 163 / 173 had detectable antibodies? Or maybe I just completely don't understand it.

Also it looks like they chose people with symptoms (i.e. "COVID-19 patients"). Would we expect the amount of detectable antibodies to be less, more, or the same among asymptomatic/mildly symptomatic people, or do we not know?

I'm not a professional and some of this interpretation may be wrong:

In the first report, 83% of people who tested positive for viral RNA (an active infection) had immune T cells that seem able to neutralize the virus, possibly without antibodies. I don't see any information on whether these patients were also tested for antibodies later, but in theory some of them could have cleared the infection without developing any.

Another cohort of healthy blood donors was tested for SARS-CoV-2 antibodies, and among the samples that did not have them, 34% had T cells that also seem able to neutralize the virus. They note that these cells appeared to work in only one of the two ways that were observed with the PCR-test-positive patients' T cells. The idea is that this one method of neutralizing SARS-CoV-2 could be a carry-over from other human coronavirus infections in the past, and any antibodies generated by this method would be ignored by tests specific to SARS-CoV-2.

Also the seronegative healthy donors skewed female and younger compared to the PCR-test-positive patients, but I'm not sure if that is significant.

In the second report, around 5% of people who tested positive for viral RNA (had active infections), and then recovered, still developed no detectable antibodies. It's possible that more than 5% of exposed people are clearing the infection without developing antibodies, because this sample was taken from confirmed cases, and someone with pre-existing immunity may be less likely to develop symptoms.

Of course, keep in mind that both of these are preprints, and may contain methodological errors.

Cool, great information, thanks!

If this study is accurate, then 1.8 million NYC resident are infected. So 15 411 deaths (as of today, including 5 121 probable deaths) suggests COVID-19 has a fatality rate of MINIMUM 0.9% which is in line with previous estimates (between 0.5 and 1.6%) and this is MINIMUM 9 times more fatal than the flu (0.1%).

Edited to add "MINIMUM", as there is a lag between case detection and death (I should have known better to mention it, as I blogged about it: https://blog.zorinaq.com/case-fatality-ratio-ncov/)

Making it the worse pandemic in decades but no where near the Spanish Flu.

Hard to compare directly against the Spanish Flu as medicine has had 100 years of progress, but assuming you are talking about overall impact, agreed.

not to mention the Spanish Flu hit children just as hard as the old.

The age distribution was also significantly different then.

The Spanish flu had a smaller outbreak in the Spring and came back with a vengeance the following Fall, so hold that thought.

https://www.history.com/news/spanish-flu-second-wave-resurge...

Or as bad as the 1957-8 "Asian Flu" that most people haven't even heard of.

Well, sure, but the “Spanish” flu (only called that because Spain is the first country severely hit that didn't lie and cover it up because of WWI) was accelerated by both the physical conditions created by WWI and the fact that covering it up to avoid revealing weakness to wartime opponents hampered response in many early-hit countries and obstructed information flow.

It's also nowhere near the HIV/AIDS global epidemic in likely total impact, but it's a lot more acute in it's impact than HIV/AIDS.

> only called that because...

Also worth noting that a huge number of names in biology are quite arbitrary and/or accidents of history.

Or tech, for that matter. Fewer and fewer "hard disks" involve any actual disk, and more non-floppy floppy disks were made than actual floppy ones. Etc.

I disagree. It's simply too early to make that determination.

The Spanish Flu had a estimated infection fatality ratio of >2.5% (https://wwwnc.cdc.gov/eid/article/12/1/05-0979_article). As another commenter pointed out, the COVID-19 IFR is 0.9% in NYC. The difference between 2.5% and 0.9% is only 2.8x. However the IFR is not only underestimated due to the lag between case detection and death, but it's also probably a lot worse in many other parts of the world (third world countries, etc). So it's possible the difference is only 2-fold, which would make both pandemics relatively comparable.

Another factor to consider is that healthcare today is much better than when the Spanish flu occurred and that could explain the 2x difference.

these comparisons to the flu usually don't seem to factor in transmissibility. seems you cant leave that out of the equation and only look at death rate

It’s important to know how risky it is to get this thing. That way we can make informed decisions about opening up.

And also the risks of non-death injury from it. Long-term reduced lung function isn't good.

Cuomo made the caveat during the press conference that these samples were collected in grocery stores etc, so might be higher than people at home etc

edit: and also potentially lower than in the essential worker populations. So some caveats to keep in mind

omg yes - tons of people switched to grocery delivery esp since NYC has so many terrific options and virtually every grocery, market & specialty store already delivered, even before COVID.

It's also lower than the 3% that was predicted that people used to justify the shutdown and closer inline with the swine flu or rotovirus.

Nobody ever seriously suggested it was 3%. The estimates were always around 1%

https://www.cnbc.com/2020/03/03/who-says-coronavirus-death-r...

I mean there were articles like this all of march. I'm not saying the WHO or CDC might of said something different but lets acknowledge there were articles proclaiming a 3% death rate.

Lots of people were seriously suggesting >3%.

Edit: Removed the term revisionism since I wasn't aware of the historical connotation.

An IFR of 0.6%, with 2/3 of the US infected (about what you'd need for herd immunity at an R0 of 3), means ~1.3 million deaths. That's well in line with the models. The distinction between CFR and IFR was not news to the experts.

Regardless of initial estimates, how is 0.6% - 1% "closer inline" to 0.026%[1]? Even based upon the low range that's 30X more fatal.

[1] http://news.bbc.co.uk/2/hi/health/8406723.stm

zso your saying its closer inline with the seasonal flu. With a 0.1% death rate.

https://www.healthline.com/health-news/how-deadly-is-the-cor...

The point still stands is that enough to warrant the current strategy compared with countries like Korean or TW.

The shutdown addresses more qualitative aspects of the situation. Even if flattening the curve doesn't reduce the area under the curve, it still avoids a potential large numbers of otherwise avoidable deaths because fewer people have it at once, keeping hospitals under capacity, and more people will get it later when we know better how to treat it.

And the earliest predictions have to be the least accurate, because they're based on the least amount of data. But early on is when you need to act.

> It's also lower than the 3% that was predicted that people used to justify the shutdown and closer inline with the swine flu or rotovirus.

The shutdown wasn't, and isn't, based on the fatality rate under current conditions as much as the shape of the curve of various medical resources being required (total hospital beds, ICU beds, ventilators, PPE for healthcare workers, etc.), the anticipated speed of being able to bring more online, and the likely impact of exceeding available supply on death rates and other health outcomes, both for COVID-19 victims and patients with other health conditions. (A potential problem which was significantly realized in Italy, where things like ventilators were for a while allocated based on other factors besides medical need, since the number with need exceeded the supply.)

It's important to remember that the fatality rate of a disease isn't a constant, and changes to conditions (like, we can no longer provide ventilators to everyone who needs one) can change that rate.

You're conflating CFR with IFR.

The city itself actually estimates a total of 15,411 deaths so far the the figure is already closer to 0.9%, and there are still people dying in the city every day so will likely end up a bit higher (of course, all of this assuming the study actually represents reality).

https://www1.nyc.gov/site/doh/covid/covid-19-data.page

So .. drawing a line between two points, if NY had 20% immunity for 15k dead, that implies there will be 60k dead by the end of the process. Roughly one 9/11 every week or so?

You also have to adjust for the people who are already sick but haven't died yet. That would likely get you somewhere between 1 and 1.5%. So for herd immunity in the US at 200,000,000 recovered, that would be 2 to 3 million. That's consistent with earlier projections.

one problem with all these projections is that they all assume everyone is the same and that it is just a matter of chance. Not true in the least bit.

those most susceptible get the disease first, those most sensitive die first -

I believe the ratios will not stay the same, but continuously tick downwards

On the other part, those who are more vulnerable are more aware, and are implementing more measures in their daily living, hence they are not getting infected as much, currently.

Hope it stays that way.

Not sure that makes any difference.

Not only are death rates sharply higher in older people, but their infection rates relative to the population are also higher as well.

Without better testing, you have no way of knowing whether older people have higher infection rates. Infection rates depend on far more variables than age. Witness what's happening on the Teddy Roosevelt.

> If this study is accurate, then 1.8 million NYC resident are infected. So 10 290 deaths (as of today) suggests COVID-19 has a fatality rate of 0.6%

No, it doesn't, because the usual time to death, if it causes death, isn't zero, so you need to compare the number of deaths a couple weeks from now with the number of infections today to get a closer-to-accurate number.

Also, you are using the 10,290 confirmed deaths and i ignoring the 5,121+ additional identified probable COVID-19 deaths. So, between those two things, the actual fatality rate if the current infection estimate from this study is right is probably 2-3 times the figure you cite.

Interesting thought experiment: if NY daily deaths over the next two weeks average 400, that's 5,600 upcoming deaths from people presumably infected now. If about one-third of deaths are not accounted for (5,121 unidentified as a portion of identified+unidentified), then we'd have about 8,400 new deaths projected over two weeks including the likely unidentified deaths.

As a percentage of those who were infected as of this study, that's a 1.3% infection fatality rate.

Obviously some conjecture and estimates there, but it seems to be on the high range of early estimates (though much lower than the 3%+ rates derived from CFR with undercounted denominator).

You can derive that from better data. From https://covidtracking.com/ we can see that 57k people have been hospitalized in New York State so far, with 15k still in the hospital. Of the 42k who aren't, 16k died[1], so we'd expect that about 5k of the people currently in the hospital won't be coming home (some of these statistics are pretty depressing).

So two methods give us similar counts, matching up very closely with the separately measured CFR from way back in the original outbreak. That's pretty good back-of-the-envelope work.

[1] this isn't quite right as not every fatality was a hospitalized case.

You are correct, edited.

The time to go positive on an antibody test is not zero either, probably 7 to 14 days. Also the infection rate is lower now than it was in the past. I doubt this adds up to more than 50% higher mortality.

Also,

I don't the flu's fatality is calculated against every exposed to the flu but against everyone who gets sick with the flu. If the flu also 50% asymptomatic exposed individuals, that would know it's fatality rate down further for comparison's sake.

We calculate both. Case fatality rate (CFR) is based on patients who test positive, whereas infection fatality rate (IFR) is based on all infections. We don't yet have adequate random sampling to have confidence in IFR estimates.

Note that deaths lag positive cases by some time (2 weeks maybe?).

The study is biased in that it pulled samples from a grocery store based on people opting in. If I walk in to a grocery store and someone offers to give me this test, I am going to say no for 2 reasons:

1) I have not been sick since last fall

2) I don't want to take additional risk getting bloodwork done by someone who is testing people for covid19 antibodies. I want to take a little risk as possible and return home to my family.

So basically this test only possibly grabs people who are willing to risk going to the grocery store rather than having food delivered, and who are willing to take on additional risk of getting tested. It is even less likely that a person not taking risk, who never had any symptoms would get tested than a person who had had symptoms.

The entire premise that this represents the population as a whole is completely flawed.

I hope that the numbers stay at .6% and that the difference from previous estimates might even be improvements we've made in figuring out how to care for people suffering from Covid-19. But I remember being happy at how low South Korea's numbers were when their epidemic peaked and receded then watching the case fatality rate slowly climb up to the 2.2% it's at today. So I'm not counting any chickens just yet.

Be careful not to conflate CFR with IFR. CFR is the number of deaths divided by the number of confirmed cases. IFR is deaths divided by total infections. It's hard to determine IFR, but CFR is almost always an exaggeration of the risk.

Yes. Hiwever, it is valid to compare the CFR of one pandemic to the CFR of another pandemic if you find the CFR from the same relative point in time of that other pandemic.

Yeah, but with the success of contact tracing in South Korea I'm assuming IFR is within a factor of 2 of the CFR or so.

I feel a blanket IFR figure isn't all that helpful.

Won't there be a high level of age stratification in there. Over 60, death rate will be way higher than if you are younger and probably close to zero under 20.

It seems better if we could have a risk per age group to see who can go back to a somewhat normal life and protect the most vulnerable.

Please don't use uppercase for emphasis. If you want to emphasize a word or phrase, put asterisks around it and it will get italicized.

I find myself reconsidering the original proposition by England to isolate the most vulnerable for a couple months and let the virus work its course through the less vulnerable population.

If death rate is substantially lower among the under 50 crowd, this could work its way through with a fairly low death toll, and result in a herd immunity within a couple months. This slow burn approach we're doing however seems possibly to be the precise worst way to handle it, leaving the virus in the population for as long as possible, reducing the ability for our vulnerable population to avoid it.

Note I think the best possible solution is a complete lock down for a couple months until the virus is more or less eradicated, as Wuhan did. But I don't think it's a realistic option in our society.

The outcomes aren't just death and recovery. There's also the possibility of long-term heart, lung, and kidney damage. And the blood thinners used to prevent coronavirus blood clots can cause brain bleeding. Those happen in younger people, too.

And realistically there's no way to isolate those over 50. They generally live with, work with, or are cared for by younger people.

This feels like fear talking. As more data comes in, we are seeing more people with no affects. Not just mild, but nigh non existent.

It is agreed that something gives people severe cases. Severe includes death and recovered with damage. We don't seem to have data letting you know who or what would land there.

I disagree, see my sister comment.

Even without data on lingering deficiencies, we can speculate with confidence that it carries a cost.

Speculation is fine. But we need data to back it. Right now, we don't. Could hypothesize that many being found with damage surviving, had damage going in. Would neatly explain why it hit them so hard.

I'm not speculating about the long term effects of covid19. I'm saying all sickness carries a cost as currently understood according to our theories of cellular senescence. You've missed my point entirely.

And you are missing mine. Agreed there could be long term things. Yes, we should study that and watch for it.

If that is the extent of your point, then my response is "yes, and?"

Your framing, though, begs the question that mild cases have long term damage. We have zero evidence of that. Literally none.

You're still missing my point.

I'm saying all sickness comes with a long term cost. It gives you a shove toward the grave. Covid19 falls into that category, regardless of how much or little we know about it.

Now you could argue that sickness and aging don't work that way. I don't think you'd be right, but you can make that argument.

Your argument about the long term effects of covid19 is not relevant to the point I made.

Your point feels vacuous, then. Yes, getting sick can have long term impact. Do you have evidence that that is relevant to our current scenario? More so than it would be for any other sickness that is running rampant?

But you shouldnt do that. Just like when we believe the death rate was 3% it seems we can't be confident in anything we've been told.

Do non-severe cases cause lung damage too? Is the lung damage heal able?

That is the point of my question. There has been no evidence of damage in non severe cases. Indeed, we are having to get complicated testing to find out many people had it at all.

How could we even tell? Biopsy? Lol no thanks.

Presumably you could find some evidence. X-ray? Just listening to lungs?

There have been a number of reports of folks who were diagnosed with otherwise asymptomatic COVID when they had a CT for an unrelated reason that showed their lungs with GGO. And in some cases they have shown no signs of recovery even weeks later.

A link would be awesome. And we would still need to ascertain that they did not have this damage before getting covid. :(. CT scans have a troubled history of seeing damage that preexisted. (See back pains)

How about this article [1] (translation at reddit [2]) about german divers who had "mild cases" (no hospitalization, recovered at home) and were found to have suffered permanent lung damage, so severe that they can't dive anymore.

[1] https://www.rainews.it/tgr/tagesschau/articoli/2020/04/tag-C...

[2] https://www.reddit.com/r/Coronavirus/comments/g3rv7h/permame...

My ability to navigate Reddit is embarrassing. Never found the comment with the translation. Saw some of the discussion, though.

Sounds like a promising lead to go down. I think my other criticisms are still unaddressed. Scarring damage is tough to place in age. Is plausible that they had some pre existing damage already.

Funny to see the comments talk if walking pneumonia. I had that a decade ago. Was cake compared to whatever hit me early March.

I think the fact that they used to dive but now can't kind of places most of the damage as recent.

My link should be a direct link to the comment. The bottom of the comment is hidden by default, but half of it is visible (and you can unhide the rest using the big blue button). I hate the new reddit design.

I agree it is a strong signal. That said, I suspect there is a lot more to it.

I confess false confidence in CT scans from stories such as back pains paints me a skeptic here. :(

Absence of evidence is not evidence of absence.

But there is evidence in all of the asymptomatic people getting tested and not having any affects worth making it into a report.

Could they have hidden damage that we can't detect? Plausible. But that is true of many activities. And illnesses.

> Could they have hidden damage that we can't detect? Plausible. But that is true of many activities. And illnesses

The difference is that we know plenty about those illnesses and activities. This is a novel virus and we are just speculating.

We simply don't know the long term effects of this virus, even in recovered cases.

Until we know this virus well enough to make such statements, we should play it safe.

I for one am not willing to risk a lung because you think it's fear mongering

While this is anecdotal, it indicates that people with no symptoms can have lung damage (pneumonia), but not necessarily long-term damage:

https://www.nytimes.com/2020/04/20/opinion/coronavirus-testi...

Having pneumonia is not having no symptoms. That would be on the low end of severe in the case category. Right?

we have absolutely no clue regarding the long-term impacts of exposure to this virus for asymptomatic people or otherwise.

we cannot gamble with our future so carelessly on the basis of preliminary data. here is a vignette to explain why.

once someone is infected with HIV, most people experience flu-like symptoms for a week or two which then resolve either with or without treatment. in some people, the virus is cleared so effectively after the initial infection that they will test negative for HIV. then, over the span of months and years, their immune system is silently and entirely asymptomatically destroyed, leaving them none the wiser until they progress to AIDS and get extremely sick.

COVID hasn't given us any indication of similar activity. but we know so very little that we can't take the risk, especially not with millions and millions of people. i will also note that we are unlikely to rule out these kinds of hypothetical risks anytime soon. so caution is our only defense against disaster.

> COVID hasn't given us any indication of similar activity. but we know so very little that we can't take the risk

We take that risk with other viruses and bacteria literally all the time. If hypothetical but unlikely risks become thejustification for destroying people's livelihoods and the world ecomony, we're all dead anyway.

as a former immunologist, i can assure you that with common viruses and bacteria, we have years and years of knowledge about how they perform, thereby making the risks of exposure calculable.

it is this body of knowledge which allows our society to normally function even during a typical flu season. we understand the upper and lower boundaries of the consequences, and we can make an informed judgment about what we should do to navigate the environment accordingly.

the idea of throwing open the gates to allow everyone to get infected is a dangerous fantasy. at best, it's taking a leap of faith with other people's health without knowing whether there is a long way down or not.

I didn't realize this thread was about throwing open the gates. That said, I think there is an argument for more directed isolation measures for at risk groups. Fearing that there may be other unknowns doesn't seem to allow for any progress.

"isolation measures for at risk groups" sound so easy. First, isolate the obese. Hmm, 30% in America so 98M. Next isolate diabetics. Roughly 35M. After this, isolate those with heart disease and hypertension. 30% in the US, 98M. Old people? 32M. Naturally there's some overlapping in these groups. We'll be generous and say that 98M is the lower bound.

How on earth are you going to isolate 30% of the population?

This is making a lot of assumptions on what the at risk crowd is.

Now, I confess I am also making assumptions there. Data would be nice.

My assumptions are we could have saved a sizeable portion by tighter lockdowns on nursing homes. I also suspect we could have done things pre-infection to strengthen lungs of people with pre-existing lung conditions.

More pointedly, I think all of the havoc we have caused by closing schools is likely not amounting to a lot of saved lives. As a risk group, children are basically not at risk of severe cases.

Now, I hasten to add that no policy should be set by some random internet poster. I am hoping we are collecting data and running simulations to give us better data for the future.

If you have compromised lung functionality, there's not much you can do to strengthen it against COVID.

Children aren't at risk, and that's not why schools were closed.

Your posts don't seem to be made in good faith. Either that or you are demonstrating a lack of information on par with Trump.

Depends what the compromise is. And a lot of my view depends on if I had it. I can't get tested, as that isn't offered here. I definitely had something that made walking pneumonia seem like a cake walk. And whatever I had, it was barely a fever on my kids and wife. That said, I am glad I had my inhaler and access to steroids. Without those, I'm confident I would be much worse off today. (Indeed, my emergency inhaler was empty. Had to call in an emergency refill and was basically bed confined waiting on that.)

Closing schools was pitched at the time with a wide net, granted. Such that I could just be misremembering some of it. As a way of protecting the older workers in there, though, it feels weak. As more evidence comes out that it was wide spread by the time the schools closed, it also feels too late for that aim. (Again, this largely depends on if me and my family had it. If so, it had passed through all of us before closures happened.)

Not sure why you think I am bad faith posting. I am posting mainly from my phone, so I suspect some of my parts are bad quality. But if you think I am twisting your point in bad faith, I do not intend that.

> hasn't given us any indication of similar activity. but we know so very little that we can't take the risk

There's little reason to believe it's significantly different to any other Coronavirus. Speculating that's it's suddenly going to be aids++ is pointless scaremongering.

The main* reason it was a "big deal" was the lack of common immunity thus leading to potentially rapid spread. Deaths in the vulnerable from the side effects of this kind of thing are perfectly normal and perfectly well understood and there is very little treatment

I don't think necessarily aids++, but speculating about long term effects is not pointless, or unreasonable.

https://www.thestar.com/life/health_wellness/2010/09/02/sars...

As with any viral pneumonia

Any novel viral pneumonia.

A garden snake and a cobra are both snakes. Belonging to the same category says nothing about the potency of their bite. We have no idea what kind of snake this is yet.

We have a pretty good idea

> we cannot gamble with our future so carelessly on the basis of preliminary data. here is a vignette to explain why.

Yes we can, and will. We’re gambling with every choice we make about how to respond to this, and anyone claiming that their plan is 100% “based on science” is full of shit. For example, every country enforcing a lockdown is gambling that it won’t lead to civil war.

Yes, recovery is not binary. Even if you recover fully you've expended some percent of your finite lifetime capacity to heal your organs. It may well have taken years off your life. That's true for other illnesses to. The flu doesn't just cost you a week of productivity.

I think I'll try to get back into running again.

Wouldnt that mean quartine would never work because it make sense that those younger people going outside would make those 60 olds sick regardless of the quarantine.

The SK/Taiwan/Chinese/Singapore approach to quarantine has been to isolate people away from their families if they're thought to be sick (i.e., in a hotel or military barracks). That's how you make quarantine actually work.

That seems like a much cheaper solution.

Aside from china who were welding people regardless of there sick status and had millions of faulty tests.

Everyone else on that list has lower death numbers and a economy not going into the toilet.

But that's going to happen anyway because people will catch it until we hit herd immunity. Unless a vaccine comes out before we get there, which seems pretty iffy at this point.

I do think we can plan for the isolation of that population for a couple of months if we have some strong leadership. Unfortunately that seems to be lacking right now.

>But that's going to happen anyway because people will catch it until we hit herd immunity

It's about managing the hospitalization rate so that the medical system doesn't get overwhelmed.

I was responding to the contents of the parent comment. I agree with you (see a separate branch of the comment tree), but that's not part of what the parent comment was talking about.

All things equal, it would be better to get it out of the way sooner. However, what it doesn't account for is:

1) can we flatten the curve enough that some people never get it because we get a vaccine?

2) can we flatten it until we find an effective treatment protocol, saving lives?

3) if we did nothing would the hospitals be overwhelmed leading to additional loss of life and a higher death rate?

The first two are speculative, but the last one is pretty much certain looking at Italy and NYC. Without lockdown it could have been so much worse.

If we prolong it a lot with all the associated cost and 1 and 2 never happen, we've lost not just a large part of the economy, but also lives without anything to show for it.

I don't think you quite understand what happens to the fatality rate once hospitals become overwhelmed. That 1% IFR will quickly become 3-5% IFR.

That's a different argument. I'm saying that "lockdown until we have a vaccine or great treatment" might mean "lockdown until 2030". We already see heavy economic damages, civil unrest and riots after 4-8 weeks. Make that 40, 80 or 200 weeks and the world will be very different.

That isn't supported by any facts and is totally wrong. Our ability to treat viral pneumonia is very limited. We aren't saving 60-90% of patients that otherwise would die which your numbers imply. We can move the needle a bit and maybe save 20% of them. As an example, 90% of patients that go on ventilators still die.

Even in a situation where hospitals are getting overwhelmed it won't make much difference. Much of the care that happens is pointless. That 85 year old obese patient with chronic heart failure ain't making it no matter what you do. So if we are getting overwhelmed we can triage and not see much impact on fatality rates because patients like that are dying either way.

> As an example, 90% of patients that go on ventilators still die.

That’s in New York, and probably because they only put the really worst cases on ventilators because there weren’t enough ventilators. In Germany, that number of deaths on ventilators is at around 30%. This shows that this quota is entirely useless to make your argument, and that maybe you should curb your intuition a bit when it comes to estimating percentages of potentially saveable people, especially if those estimates are then used to effectively sentence people to certain death.

Do you have a cite for that 30% number?

Sorry, just saw your request. I'm taking that number from the Robert Koch Intitute daily status reports, which is basically the central official report regarding COVID-19 in Germany.

Here's the one from today (in English): https://www.rki.de/DE/Content/InfAZ/N/Neuartiges_Coronavirus...

The interesting table is on page 6, where there's the percentage of ventilated patients on ICU, which is 73%, so we know most ICU patients are ventilated, which means we can treat someone being on ICU as roughly equal to someone being ventilated. Right below is the number of discharges from ICU, and the percentage of deaths, which is 30% of all discharges. Since almost all ICU patients end up being ventilated, we can conclude that these about 30% of deaths mostly happened on ventilation, and most of the 70% recoveries also happened on ventilation.

I know that my calculation doesn't exactly result in the real percentage of deaths on ventilation, but the error range of my estimation does not allow the actual percentage of deaths on ventilation to even come close to 90%.

>so we know most ICU patients are ventilated

This is where your logic fails. Most patients in the ICU at this point in time are ventilated. That doesn't (necessarily) mean that most patients that went to the ICU were ventilated. Those that are ventilated will stay in the ICU for weeks. Those that aren't might only be there for a few days.

That uncertainty is why I already considered my calculation to be a rough estimate with a relatively large error window. But it is not large enough to allow the true percentage to even come close to 90%. For that, huge numbers of patients would have to be in ICU for a single day only, which is a ridiculous assumption.

Yeah, I have less and less hope in a vaccine. We'll likely be well on the way to herd immunity before one is ready.

For #3, I agree. I'd be interested to see the math on how many under-50's need hospitalization, ventilation, and how severe of an additional effect would that have. It may be that it requires less medical capacity to support a high percentage of the healthy population contracting the virus, than a far lower percentage of the general population (including over-50 crowd) contracting it. Or it may require vastly more capacity. I have no idea.

The other thing to take into account is the effect dragging this out has on developing economies. It seems like this is shutting down international supply chains and food shortages could result. So that's what's got me feeling like more aggressive action should be considered.

Option 2 can be the most realistic, depending on the outcome of the currently-going clinical trials. There quite a bunch closing by June (although most of these are on repurposed drugs, and with not too large sample sizes).

Sweden didn't lockdown and might have herd immunity in weeks, according to their chief epidemiologist. It'll be interesting to see how this plays out in the long run.

“In major parts of Sweden, around Stockholm, we have reached a plateau (in new cases) and we’re already seeing the effect of herd immunity and in a few weeks’ time we’ll see even more of the effects of that. And in the rest of the country, the situation is stable,” Dr. Anders Tegnell, chief epidemiologist at Sweden’s Public Health Agency

Tegnell said sampling and modeling data indicated that 20% of Stockholm’s population is already immune to the virus, and that “in a few weeks’ time we might reach herd immunity and we believe that is why we’re seeing a slow decline in cases, in spite of sampling (testing for the coronavirus) more and more.”

https://www.cnbc.com/2020/04/22/no-lockdown-in-sweden-but-st...

It might be herd immunity. If might also be that this disease is seasonal. Either way it is an interesting metric.

I would not really call the Swedish model successful - Czech Republic and Sweden have both about 10 million inhabitants.

Things moved pretty quickly to pretty comprehensive lockdown here in Czech Republic, including mandatory mask wearing. So far this seem to be effective, with combined death toll currently at 210. In comparison Sweden has almost 10x as much, 2010 dead.

Looks like if more has been done less people could have died, not to mention the alarming news about organ damage, virus resurgence and questionable long term immunity.

as others have said, the area under the curve remains the same. The idea with Sweden is, after one year, the deaths will be about the same as everywhere else.

If the hospitals get overcrowded, there will also be more people dying from other causes and suffering from long term consequences, because a percentage of them won't be able to receive adequate care.

I haven't seen any actual data on how big that impact might be, however.

Even without overwhelmed hospitals there is a lot of excess mortality not directly explained by published COVID-19 death numbers. Including in Sweden.

Q is how many are dead when a region has 80% immunity (through vaccination or infection). Those dead should include also deaths due to lack of planned care, cuts in healthcare due to recession, suicides due to unemployment and so on.

Those countries that lock down and stop a first wave with only a few percent infected are basically betting on the arrival of effective treatment or effective containment.

Those that don’t are betting that containment is impossible and effective treatments and vaccines are a year out at least.

It’s impossible to say which scenario is right but I know I’d place my bet the same as the Swedish state epidemiologist.

I don't get it. We have numerous reports of the antibody tests being inaccurate. We have numerous reports suggesting you can get the disease twice => once does not equal immunity. So how exactly are these Swedish scientists testing for immunity?

Sweden hasn't done a government mandated lockdown, but has a lot of social distancing occurring. Also, I wouldn't use Sweden as an example of herd immunity since antibody tests are only coming on line, and many are unreliable. Besides look at Sweden's CFR. Not something I'd like to see in a modern country.

I've been making many posts arguing against the efficacy of lockdowns (and been getting shat on by points I find mostly naive). I think something like this is probably the best approach because it minimizes economic harm while also trying to minimize hospitalization rates among the elderly.

But even if we pretend we could implement a perfect bubble for everyone over 50, they're not especially safe when the lockdown is lifted for them. The virus will still exist, and people over 50 tend to spend time with people over 50 (see: homophily). Once it enters the social network, herd immunity doesn't exist because your local community of old people are both super vulnerable to acquiring the disease and few of them have immunity from past exposure because of the lockdown.

Locking down the country until the virus is eradicated is not a realistic option and its incorrect to think Wuhan has "done" this

Any time I try and make counterpoints to lockdowns or even logical evaluations of them online the downvotes and hate start flowing. There is a huge bias among those in our circles towards locking down as long as possible, because all of us can work from home and aren’t worried about feeding our families.

Arguing it's a matter of bias sidesteps the reasoning for lockdowns. This is like when someone dismisses someone for possessing a political bias---that's not an argument.

And framing it as lockdown vs. being able to feed your family is a false dichotomy. It seems like a waste to even get into the finer details if this is where you're coming from, but keep calling it hate while ignoring the criticism.

No, it's a very real dichotomy. People won't literally starve in the US, but waiting in a bread line for hours is very different than feeding your family through your own money and effort.

No, this is a very fake dichotomy.

The array of options available to society is not "starve" vs. "waiting in a bread line for hours" vs. "feeding your family" as can be seen by countries that have approached this problem differently.

And lemon zest isn't spicy.

Relatively few people will starve "to death". But a significant number of people are already not getting enough food before the crisis: https://www.rt.com/usa/314881-usa-households-not-enough-food...

If food prices increase, and people lose jobs, more people will suffer food insecurity. This problem is MUCH worse in poorer countries for obvious reasons.

> I've been making many posts arguing against the efficacy of lockdowns (and been getting shat on by points I find mostly naive).

I skimmed some of your comment history. Your comments aren't as rock solid as you believe, and there are many points either glossing over things or complete ignorance.

> More people will die if the lockdown is lifted, and many of those who would have died anyway will die sooner.

Are you really arguing many would have died anyways? How many? What percent? You're ignoring the reality that in regions with overwhelmed healthcare systems they're having to engage in triage (read: many people are dying who would not have died anyways) since they cannot treat everyone. This isn't just for covid-19, but for people with other treatable conditions.

> Your comments aren't as rock solid as you believe, and there are many points either glossing over things or complete ignorance.

thanks man. I think you're demonstrating complete ignorance too. For instance, that quote of mine which you posted out of context clearly says the opposite of what you think it says. MORE people will die if the lockdown is lifted, and that those who would have died anyway (if it were not lifted) will die SOONER. Both obvious and uncontroversial.

That aside, triaging is very sad, but has a relatively small effect on total deaths, because the people you triage are the most likely to die, given care. This is very very different from random people dying who would not have died anyways. Hospitals that need to deny patients, which as far as I'm aware is just a single region in Italy with one of the oldest most vulnerable populations in the world, are doing so not because they can't actually take more people in for the most basic low level of care, but because there's basically nothing they can do.

> But even if we pretend we could implement a perfect bubble for everyone over 50, they're not especially safe when the lockdown is lifted for them. The virus will still exist, and people over 50 tend to spend time with people over 50 (see: homophily).

The point of the lock down is to prevent the spread of the virus from happening too fast and overwhelming the healthcare system. It is not to prevent people from ever getting it as that's clearly not possible. You seem to be missing that point in your argument.

That's what people say. That's not how it works.

If the lockdown is ineffective, there's not point in doing it so whatever.

If the lockdown is effective, you kill the exponential spread. This is good, but if there's no exponential spread, you haven't actually infected many people at all. When you lift the lockdown, you're close to the same level of vulnerability as when you began. For a virus like this, you likely cannot 'eradicate it' by keeping the lockdown ongoing.

There's no middle ground where you slowly build up immunity across the population at a level hospitals can support. We can't turn the economy off and on again until the virus has had burst spread either.

The lockdown makes a lot of sense for 3 reasons: to learn more about the disease, to gather more PPE for doctors, and to reformat critical systems to run with a healthier population. It's not a bad idea. But it's utility is going to run lower while the cost gets higher. We need to address that.

Good news. Herd-immunity is growing.

Also, this means that the death-rate is far lower than reported. And this is nice too. Maybe we can save people by starting the society again.

In other news, we see that hydroxychloroquine is actually working according to doctors. But there was a very bad study that claimed that it caused 2x as many deaths. It should have said instead that it was mainly used on patients that were close to death. Here is a good analysis of that study. https://www.youtube.com/watch?v=dLSYRqcg0wo&feature=youtu.be... Bad studies like this wide published one show that science is not immune to political agendas.

This video states that a randomized controlled study needs to be conducted, otherwise nobody can claim that hydroxychloroquine works or does not work.

The presence of antibodies is not sufficient for herd immunity. For herd immunity you need those antibodies to stick around.

Is this antibody test specific for covid-19 or would it be positive on other coronaviruses? That's a real concern since the other 4 coronaviruses are endemic.

What's terrifying is with all these professional analysts in here, yours is the first comment I've seen that asks this obvious question.

To assume the infection fatality ratio(IFR)is 0.5% from this is retarded. We know that when covid-19 began to spread in South Korea at first it spread among a younger population who were ignoring or at least not following government advise so closely and taking chances the IFR was looking like about 0.7% to start with (In SK). We know this as SK kept excellent data / records to begin with. We also know that the Diamond Princess with a more generally aged population has a IFR of 13% +. We know this is a disease that adversely effects the elderly and infirm (in the developed world) If you are over 80 getting covid you might as well play Russian roulette with a Magnum .45 .These groups of people know there at risk, so if they have any sanity they are staying the hell at home. So people who are going into the Grocery store in NY city aren't representative of this population. This is why we are staying at home mostly to protect the old and infirm. That's not to say you want Covid-19 yourself either because there's about a 5% chance you will end up in the ICU (and an as yet undisclosed chance of death) even if your a member of the Hungarian Olympic Shot-Putting team. This does kill young tough people, it does happen but this is mostly killing the elderly and infirm. Why are people so fecking retarded? I can't take this anymore!! Stay the Sm3g at home! It's stopping the hospitals being overloaded.. It's buying us time to figure out new treatments / how to treat this better.

Read an article that over half of Europe's Covid deaths might be from nursing homes...

Sounds plausible. Do you have a link to that article? I'm not trying to bate you, I'd genuinely like to read it.

Well, it looks like in the end that much-criticized imperial model might be right on transmission rates but way off on the fatality rate and with that predicted deaths.

It pretty much looks like none of the epidemiological models are reasonably accurate on both, case numbers and fatally rates

If you're fitting a model to observations, they'll have something of an inverse (but of course non-linear) relationship. So if your virulence estimates are worse than your transmissivity estimates, or vice versa, the bad measurement is going to push the model out of whack.

Which model and what fatality rates did they predict?

How are they off on fatality rates? Also note that the fatality rate is an input to the model, not a prediction of the model.

If the results from this NY study hold, the numbers are basically exactly in line with the Imperial College study. That study assumed an IFR of 0.9%, and the NY results imply an IFR right around that.

> The reason the tests were randomly taken from those at grocery and big-box stores is that these were people not isolating in their homes and presumably not at work and therefore not essential workers.

I don't know what to make of that. I would expect people at work to have higher infection rate (not the least because many of these jobs involve contact with the population). It also filters out older and sick people. So intuitively I would expect to undershoot the actual rate.

I read it as people shopping, not people that work there. So folks who are out-and-about, but are not in the more likely to have it groups (healthcare workers or people working at the grocery store). You're right about older and sick people.

No that's what I meant. People shopping and therefore not at work. Therefore intuitively I would expect the population tested to be less contaminated than the population not tested (because at work).

People who work also shop. People who work in stores also shop in stores. At that time, they could be randomly tested like non-workers.

So workers are not excluded from study.

I don't understand - essential workers still going to work also have to eat, and so go to grocery shops like other people.

The sampling location isn't meant to exclude essential workers, it's meant to avoid only sampling essential workers.

IE: if you measured at workplaces that would capture only essential workers, if you measure at people's homes during work hours, you get people who mostly are quarantining, if you measure at a grocery, you might get a mix of both, etc.

> isn't meant to exclude essential workers

But is specifically says

> and therefore not essential workers

It doesn’t say ‘not only’ - it says ‘not’.

> presumably not at work and therefore not essential workers.

"presumably" is the key word. They aren't saying "definitely".

I’m an essential worker going to the grocery at 10am because there’s less people there. My hours are flexible at work and we’re set up to do staggered shifts to minimize number of people in the building.

At what point have we scientifically reached "herd immunity"? Is it 50%, 90%, 99%?

https://en.wikipedia.org/wiki/Herd_immunity#Mechanics

Although it should be noted that incomplete herd immunity will still help, by reducing the number of other containment measures which must be taken.

More like 50-80%, you just need spread to be impeded so that r falls below 1, but for that many people to get it would mean very high deaths, in the US 1% of 80% of 320m is > 2m deaths.

Keep in mind that if 21% of NYC residence have been infected this probably implies R0 in that population was higher than in most prior estimates of R0, which increases the threshold of immune people before R0 falls below 1.

It also implies that the R0 of the already-infected population is higher than that of the not-yet-affected population.

R0 varies hugely. The R0 for highly connected people meeting others regularly, will be far higher than the R0 for people who only leave the house once a week. The R0 in a crowded prison will be far higher than the R0 of rural farm owners.

60-85%

1-1/R0

Most commonly the R0 of is assumed to be 2.5-3.0, but estimates range up to 6.5.

https://academic.oup.com/jtm/article/27/2/taaa021/5735319

Probably in the 80-90% range depending on the reproduction number. It would be 1-1/R0 from my understanding.

The percentage scales with the R0 of the virus. The more infectious the disease, the higher the percentage of infected people for herd immunity to take shape and effectively push the Rt/R0 < 1.

i think people misunderstand what herd immunity is. If NYC has 80% infection rate, then people within NYC can assume immunity. People outside NYC having 5% are still vulnerable

An important thing to keep in mind is that even without herd immunity, the transmission rate gets lower every time a person obtain long-lasting immunity (assuming such a thing typically occurs with this virus). Keeping the transmission rate down around 1 becomes increasingly easier and requires less severe protocols.

So I await further information.

So no matter what, no one should be walking away from this study saying "See it's no big deal! Just the Flu/Cold etc!"

Do you have a source for the CFR of 0.1 for H1N1? I could only find this[1], which is a much smaller number.

[1] http://news.bbc.co.uk/2/hi/health/8406723.stm

You may be looking at "settled" numbers when all was said & done and all facts were known. That's not an "apples to apples" comparison with current covid-19 knowledge.

Here's my source [0] and the relevant quote, below. It is the CFR during the H1N1 pandemic, 10 weeks into it, which is why it is a much more appropriate (though not perfect) "apples to apples" comparison with Covid-19:

>"The overall case fatality rate as of 16 July 2009 (10 weeks after the first international alert) with pandemic H1N1 influenza varied from 0.1% to 5.1% depending on the country."

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

They're trying to measure infection rates by analysing people who are more likely to go outside, and more likely to be infected. There's no adjustment for this confounder at all that I can see. Quick example:

Suppose there's a 10% chance of getting infected every time someone goes outside. Suppose there's a million people of which half are type-A people who go outside once this month, and half are type-B people who go outside 10 times a month.

The type-A person will have a 10% infection rate. The type-B person will have a 65% chance to be infected after a month. The average infection rate is around 42%. Yet if you randomly select someone outside, odds are 10 to 1 that it'll be a type-B person, and you'll get a far higher average of 60%, which isn't indicative of the average person, only of the average person who goes outside.

Second, the article then compares the infection rate to the mortality rate. But the mortality rate does not include excess deaths. These are quite a bit higher for NYC, about 30% or so it seems than the official figures. But even this may be undercounting (or potentially, overcounting) the impact of the corona disease itself. Other deaths might be much lower (e.g. traffic, murder) or higher (e.g. untreated cancer).

Then there's the lag-effect. There's 150 thousand confirmed infected, 15 thousand dead. But the vast majority of confirmed cases are still active, not recovered. They're confirmed because tens of thousands were hospitalised. Of the incubated people, 80% do not come out alive. There's lots of people fighting for their lives right now. Once those cases resolve, many will not have made it. And that's just hospitalised people. Then there's lots of cases who just got infected, and will be developing symptoms in 1-2 weeks.

The way exponentials work is that lag is way more important. In the first weeks NY doubled every 2 days. That means if you have 10 thousand today infected today, in 10 days that's 320 thousand. That's when people start to get symptoms, and 10 days later people start dying from the symptoms. That means your infection count can go into the hundreds of thousands or even millions before seeing any death. But you can't conclude mortality figures from that, at all.

In short, the 21% and the 0.5% conclusions are extremely premature and should not be reported like this. It's good to report the measurements, in context, but you can't draw the conclusions about the population or disease from it.

I’ve continuously questioned the antibody tests...

My understanding (from NBC news) was that at least some of the antibody tests were derived from a couple who were on the princes cruise. They never showed symptoms, but tested positive. IMO it’s quite possible that they never had it and those tests were inaccurate (right at the start of the outbreak, no symptoms, etc).

Further, these antibody tests likely weren’t fully vetted nor were the samples they derived it from. For instance how often does another coronavirus set off this antibody test?

Personally, I’m just waiting to see. I actually do suspect 20% have been infected, so it fits my expectations, but that’s not validation.

0.2% of people in NYC has died from Covid-19, so this is exactly what we would expect.

0.5% IFR is still very high, I wonder what the antibody detection threshold is - i.e. is it possible that a lot more people have been exposed but don't have detectable antibody levels ? Would it qualify as infection in the sense that they are not susceptible for covid19 in the near term ?

It sure would be nice to find out the virus is far more widespread / exposure is higher than expected if only that would seem to indicate that the light at the end of the tunnel is nearer than we think ... and a lot of people have been exposed and doing well.

For the people that thinks death rate is low is good news, it’s not when you know how contagious it is, you then need to look at numbers, not percentage to get a practical look at severity.

Another datapoint suggesting the death rate of this virus is within 0.5%.

Also another data point suggesting that the virus is much more contagious than the early estimates said.

I personally don't see how the test, track and trace efforts that have been touted as the only way to open things up can work with a virus with these characteristics. You would need to test a huge percentage of the population each day with quick results tests and something like that is many months away from being possible assuming it ever would be possible.

I agree. But it seems to have worked in Korea. The math doesn't seem to reconcile somehow. Actually probably the prevalence of masks explains the difference.

The math seems to point more to there being another factor to what makes it severe, in my eye.

Going by the news we have seen, elementary schools could have 80%+ infection rates and fewer deaths that NY has seen. Similarly, are there other factors besides age? (Age feels way more like a proxy.)

More contagious OR began community spread and became endemic much earlier.

r0 is part of the formula for total infected at X/Y/Z date but so is date of initial infection and size of initial infected population.

The key is testing, contract tracing, and quarantining when the number of cases is small. That's what South Korea, New Zealand, and a few other countries did and they've all suffered relatively few fatalities. You still need a massive amount of tests to contain an outbreak, but the upside is that if you contain it early you don't need toe enact clumsy and broad measures like lockdowns.

I wonder if we did these preliminary tests with the flu if we would see a large death rate drop for influenza as well

The US's influenza mortality figures are a combined influenza and pneumonia figure because they usually don't even bother actually screening for influenza.

Research attempting to determine how much of that figure is infected with influenza have found figures like 1/3rd (though with large differences year to year).

I had the opposite conclusion. There are between 10k-15k COVID-19 associated deaths in NYC, depending on how you count (the higher rate is consistent with the excess-mortality data, I believe). That by itself produces a range of ~0.56%-0.86% if you assume 21.2 % of NYC has been infected (larger range if you allow for some error on the infection rate, obviously). Moreover, we don't know what fraction of the currently-infected who will die have already died. A reasonable guess might be 50%, which would mean that the fatality rate for NYC is somewhere between 1.1 and 1.7%.

Either way, based on NYC, 0.5% might be a hard lower bound on the all-population fatality rate (of course, maybe NYC is not a representative sample for some reason)

“ Moreover, we don't know what fraction of the currently-infected who will die have already died. A reasonable guess might be 50%” I lost you at reasonable guess. Why not ~1%?

If the mean time between infection and death is 2 weeks (Mostly true), and the virus has doubled every two weeks (True in NYC), then assuming that not a single new infection takes place starting today (Obviously not true, but this is a hypothetical), then in two weeks, you would expect to have twice the deaths that you do right now.

1% would imply an unrealistically high-fatality rate, so that can't be right.

The virus takes time (2-3 weeks) to kill people, and the prevalence was presumably measured within the last week.

Therefore, we have to take out the cases that occurred in the last week or two. I would suspect that 50% is on the upper end of how much the death lag could skew the results, but 20-50% doesn't seem unreasonable.

0.5% comes from Cuomo’s presentation [1].

Also your 50% seems very high to me. My understanding is that 50% is about the death rate of covid patients requiring ventilation, so the overall death rate of people hospitalised for covid must be well below that.

Also if you factor this timing impact, you have the same timing impact on the denominator, i.e. people who are infected but haven't developped antibodies yet.

[1] https://youtu.be/TisDYYWJgBA?t=967

[edit] in fact for the 50%, we have an idea from Cuomo's presentation charts. The gross daily hostpitalisation rate as of 10-15 days ago was about 2000-2500 per day, and the number of death now is about 500 per day, so that suggests roughly a 20-25% death rate for hospitalised people (I am sure there must be some more precise figures somewhere).

50% is not the death rate, but the fraction of those who are an infected and have died to those who are infected and who will have died (since there is some lag between infection and death).

The death rate of those requiring ventilation in NYC is closer to 88% https://jamanetwork.com/journals/jama/fullarticle/2765184

Outside the context of this virus, for adult men, the survival rate one year after mechanical ventilation might be 30 percent: https://www.ncbi.nlm.nih.gov/pubmed/8404197/

no, the death rate for ventilated covid patients is something like 80-90%. this is based on a NYC study published yesterday.

It’s not clear what kind of antibodies they’re testing for, but it can take up to 4 weeks after infection to develop detectable levels of antibodies. It takes on average ~3 weeks after infection to die. There needs to be some time lag adjustment in your calculation, which would likely result in a lower IFR.

IMO the two effects probably about cancel each other out and we’re looking at <1% IFR for the population tested (AFAIK children weren’t included).

I guess I would assume they would have corrected for any time-lag in the result, but I have not read the study in detail

Right now it's just a press release so there's not much to read. Cuomo's statement reads to me that is just demographically adjusted though.

Mortality rate, case fatality rate and infection fatality rates are all different things and have different values.

Infection fatality rate of the virus could be within 0.5% (because its the one that tries to take into account everyone who has been infected), mortality rate is very likely still the one WHO has published and the third, case fatality rate is ~38% in the US.

What? 38 out of 100 infected in US die?

Infection fatality rate, the big number everyone is trying to figure out, best guess is somewhere around 1% right now, with a confidence interval of something like 0.3%-2.5%

Case fatality rate, calculated as "number of deaths" / "number of known cases", usually something like 10% in most countries, although occasionally much lower or higher for the odd country that has really good or really bad testing.

Resolved case fatality rate, calculated as "number of deaths" / "number of recoveries + number of deaths", which has been around 40% in the countries that have been hit hardest for the duration of their outbreaks. Tends to go down after the curve is bent, because it takes people longer to recover than die in most of these cases.

15,000 deaths (So far) in NYC. 8.4 million people. If 20% of the population has had it, the death rate is 1%... Assuming that none of the currently infected die. Which is an unreasonable assumption (As the mean time to death from the point of infection seems to be ~2 weeks, and infections have doubled over the past 2 weeks.)

But those currently infected are not included in the 20% of the population that has had it. Or maybe some are, but most of those infected now who may still die, do not have antibodies yet.

Yes, but the age is a 10x factor, os CFR would be much different in Nigeria (average age is about 19) and Italy (average age 45). We have two good petri dishes:

USS Theodore Roosevelt - 600 cases out of 4800. 5 sick, one dead (41 years old). which means hospitalisation is at 0.83% and CFR is 0.016% (1% dead or in hospital). Not idea what is the average age, probably around 30-35.

Diamond Princess: 700 of 3711. CFR 1.1%, average age around 50.

What worries me the most though is potential mutations and death rates when you are infected with Covid-19 and flu at the same time (or other pathogens).

https://www.reuters.com/article/us-health-coronavirus-usa-mi...

https://www.nature.com/articles/d41586-020-00885-w?utm_sourc...

Average age on an aircraft carrier appears to be 19 [0], 20 [1], and/or 24 [2] years old.

0: https://www.nytimes.com/2008/04/20/arts/television/20jens.ht...

1: https://archive.defense.gov/news/newsarticle.aspx?id=15334

2: https://mashable.com/2016/06/23/what-its-really-like-to-live...

> which means hospitalisation is at 0.83% and CFR is 0.016% (1% dead or in hospital).

0.16%, not 0.016%.

You can't measure CFR or IFR this quickly in an isolated environment like the Roosevelt due to mortality lag.

Also, the CFR for Diamond Princess is now up to 1.83%.

The death rate would go up if more people had the virus at once, which is the entire reasoning behind isolating.

I am wondering that number, if the limiting factor is ventilators, but the CFR of people on a ventilator is close to 90% either way.

Other cases may just need oxygen supplementation, which is more manageable.

It is pretty clear now that Germany is not that far from from Italy in terms of CFR when controlled for age.

…when people are able to get treatment.

So, at what point can we declare the lockdown to be a harmful overreaction and start opening countries up? Will we hold anyone accountable for models which proved to be pessimistic by orders of magnitude, causing implosion of economies around the world?

has anyone modeled at one point deaths from economic collapse overtake deaths from Covid19?

That was the first thing I said, 2 months ago, and got told I was a selfish asshole and had no clue what I'm talking about. This is going to be a real big history lesson for our grandchildren on how not to handle a situation in one way or another, unless the media rewrites the narrative.

Oh, wait.

I would be very sceptical about any models produced in the US, since the issue is so partisan, and one can tweak variables to prove whatever outcome one wants from a model.

I think the lost life-years of economic hardship could easily outweigh the lost life-years of Covid deaths. That is because economic hardship affects younger people, while Covid deaths primarily affect people with not many years left to live. But there are many other external costs (social costs if your grandma dies) and economic benefits to deaths (lower pension payments, home availability?).

However, given current information, a lockdown for weeks to months seems to make sense, since the consensus is that economic collapse is not yet immanent, and that steps can be taken to help keep the economy spinning when lockdown restrictions start to be relaxed.

Economic hardship affects middle aged and old people too.

I think,if anything, that this new data validates the lockdown. New York City has been hit hard by this virus (0.2% of the population; for comparison 0.3% of the US population died in WW2) and there are still approximately 80% of the population that hasn't gotten it yet. If 70% of the population of NYC ends up getting it over the next few months, that would mean something like 50k to 60k dead just in NYC. If 70% of the US population ends up getting it that would be 1-2 million dead. Locking down in the face of those numbers is not an overreaction, although maintaining lockdown until a vaccine may be an overreaction. I think we should be devoting huge amounts of resources to manufacturing face masks and testing kits so that we can safely ease out of lockdown without causing more outbreaks like we saw in New York or Italy. Ending the lockdown without a plan, though, is highly unethical in my opinion

Sure, let's ignore the hospitals getting overloaded.

you're getting the wrong message from all of this.

This data might suggest that the lockdowns suppressed the infection rate down to 20% at this moment in time.

Without a lockdown, a month more of infections would have likely bumped NYC to achieving herd immunity, or 60-ish percent, since 20% to 60% does not take long with exponential growth.

This means two things. One, it would mean that we would have almost certainly overwhelmed the hospitals with triple the rate of infections. We've be leaving people dead in the streets and it would be a humanitarian disaster as every hospital was at double capacity.

It would also have meant that the curve would be flattened for good.

>It would also have meant that the curve would be flattened for good.

I have yet to see any evidence that covid antibodies provide long-term immunity. We can't say with any certainty that the curve would be permanently flattened. It may just be flattened for a few weeks, a few months, or a few years.

With no long-term immunity, lockdowns need to be permanent until we have a vaccine or treatment. That's not feasible. If there's no immunity we might as well end the lockdowns.

0.18% of the entire NYC population is already dead from the coronavirus and by the time this is over the number will probably reach or exceed 0.3%.

If you want the whole country to get herd immunity you will probably see similar mortality rates across the country. 0.3% of the US population is 1 million people. How have the models been proven to be "pessimistic by orders of magnitude?

Just so you know, around 3 million people die in the US every year, many of the million people you expect to die would have ended up being counted in that 3 million background statistic.

More importantly, herd immunity is the ONLY solution on the table right now, the lockdown is not about stopping the virus in its tracks. It is about spreading it out over a period of time.

>the lockdown is not about stopping the virus in its tracks. It is about spreading it out over a period of time.

It's unclear what the lockdown is about at all since the government messaging has been incoherent. Many countries have (so far) succesfully contained the virus without herd immunity through contact tracing, and the US government has been making vague comments about pursuing something along those lines despite not doing anything to increase testing volumes to the levels necessary for contact tracing.

Depends where you are. The Bay Area is contact tracing every case and as a result is at a really low new case count (Santa Clara County is at 11 per million/day over the last week, significantly lower than successful country's peaks (NZ/SK), while still having tighter restrictions.

At the point where more deaths are caused by the global socio-economic collapse than COVID-19 would have caused at even pessimistic projections.

At this point, I'm not convinced in either direction.

You're assuming the economy wouldn't have collapsed in the faced of the pandemic.

That the 0.5% death rate in NYC is not higher is _because_ of the pause. We saw the death rates spike at the peak of the curve. We saw the hospitals near total collapse. We even have an example of what would happen if we didn't flatten the curve (Northern Italy). We have another counterexample of California that shows NY's pause was late.

I guess Sweden is the counter-counter example. I think the US would have needed to have built out a far different medical system to achieve what they have.

For a few days at the end of March and beginning of April, the hospitals came so, so close to collapsing. Just a few more days up the curve and we would have run out of beds. (In reality, we did run out of beds, as you can see stories of many turned away from hospitals in those dark days, but it wasn't quite a systematic collapse).

All of this happened, we were here to record it. Models are models, and we're in the early days of a deadly, virulent, confusing pandemic. But there is no doubt that our reactions and solidarity saved lives. The imperfect models helped us get there.

Now, there is a more reasonable question of where do we go from here. For urban metropolis like NYC, I think a slow re-open like Germany. We slightly open the spigit so that 20% becomes 40% becomes 60% (and hopefully immunity is immunity) and we achieve herd protection (at great cost).

And likely the less dense, the slower we'll need to go (depending on hospital saturation).

I certainly will cheer the scientists who took the best information they had at the time and worked with the leaders to make the best decisions they could. Yes, I will indeed hold them "accountable".

I'm holding judgement on Sweden since their numbers don't look as good as people are claiming, and the pandemic is still young.

But they can infect others

At 21% it would be time to end the shutdown.

EDIT: Read the comments below.

That doesn't make sense to me. I would think you'd want to get to the point where you've reached the carrying capacity, so that "overshoot" of the equilibrium doesn't result.

See my other reply below. You can't overshoot 100%, and long before we get there herd immunity takes hold, and there's no question of the disease being kept from working its way through -- only a question of managing the rate at which it does. All in all the economic damage of continued shutdown does not make sense at this point (at 21% of the population exposed). We should stop now.

Your comment doesn't address my point. See this Twitter thread, https://twitter.com/CT_Bergstrom/status/1251999295231819778

specifically this comment:

https://twitter.com/CT_Bergstrom/status/1252008040376614912

for a good illustration of what I'm talking about. Keeping the rate of transmission low through the peak can definitely save lives.

I'd think it'd be decided based upon the new cases and hospitalization and death rates, not an estimate of population with exposure.

Sorry, but that's dumb. If 1/5th of the population has been exposed, then you know the total strain on the healthcare system is extremely unlikely to top an additional 4x what it has been, but not only it has 1/5th the population being exposed not in fact been enough to overwhelm the healthcare system, as more of the population is exposed you begin to get herd immunity, so in fact the total strain on the healthcare system is extremely unlikely to reach a total of 5x what it has been, very likely not even half that.

Give all of that, it makes no sense to continue the shutdown considering how costly the shutdown is.

To those who think this is a question of saving lives, that's not so. At this point the disease will work its way through the population because there's no time to finish testing, producing, and distributing a vaccine, which means the only question is of managing the maximum load on the healthcare system. Moreover, many of the most vulnerable have been hit already, and those who haven't can be protected by measures far short of a shutdown.

No, at 21% it is most definitely time to stop the shutdown. (Note: not all the measures, just the shutdown.)

We don't know that actually 21% have been exposed - hence the use of "preliminary" and "suggest". We need actual positive results that aren't based upon hiding behind the repeated use of "extremely unlikely". It seems like a bad idea to gamble based upon an initial suggestion.

The only actual good indicator we have is the rate of change of deaths and hospitalizations.

Weeks ago there was a report that 13.7% of pregnant women admitted during labor in NYC tested positive for covid-19. That was using a PCR test, not an antibody test, IIRC. That surely implies that weeks ago the rate of infection in the city was at least 13.7% minus false positives plus what would have been antibody test positive patients who were not producing the virus any longer. To go from ~13% to 21% in a few weeks seems perfectly reasonable. Moreover, "suggest" is a bit of a weasel word: there's no proof, only statistical approximation. So, yes, I suspect the city really is at 21% exposed.

> The only actual good indicator we have is the rate of change of deaths and hospitalizations.

Nonsense. The right thing to do is to perform randomized antibody testing of the population. The sample has to be random. The city (and other cities, and States) should pick an appropriate number of household addresses randomly, send techs and police to each one, pick a random person at each address, and test them. Given a sufficiently random and representative sample, you don't need to test so very many people to determine with a very high degree of certainty just how much of the population has been exposed.

Only then can hospitalizations and deaths be put in perspective. If SARS-cov2 spreads through enough of the population to reach "herd immunity" and its ultimate impact is, as I expect, much less than a bad normal-flu season, then the shutdown was not necessary and should stop (not necessarily other protective measures).

On the flip side, if very little of the population has been exposed to covid-19, and we were to stop the shutdown due to declining death and hospitalization rates, then the risk would be very high of a second wave.

Randomized antibody testing is absolutely essential here. We need to know how it progresses through the population, and we need to know ASAP.

This might be a bit blunt but I am not jesting - an article about disease testing that does not contain the words "false positive" and "false negative" in some form is literally not worth reading.

Even decent antibodies tests can have 10% false positive rates (bodysphere), and lots of those tests have dodgy provenance and 20% false positive is not out of the question.

Here's a better article, thou still not great (paywall): https://www.nytimes.com/2020/04/23/nyregion/coronavirus-anti...

The closest they come to discussing the errors is to acknowledge there might be some: State officials said the test had been calibrated to err on the side of producing false negatives — to miss some who may have antibodies — rather than false positives, which would suggest a person had coronavirus antibodies when they did not. Not very informative, I know.

A word of caution from the NYC health officials: https://www1.nyc.gov/assets/doh/downloads/pdf/han/alert/2020...

Aha! So perhaps we shouldn't have shut down the economy and financially ruined millions of people, not to mention the hundreds of millions of people on the brink of starvation in developing countries.

Perhaps we just like sharing images of coffins and feeling virtuous.

Even if the study is true, this doesn't follow.

You only get to limit your response if you have the capability to do actual testing... which we didn't and still largely don't.

I'm starting to wonder if there aren't 2 (or more) transmission methods for this virus with differing symptoms. Aerosol transmission with 'I got a cold' type symptoms, and droplet transmission with 'I got the worst cold I've ever had' type symptoms.

Why would different transmission methods result in different symptoms?

Viral load, is what this typically refers to. Akin to an allergic person seeing one cat versus a room full of them.

And, of course, for some people with allergies, contact where a cat has been is enough. ;)

So... Complicated.

Edit: was corrected down thread that this is infection dose, not viral load.

There’s a very weak association between viral load and symptoms once you’re infected.

But that is what the thread was talking about, right?

Would be nice to see studies. My intuition is that it is weak. But my intuition doesn't count for much here.

Is it? I read the original comment as saying there were significantly different symptoms based on the transmission method. Even assuming that the transmission method results in a significantly different viral load, that's not enough to explain differing symptoms since there's not a whole lot of observable difference between cases with different severities[1,2]. There are some studies that show a relationship, but nothing strong enough to explain a dramatic difference.

If it's true that the transmission method makes a big difference, it's more likely due to some other reason. E.g. maybe mild strains spread more easily in the air (although as far as I know there's no evidence that's true.)

[1] https://arxiv.org/ftp/arxiv/papers/2003/2003.09320.pdf

Page 3. "We did not observe significantly different viral loads in nasal swabs between symptomatic and asymptomatic."

[2] https://www.medrxiv.org/content/10.1101/2020.03.15.20036707v...

Page 4. "There was no obvious difference in viral loads across sex, age groups and disease severity"

Fair. My entry to this thread should have begun with a question mark. I thought the point of different transmission methods, as frames, was referring to potential differences in viral load.

Rereading your first comment, I think you might be thinking of infection dose. Viral load is the amount of virus present in a patient. Infection dose is the amount you're exposed to.

So, I was indeed mixing those up. I think most of the following points stand?

There's not much evidence that infection dose matters in terms of case severity either, other than you're just less likely to be infected in the first place.

Perhaps different amount of viral load

Another factor couldd be where the virus load lands - in the lung or the throat.

If this study is accurate, and is not just working on garbage data, and if these antibodies actually mean long-term immunity, this means one of two things:

1. If the lockdown is lifted, we will see another ~60,000 to 100,000 deaths in NYC, before it will hit herd immunity.

2. Or, the lockdown will continue.

The accuracy of antibody studies has been called into doubt, and as far as I know, nobody has ever followed up on whether or not people with study-detected antibodies can catch the virus. The data for the latter is unlikely to be available until the second wave hits.

That would be a very useful follow up study with this same population.

There is no reason why an all or none approach is necessary here, is there?

By my count, 10,120/15,740 of deaths have been folks over 70. If you kept people over 70 locked down (including their caretakers), you could significantly reduce the number of deaths while increasing the percentage that have had it.

In fact, only 9% of New York's population is over 70. So theoretically (though clearly not practically), you could get over the percentage required for herd immunity while reducing the deaths by 66%

> if these antibodies actually mean long-term immunity

This is the big unknown that seems to be largely ignored, thank you for bringing it up. I am frequently seeing "herd immunity" thrown around without any qualification on the durability of the associated antibodies.

We'd also need to make the decsision to continue a lockdown over a virus with a 99.5% survival rate or not. Seeing at the flu has a 99.9% survival rate does it make sense to destroy the economy over that.

Hospitalization rates per infection are greater for covid than for flu. What matters is hospital capacity, to protect against greater non-covid death rates.

I agree, but this is not universally believed. Many people argue that hospital capacity is not what matters, and we must continue lockdowns even if hospitals won't be overloaded to eradicate the disease.

Ah, yeah. If hospitals are fine, I think lockdowns can be swapped for a contact tracing system, assuming enough people use it and assuming easy test access and 24-hour test turnarounds. (And even then... use masks.)

But, in the absence of that, if a hospital is at 50% capacity, I don't agree with opening up a lockdown until a hospital gets to 95% capacity.

(The game changes entirely if we get a good anti-viral.)

But if that held then we'd eradicate flu and the common cold two. Quarantines don't eradicate disease they slow the spread but the area under the curve is constant. I doubt will change the number of infected is a constant regardless of the rate.

That's only true if medical and technological advancements don't happen. A vaccine a year from now means less death if we've locked down in the meantime, compared to if we haven't locked down. Similarly, if we scale up testing and contact tracing to the point we can eventually eradicate the disease through targeted quarantines, a preceding lockdown means less death.

If instead we don't experience any advancements, then right, it just means the same number of infections are spread out over a longer period of time.

Which for me the later seems more possible simply because vaccines from what I heard would take 5 years. Thinks like SARS and zika still have no vaccine despite being around for years.

The flu mutates. The common cold isn't a single virus, but one of hundreds lumped together, that also mutate. So there's no long term herd immunity for either.

But we can't say that it could just be that infection because of a lack of herd immunity is the only thing preventing something like the flu from having the same result.

If they have the same hospitalization rate yet covid-19 infection rate is twice the flu because of the lack of immunity you would immediately double the hospitalizations. There's two variables in that equation

First of all, it's not 99.5%. A naive interpretation of these numbers is a 1% death rate, not 0.5%. 15,000 dead, out of an estimated ~1,680,000 with antibodies.

Second of all, deaths lag infections, and the actual death rate is closer to double the current death rate.

Third of all, you have no idea that if someone has antibodies, they are immune. As far as I know, nobody has tried infecting antibody-positive people... Or released studies that followed up on them, compared to an antibody-negative control group.

Fourth of all, World War II 'only' killed 3% of the world's population.

And lastly, if you lift the lockdown, and everyone gets sick all at once, the infection rate will spike dramatically. Because when hospitals are overloaded, people who can be saved are left to die. Not to mention that you'll be killing thousands of doctors, due to the high viral load they are exposed to.

Only if you think cuomo presentation is wrong because that what they quoted. Also you numbers are wrong 15,000 is only .18% of 1.6 million not 1.8%.

Also death lag infections in numbers but that wouldn't change the ratio of deaths to infections. Unless the infection rates are going up or down. Right now infection rates are lowering so you'd expect the death to infection rate to continue to stay in line with the 0.5% infection rate.

Also we have never shut down the economy with other disease it was generally assumed that you'd at least have limited immunity to disease with antibodies. I don't think swine flu or bird flu had this react why should this.

If we know only the sickest people are getting sick why don't we lift the quaretine on the people less likely to get sick or people with antibodies. It seems like we made the lockdown decision based on a lack of data. I don't know if continuing to do it when we do make sense. Especially because it could make people more sick not less.

Edit: correction used 8 million as denominator. 89% is correct

0.89%: https://www.google.com/search?q=15000+%2F+1.68+million+*+100

Stating the survival rate really hides how there is around an order of magnitude difference in the death rate.

And that ignoring that while immunity towards various types of influenza is widespread, there is no previous immunity against COVID-19 apart from a handful of people that had SARS or MERS before.

I wouldn't expect SARS-CoV-2 immunity from a previous MERS infection, AFAIK their similarity is in their severity of outcomes, not in the immune responses.

It's possible that a less interventionist approach by the government would also have the exact opposite effect by reducing public confidence in the response, and in the safety of going outside, since nothing is being enforced.

As Joseph Stiglitz said the other day "In those circumstances it won’t be the government enforcing the lockdown, it will be fear. The concern is that people are not going to be spending on anything other than food and that’s the definition of a Great Depression."

The purpose of the lockdown isn't to hide until the virus magically disappears, it's to flatten the curve and allow the virus to spread slowly enough that it doesn't overwhelm the healthcare system in any given place.

Maintaining a lockdown will not lower the number of people who get the virus, it will merely extend how long it takes to get there.

At least if you heard the statements of my government (Italy) from a couple weeks ago, they were hoping (or so the press said) to "reduce infections to zero".

Since then, quite obviously, they backtracked from those statements.

Flattening the curve until there is a vaccine might be the right plan for a influenza pandemic. For COVID-19 we can go further: we can bring down infection rates so much that contact tracing becomes feasible again.

Matters where you are. The Bay Area is keeping their lockdown up so they can contact trace and crush any infection spike. Otherwise, we're going to be locked down until a vaccine at the current new case rates.

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