Hundreds of thousands in L.A. may have been infected with coronavirus: study | Hacker News Reader

Hundreds of thousands in L.A. may have been infected with coronavirus: study

latimes.com

Some more information about the study here: https://pressroom.usc.edu/what-a-usc-la-county-antibody-stud...

Population is supposed to be representative of LA county. Tests are from Premier Biotech with some preliminary data on false negative/ false positive rates shown. I suspect assumptions related to the test itself, rather than population, may be a bigger factor for any inaccuracies. There's also the question of whether antibody response implies immunity for SARS-COV2

Another interesting aspect of the result was 6% of men showed antibodies but only 2% of women tested.

Also: --2.4% of people between the ages of 18 and 34 had antibodies to the coronavirus --5.6% of people between 35 and 54 had antibodies --4.3% of between 55 and older had antibodies

Edit: The key assumption that really, really needs to be triply checked here is the specificity of the test. They're assuming 99.5%, similar to the Stanford study I believe. If it's closer to 98% then prevalence estimate confidence intervals should include 0%, I believe. Also should be noted that the same PI on the Stanford study, Jay Bhattacharya, is also involved in this. Same criticisms likely apply here - more caution needed!

Second edit: On further reflection, I think these studies could do serious damage based on how the results are being announced by press-release - in this case, afaik there's no pre-print. In our current environment this isn't just science. Policy is being enacted and people may die because of poorly framing results. Everyone involved needs to take a step back and think through this carefully. Also - did anyone figure out why a hedge fund guy was an author on the Stanford pre-print? And why that same guy then published a Wall Street Journal op-ed about the study without identifying himself as a co-author? Shady stuff.

I’m very skeptical. Why were they so few hospitalizations in LA vs NYC if such a large % in LA contracted Covid? Is this suggesting that a much, much higher % in NYC have it (and the number of those with any serious symptom and thus need for hospitalization was higher)?

It needs to be emphasized here:

This virus is much more transmissible than the viruses we usually deal with in humans.

If a virus has a low fatality rate, but is extremely contagious and prevalent, hospitals will get overwhelmed. A small percentage of a big enough number is itself a big number.

The subway and high-utilization of buses are likely factors in NYC vs. LA. LA is much more representative of the typical US city.

Frankly, I'm not sure why so many on HN are surprised by this. The scientific data on asymptomatic carriers coming out of Italy, Germany, South Korea, and Iceland has been very clear on a majority asymptomatic frame for this virus. I think that maybe authorities and responsible people are concerned that this information could lead to people flouting lockdowns more.

I mean NYC publishes the number of positive tests each day and it’s dropped from 60% to 30%. So majority of people proactively getting a test because they are symptomatic are showing up negative. Hard to believe a very high percentage of all people have had Covid, then. https://twitter.com/natesilver538/status/1252317247785271297...

Isn't that the virus test, not the anti-body test? Maybe a fairly low percentage have the virus but a high percentage have been exposed and have the anti-body.

The optimistic take on this is: that means soon enough will be exposed that their immunity will stop the spread.

The pessimistic take on this is: we also don't have strong evidence that the previously exposed are immune, ie, have a strong enough immune response to stop the virus.

Those 30% numbers are still very high, the similar rate in Italy has dropped bellow 10% for 2 weeks now (it's the line-chart on this page [1] bellow "Tamponi giornalieri e contagiati"), with the latest week's figures somewhere between 5% and 7%.

[1] https://lab24.ilsole24ore.com/coronavirus/

Norway's number of positive tests was 5% at the current peak of the epidemic, for reference. Testing and isolation measures have been very effective here, so we have not had an overwhelming wave of infections (yet, knock on wood).

You are conflating a test for active virus versus an antibody test. my suspicion is that a very large percentage of people in certain neighborhoods of New York City are going to show up having been exposed and never presented symptoms. I just don't compute how else you can explain this with a virus that is so transmissible. we should also keep in mind that the number of deaths isn't necessarily indicative by itself of number of infections since deaths can be caused by hospitals being overrun.

This only tells if you are sick not if you were sick

Already 0.1% of NYC's population has died from COVID-19 - 10k deaths of 8.54 million people. That's... quite a bit higher than LA's current 576 of 10 million.

So either everyone in New York City was infected, or most probably 200-400k infected in LA is an order of magnitude of wishful thinking (e.g. ~2% false positive test rate.)

There's the theory of Vitamin-D deficiency. It would make some sense given NYC's climate and time of year. In LA, people likely are exposed to the sun year round. It would be interesting to see if other respiratory illnesses exhibit a similar pattern between these two cities, as I understand Vitamin-D levels have an affect those as well.

But also, NYC is considerably more dense, so diseases spread far more rapidly. Especially so considering heavy public transit use versus, what I would say is, extreme overuse of personal vehicles.

Another option: A bunch of people will die in a few weeks.

So like the fact that most of NYC, like most of Italy, and Spain is infected because they are a more dense city than LA is lying about the data because this is backed up by data in other countries like Korea, Germany and everywhere else with similar data.

Could some of the discrepancy in regional death rates be explained by a mutation in the virus?

Honestly, it seems more likely that California's much maligned car culture has served us well in this case - ensuring many of us maintain social distance which is impossible on NYC's subway and bus system. All the positive press about governor Cuomo's leadership and handling of the crisis seems a little weird given his failure to shut-down the subways (or at least curtail a but the absolutely most urgent ridership).

> Honestly, it seems more likely that California's much maligned car culture has served us well in this case - ensuring many of us maintain social distance which is impossible on NYC's subway and bus system.

California local and state government being ahead of NY in issuing shelter-in-place orders even though NY was being harder hit even before the divergent response also makes a difference. But, yeah, the hyperdensity of the NYC metro area is a big factor.

It could.

Previous studies suggested that the strain circulating in NYC is of European origin, whereas the California strain, arriving earlier, was of Asian origin.

It's always possible that the NYC one is more fatal. We don't have data, but it is possible.

Thing is, though, at the peak, NYC was seeing a 60% positive rate on tests and is still seeing something near 30%. That pretty much guaranteed that the vast, vast majority of NY cases were not detected, and still probably are not.

(Italy is back around 5-6% again, for example)

It must be the case that a dramatic percentage of NYC is infected. Certainly not more than 100% as some oversimplified maths suggest, and likely not 80%+ that would be very comforting -- but quite likely between 30% and 45%, given the Stanford and LA studies, and the NYC testing data.

Not from, with. Also, gov’t pays a hospital out $70k for every covid death.

This "with, not from" stuff collapses when you look at 5 year averages.

EG this from UK https://twitter.com/ONS/status/1252518098047041536

Seems so obvious that this was the flu but far more contagious from the beginning because of the lack of herd immunity and vaccines. We are just getting verification.

"so obvious".

I love it when people have strong opinions about a complex, constantly changing piece of difficult science.

It's not opinion is the logical conclusion from literally every random sampling study. Pretty much consistently 50x as many people have been infected that we thought. And if we made the current strategy with limited data why aren't we updating what we are doing with more data.

Even the Diamond Princess showed a 50+% asymptomatic rate and that group skewed way, way older than average -- you know, people who take cruises.

50? Just initially, but in a week or two more of these developed symptoms, so more recent number was just 18%:

https://www.cebm.net/covid-19/covid-19-what-proportion-are-a...

"Setting: Diamond Princess cruise ship, Yokohama, Japan (n=-634 tested positive).

Proportion: 18%"

Also:

"23 Residents of a Long-Term Care Nursing Facility King County, Washington

10 (43%) had symptoms, and 13 (57%) were asymptomatic."

But then:

"Seven days after testing, 10 of 13 asymptomatics developed symptoms"

That means that just 3 of 23 remained asymptomatic there too, i.e. 13%.

It is estimated that the people mostly spread this virus exactly in the phase where they still haven't developed symptoms, and the virus can be detected. But that doesn't mean they will remain without the symptoms.

Thanks, I actually didn't realize the number drifted so much over time. Good to know! I'm curious how many of them were actually seriously afflicted vs. a stuffy nose. I'd argue almost 20% actually asymptomatic is still huge in context.

Especially as we know the disease affects these demographics up to 100X more than the young.

For instance consider: "Emerging evidence suggests many more people are infected than tested. In Vo Italy, at the time the first symptomatic case was diagnosed, about 3%, had already been infected – most were completely asymptomatic." [1]

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

> a stuffy nose.

Not a covid-19 symptom.

EDIT: I'm wrong, sorry. Thank you to kgwgk for the correction.

I suspect most people notice a bit of a cold or "allergies"

Like you said, probably only 10% to 15% truly asymptomatic.

there's a really big pro-density crowd online which is working hard to undermine this argument in order to satisfy its agenda.

>Frankly, I'm not sure why so many on HN are surprised by this. The scientific data on asymptomatic carriers coming out of Italy, Germany, South Korea, and Iceland has been very clear on a majority asymptomatic frame for this virus. I think that maybe authorities and responsible people are concerned that this information could lead to people flouting lockdowns more.

Because people on here panicked without thinking and don't want to admit it.

No one wants to say "Oh yeah, I ignored second year calculus and never bothered plotting logistic growth curves with the full spread of expected fatalities. Because of this I supported legislation that's started the next Great Depression. But I meant well!"

We don't know that the fatality rate is off, we just suspect and hope it. Measurements of this kind are how we'll find out.

Are you claiming that everything is normal and would benefit from a complete reopen? Are you also claiming that the economy is in otherwise healthy shape regardless of the interest rate levels and global buyback programs that have been ongoing prior to the outbreak?

A complete reopen, no - crowded stadiums and amusement parks probably shouldn't happen. Things would never have been normal right now, and there's no silver bullet to make them normal again.

I can't speak for the original commenter, but I think the evidence increasingly suggests that we would benefit from allowing people to visit small groups of friends and run non-essential businesses.

It's rough because it's simultaneously very mild in the vast majority of people, but a horrific, suffocating death in a tiny minority, and highly contagious.

I don't know if there is a "right" policy answer on what to do with something like that.

As someone else said, the lockdowns are probably illegal, you can't just throw away freedom of association over the flu. But if we didn't have them, we might have seen another 100k dead..

". . . the lockdowns are probably illegal, you can't just throw away freedom of association over the flu."

Where did you study constutional law? Rights granted in the constitution are subject to many restrictions. There is no explicit "freedom of association" in the Constitution, although it has been held to be implicit in freedom of speech. Free speech doctrine includes a "time, place, and manner" test that's applied to restrictions on speech when the government has a "significant interest" in the restriction[1] (e.g., national emergency for a viral pandemic). I don't know how the lockdowns (which are mostly not full lockdowns) would fare if Supreme Court were to apply the time, place, manner test, but I wouldn't presume to offer an offhand opinion that they're "probably illegal".

[1] https://en.wikipedia.org/wiki/Freedom_of_speech_in_the_Unite...

Not op. I am not a lawyer and I’ve not studied constitutional law. What I recall from high school is that anything not expressly stated as illegal is legal for citizens and any power not expressly given to a state or federal government is unavailable to them.

A law has to exist that gives states and the federal government the ability to do what they are doing and that law needs to not violate special protections built into the Bill of Rights.

With a state of emergency declared, some things can temporarily be enacted that under normal circumstances would not be legal.

I don’t know if all the lock down rules are legal, but I wouldn’t be surprised to find out that governments have overstepped their bounds.

You may be remembering something, but you seem to be remembering it almost exactly backwards. It sounds like you are remembering something about the 10th Amendment, which reserves to the states any power that is not granted to the federal government in the constitution: https://en.wikipedia.org/wiki/Tenth_Amendment_to_the_United_...

In part because all the headline numbers we've seen are CFR numbers which suffer from huge adverse selection bias -- and aren't directly comparable between regions for a lot of reasons.

As I've mentioned before, using CFR to determine fatality rate of the disease is like trying to determine the fatality rate of skydiving by measuring the ratio of people who go to hospital with skydiving injuries over those who recover. Obviously you'd think skydiving kills basically everyone who does it, but it just doesn't.

It's starting to become clear that the infection fatality rate of COVID is in the lower quartile of the range 0.25-1%.

And yeah, a huge portion of New York likely has it. It's super contagious.

Define "huge portion" because will still have to square this with the negative test data from NYC.

Yeah, trying to do the basic math on the implications of these figures...[Not claiming they're true, that's a different question]

The article says 221,000 infected at a 2.8% rate, in "early April". So they're assuming a 7,892,857 population. Currently, we have 601 Deaths total in LA[1], which we can assume are a result of early april event on April 20. So a 0.002% "fatality rate" relative to total exposures, "Woohoo" (NOTE, this is an optimistic figure implied by this study, the standard 2% [edited] previously claim but still not equivalent to the flu, which has 0.2% [edited] relative people who get sick). Now, with weekly doubling: 221,0008 = 1,768,000 infection now = 22.4% - > 4808 deaths. However, past 10%, we can ask whether we start reaching saturation/diminishing returns. But that is a key question.

-- Let's look at NYC. There we have 14,500 Death. Using the article's implied figure, 14,500/ 0.002 = 7,250,000 infected on April 1, out of 8,398,748. So this implies an infection rate of 7,250,000/8,398,748 = 86.32239%, which, in fact, seems very implausible and which implies; a higher death rate among "vulnerable people". Maybe we're about to hit full saturation with NYC and see an immediate decline.

Now, the other bad* scenario is the possibility that a lot of sickness and death NOW is being driven by reinfection, that exposure isn't giving much immunity at all and instead of the unexposed dying, what we're seeing is the re-exposed dying and with this dynamic, the death rate settle into a steady and unacceptably high level. Note that Italy, which has seen a high death rate, hasn't a strong drop-off at all, just gradual plateauing. IE, guess what, one possibility is "herd immunity" just doesn't exist for this virus.

Anyway, time will tell, literally a week will tell if NYC has enough fatalities to make this 0.002 exposure-to-death rate numerically impossible.

[1] covid-19.live

Edit: corrected conversion ratio to percentages.

What’s the source on reinfection? That sounds kind of extraordinary.

It seems weird to think the body can clear an infection but have no ability to fight it later.

There is none, it is just speculation/hysteria like so much else about this virus.

There's been only a very few cases globally where a person who previously tested negative later tested positive, for which a test giving a false negative is a much more likely explanation - especially as these cases were early on when tests were more unreliable. Otherwise - everybody else who tests negative does so consistently.

The virus is very similar to the original SARS (genome about 80% the same which is huge). It is a relatively stable by virus standards not mutating massively. In fact, if you had SARS your are likely immune from covid19: https://marlin-prod.literatumonline.com/pb-assets/journals/r...

So the general scientific consensus I have seen is that there's no evidence to suggest covid19 is some sort of supervirus that will reinfect people and defeat your immune system.

There's also been some animal testing on reinfection : https://www.biorxiv.org/content/10.1101/2020.03.13.990226v1

Yes, reinfection is speculation and limits the virus to infect the population is speculation. Beyond the situation of the virus growing exponentially, there are few certainties.

I add this point because most of speculative arguments are used to claim allowing the virus infect a large portion of population won't cause much harm.

> Note that Italy, which has seen a high death rate, hasn't a strong drop-off at all, just gradual plateauing.

Regional differences? Lombardy's death rate is less than half of peak

I think that number should be 0.2% (2 / 1000) and not 0.002% (2 / 100000).

Yeah, 2% is the standard and more plausible in many ways than 0.002% [edit, WRONG ,missed a decimal point, should be 2% & .2%]. My comments above are about what this study implies, this something new.

Italy has had 24114 deaths. If take those as being in Northern Italy with a 27,000,000 population, that's 0.8931 % of the entire population. Even adjusting for age, that can't squared with 0.002% death rate [wrong wrong, it's 0.08931%, which is possible but with a huge infection rate, which maybe].

I think you've misunderstood the comment by zucker52: your post is using 0.002 and 0.002% interchangably. It's quite hard to figure out what you mean when the numbers are off by two factors of magnitude in places.

For example:

> So a 0.002% "fatality rate" relative to total exposures,

You appear to be deriving this from the computation 601/221000. That gives .27%, not 0.002%.

> Italy has had 24114 deaths. If take those as being in Northern Italy with a 27,000,000 population, that's 0.8931% of the entire population

No it's not. 1% of 27M is 270k, not 27k.

"Now, the other bad* scenario is the possibility that a lot "of sickness and death NOW is being driven by reinfection, that exposure isn't giving much immunity at all and instead of the unexposed dying"

This would explain: NYC vs LA vs Italy. Why isn't this theory brought up more?

"Wang, whose full name has not been disclosed for privacy reasons, is one of more than 100 reported cases of Chinese patients who have been released from hospitals as survivors of the new coronavirus — only to test positive for it a second time in the bewildering math of this mysterious illness."

https://www.latimes.com/world-nation/story/2020-03-13/china-...

> Why isn't this theory brought up more?

Because if reinfection so close to recovery were possible, it would make this virus really unique, and it doesn't really fit the available data very well. It makes much more sense that the virus takes a long time to clear after symptoms abate, and the tests have a really lousy false negative rate.

The common cold is infamous for mutating at a rate where you can catch the virus again two months after infection. And moreover, the common cold is also a coronavirus.

Moreover, if a lot of Covid infections are in the area and the virus is mutating quickly, a person might be exposed to a different strain soon after recovery and wind up infected, if the SAR-COV-19 is similar to the common cold. This would be hard to notice, since it would tend to happen as the virus was spreading rapidly anyway. It would become a problem only if people expected the virus to abate once everyone had been exposed.

See: https://www.technologynetworks.com/immunology/news/why-dont-...

The main reason should be that speculating on both this and the exposed-and-maybe-immune isn't that relevant for the more responsible approaches to managing the situation - isolation, quarantine, safety equipment, testing and contact tracing.

This kind of speculation is relevant to the more irresponsible approaches; "developing herd immunity" and "immunity certificates" but it mostly should be a reminder to "don't do that because you really don't know what you're getting into".

It’s not clear this is the responsible solution as there’s no evidence the entire process wouldn’t start again the minute restrictions are lifted due to the sheer infectiousness of the disease and the large number of people with no symptoms or limited symptoms.

There’s clearly a lot of false negatives produced by the PCR testing we’re doing nationwide.

What is CFR?

"In epidemiology, a case fatality rate (CFR) — sometimes called case fatality risk — is the proportion of deaths from a certain disease compared to the total number of people diagnosed with the disease for a certain period of time."

As compared to the IFR: "The term infection fatality rate (IFR) also applies to infectious disease outbreaks, and represents the proportion of deaths among all the infected individuals. It is closely related to the CFR, but attempts to additionally account for all asymptomatic and undiagnosed infections." [1]

One's the risk that given you show up at a hospital, you die. The other is the risk given that you get infected, you die.

[1] https://en.wikipedia.org/wiki/Case_fatality_rate

Case Fatality Rate; the rate of people who are confirmed to have COVID-19, and die. The problem is that the people most likely to be tested are those in the worst condition.

According to the New York data, the fatality rate is about 0.5% for tested persons without underlying conditions. Considering the gross selection bias it likely means actual death rates in the healthy population close to that of the flu. It is this data that shows the lockdowns were a complete illegal overreaction. Locking down nursing homes would have fixed most of it.

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

I don't understand what calculation you're doing here.

The worst flu season we saw in a decade killed (~80k / 300M) = .02% of the US population. NYC has seen ~14000 deaths, or .18% (~14k / ~8M) death rate for the entire population, and while the absolute worst may be over, people are still dying at a high rate in NYC.

The only way I can make sense of this is that you're somehow mixing figures relating to deaths in healthy populations (COVID) vs. overall death rates in flu (including older/unhealthy populations).

I don't think it's right to compare whole US population with worst-case NY population. Of course worst-case would be much worse that average case.

What exactly do you mean by worst-case here? The actual worst case would be if nobody took any precautions, which almost certainly would result in more than 0.18% of the total US population dying.

New York seems to be affected by COVID pandemics way worse than any other place in the US. That's what I mean by "worst case".

Depends on what you're comparing. I agree we can't assume that the rest of the country will see similar infection rates. However, it's less clear to me that fatality rates would be incorrect.

On the other hand, there's one very specific way that considering NYC gives us data we can't (yet) get elsewhere. While it's possible that we're underestimating disease prevalence by 50x elsewhere, it's basically impossible in NYC (almost 2% of people already have tested positive, with less than 10% of the population being tested).

Note that the test NYC is using only tells you whether the person has Covid-19 at that point in time, so it's entirely possible to test even 100% of the population and still substantially underestimate disease prevalence if you tested most of them at the wrong time (which you probably would!)

> However, it's less clear to me that fatality rates would be incorrect.

Depends on whether mortality rates can be variant on environmental conditions, such as viral load, population density, access to healthcare/ICUs, population socio-demographic profile, etc.

Nah, my calculus is basically as follows. The disease is not bad for people who aren't old, and who do not have pre-existing conditions. The data is pretty unequivocal there. The number of deaths under 20 worldwide rounds to zero. It's incredibly contagious (and contagious while no symptoms are shown), and a vaccine is 12-18 months away at a minimum. There's no way we're going to be able to contain everyone indoors, with no jobs, for 18 months while we await a vaccine that may not arrive. And that's just here in the US -- the weakest link dominates.

If we don't develop herd immunity ASAP, and instead pursue a course of lockdowns, as soon as we lift the lockdowns (either voluntarily or because people just walk out -- see the midwest), we'll immediately start playing rolling lockdown whack-a-mole as China is. The first contagious person who flies in from a foreign country (or domestically) without perfect lockdowns will re-ignite the wildfire.

We should do exactly what Sweden is doing and what the UK proposed: isolate and provide support services to those who are at risk, and let out those who aren't. I think it speaks volumes that Sweden's new infection rate stabilized at the same time as the rest of the world but without lockdowns.

It's really the only path forward. Is it perfect? Of course not. People will die. However, there's no world in which about 70% of the population won't get the disease before the vaccine arrives, so we need to control who gets it, when, and in what order, to minimize harm.

> but without lockdowns.

While I agree mostly with your assessment, it's worth mentioning that the lack of official lockdown in Sweden doesn't mean there isn't a lockdown. According to Google's mobility data for Sweden, their lockdown activity looks pretty similar to what we see in the US. People are voluntarily staying home.

> their lockdown activity

Since Sweden doesn't have a lockdown, this statement is meaningless. A major objection to lockdowns is their involuntary nature. In general, there is a big difference between choosing to stay home, and being coerced (with the threat of force) to do so. One is "choosing to do what you believe is best", the other is "prison".

Your calculus has no numbers in it.

Different kind of calculus.

The serological surveys are showing 50-100x infection rates of those tested. If tested healthy persons are dying at 0.5% that gets you to 0.01%-0.005% death rate in healthy persons. That number is lower than the all cause death rates of 25-34 year olds...

https://www.cdc.gov/nchs/data/databriefs/db355-h.pdf

I see. You're correct that while the overall death rates in NYC are vastly higher than any season flu, it's mathematically possible that those are all deaths of unhealthy people.

It's a creative interpretation of the facts.

One important note: it's impossible that NYC is overestimating cases by 100x. Close to 2% of NYC has confirmed cases.

Try to find some 5 year / 10 year averages to see how excess mortality because of covid-19 compares.

Here's one for the UK: https://twitter.com/ONS/status/1252518098047041536

Illegal, no, but a massive overreaction. This is why the Swedish model (of telling people to take reasonable steps like not getting too close, and staying home when sick, and away from old people) is proving so successful. They've even started to see a flattening of the curve alongside the rest of the world without lockdowns. [1, 2]

[1] https://fee.org/articles/sweden-s-top-epidemiologist-covid-1...

[2] https://aatishb.com/covidtrends/?location=Canada&location=Sw...

Let's not lose sight of the reason why lockdowns were advised in the first place.

Absent the data from widespread testing, the spread of the disease was consistent with two epidemiological models -- low-contagion/high-mortality and high-contagion/low-mortality. In the latter model, there's not much to be done to stop the disease from infecting the population at large, and not that much that needs to be done, since the vast majority of people will emerge unscathed. But in the former model, it matters tremendously -- doing nothing will slowly but surely result in vast loss of life but quarantines can stop the disease dead in its tracks.

Therefore the most cautious course of action, from a public health perspective, was to assume the former and proceed with a regime of extreme social distancing. Unfortunately doing so had dire economic consequences.

What if the swedes are trying to find out how to live with this virus? Like..what if there were no cure and no immunity? Unlike other governments who are betting that there will be a resolution in the near future?

They knew within a week or two that the lockdowns had no effect. We converged right to the same level as Sweden. That they kept pushing it is insane.

What explains the difference between NY and CA if it isn't lockdowns? The difference in death per capita between the two is staggering (nearly 30x more people per capita have died in NY than CA).

To be fair, the US engaged in lockdowns well after the situation had run its course in China so we had the data. It feels much more like a panic reaction than a question of which epidemiological model it best fit.

No way. China did an even more intense lockdown. If anything, the data from China indicated we should have done more, sooner. And that’s assuming you can even trust the data from China. Which you can’t.

The answer to "what is the correct action to take in the face of uncertainty" is not "whatever people say we should've done after we gain full information." That's nonsense, it assumes that next time you should just know the future.

Something that you might consider looking at when comparing countries or areas is the ratio of active cases to recovered. A higher ratio suggests that the epidemic is in an earlier stage.

Sweden has about 23 active cases for each one recovered. The US is about 10:1, Europe overall is about 2:1, so is Italy, and Spain is close to 1:1. Asia as a whole is about 1:1.

That likely has to do with availability of tests, who's getting tested, where and when. With Sweden not attempting to stop the spread, I doubt they've done wide-spread population testing. They're likely limiting it to hospitals, so the fact it's flat its really what matters until they kick off their wide-spread serological study in the works.

[edit] I misread, disregard, but preserving for posterity.

There is random sampling being done. 2,5% had an ongoing infection in Stockholm in late Mars.[1]

Now, 11/100 blood givers in Stockholm who has not been sick in the last two weeks have antibodies according to researchers.

That gives you a naive mortality rate of 0,4% per infection, assuming the spread among bloodgivers is the same as the population in general and just dividing the death count by 11% of the population. It is a high eastimate as I guess many wont get traceable antobodies and mostly asymptotic people give blood.

[1] (Swedish) https://www.folkhalsomyndigheten.se/publicerat-material/publ...

Active vs. recovered is a metric related to the people who are tested and confirmed. It cannot be changed by testing fewer people, only by rapidly ramping the number of tests up or down.

Part of the reason why lockdowns were resorted to in the first place is that Americans are, for various reasons, not likely to listen to official guidance about staying apart. See: all those panic articles about various people who knew they were at high risk and continued to go about their daily lives, or take flights or other forms of transporting masses of people, etc.

In fact, the general take is that Americans have been pretty good at following distancing rules with or without hard rules but some were slow to do so (and most government was in retrospect slow to strongly recommend doing so) but, to a first approximation, few will do so over an extended period.

So no.

Quarantining the sick is legal but otherwise the freedom of assembly clause in the 1st amendment does not say "except when there is a pandemic or other fear". If you don't believe that should be the law fine, but since it is law and is incorporated to the states via the 14th amendment it qualifies as illegal.

Freedom of assembly, like the other 1st amendment freedoms, can be regulated by government so long, per prior SCOTUS decisions. They key is that restrictions must be content-neutral and can only pertain to "time, place, and manner," additionally surviving strict scrutiny (which, in plain English, means "there's no other way to do it"). This is bread-and-butter First Amendment court case stuff; any US government class that's at least halfway competent ought to have covered this.

The coronavirus bans on public gathering are fairly clearly permissible under SCOTUS precedent.

I would argue the second amendment doesn't explicitly say you can't own a nuclear warhead for personal amusement, but I would argue nobody's going to challenge the validity of such impositions.

"Arms" in the context used by the second amendment is AFAIK generally understood to mean "small arms" and by implication would exclude such weapons by matter of definition.

Probably because they know it's futile, not because it shouldn't be covered under the Amendment.

Can you please provide links to judicial opinions or rulings that validate your interpretation of the law?

Not quite; you'd have to drag this through court to get any sort of legal determination on it.

> Underlying illnesses include Diabetes, Lung Disease, Cancer, Immunodeficiency, Heart Disease, Hypertension, Asthma, Kidney Disease, and GI/Liver Disease

That's a whole lot of people left out of your number. A quick google suggests that 1/3 of Americans have hypertension and 9% have diabetes. It seems like probably the majority of the US has at least one of these underlying illnesses.

How many healthy people do you think that dies because of the flu?

Probably the same number of young and healthy people that die of COVID. For instance COVIDs fatality rate is lower than that of the flu for all demographics of people under the age of 35 per CDC data. It's about the same for 35-44 and really only diverges over 45. Worldwide the fatality rate under 20 rounds to zero. To date, pneumonia far outstrips COVID. [1]

[1] https://www.cdc.gov/nchs/nvss/vsrr/covid19/index.htm

This is completely inaccurate, and nowhere in the linked document is there supporting evidence to your claim that the flu has a higher fatality rate for under 35, nor the same for 35-44.

And this report relies on figures that diverge dramatically from the COVID death tolls. It lists the current total report as around 15k, when we're close to 3x.

> This is completely inaccurate, and nowhere in the linked document is there supporting evidence to your claim that the flu has a higher fatality rate for under 35, nor the same for 35-44.

Here's some more data. What I'm saying (that it's approximately the same as the flu for the young, with some early evidence pointing to it being better) isn't particularly controversial and well supported by evidence [1] (also older, April 14th). CDC as of late March "... no ICU admissions or deaths were reported among persons aged ≤19 years. Similar to reports from other countries, this finding suggests that the risk for serious disease and death from COVID-19 is higher in older age groups." [2] In Italy nobody under 30 died, and only 5 people between 30 and 39, as of April 19th -- yes in spite of a CFR of what, 13.22%? [3, 4]

More data as of April 20th from Oxford [4]. Scroll through all the data the world has to offer, I'm not wrong on this. I repeat, the facts you're arguing with me over, without citing data to support your case are not controversial. Surprising maybe, but not controvercial.

Especially when you consider (an albeit bad flu): 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. [4] The flu can absolutely be very bad is the take away there. And yeah this is worse than the flu, by around 3X on both ends of that range. Not bad, not great.

> It lists the current total report as around 15k, when we're close to 3x.

They actually address that critique, that it takes a few weeks for case data to be finalized and reported upwards, so the CDC data can be up to a few weeks behind. It's not divergent, it's delayed. This is a live list updated and maintained by the CDC. Make of that what you will.

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

[2] https://www.cdc.gov/mmwr/volumes/69/wr/mm6912e2.htm

[3] https://www.bloomberg.com/news/articles/2020-03-18/99-of-tho...

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

About 1500 people in the US died yesterday of covid; year-to-date, about 1300 people died each day of the flu.

So it seems like there should be no possible way to frame it as less severe than the flu, even only comparing to when the flu is at its worst and even assuming 100% of the population has covid or whatever other extreme things you want.

This can’t possibly be true about the flu. There’s been over 100 days this year and nowhere near 130000 people in the US have died of the flu in any year in recent memory

Good catch, I think I was using global figures from worldometer.

Note that for the flu, most of the community does literally nothing. I mean, some people get vaccinated, but most literally ignore the flu as if it didn't exist - except if they get sick of course, and even then some people go to work if they can (they shouldn't, really, but they do).

If we compare this with complete shutdown of all public activity on the other end of the spectrum, we can see there are things we could be doing that are on neither ends. Maybe distancing but not shutdown, or avoiding some mass gathering but still being ok with individual meetings, or having places like restaurants operating at reduced capacity to ensure people aren't too close to each other, or ask people to wear masks when they enter stores, etc. And strict lockdown for places like nursing homes, but more relaxed for places like college, or having people of high-risk groups to work from home, but people with lower risk be allowed to work in the office. I'm not saying it's the right way to do right now, but it's a possibility, there are options.

So if this is e.g. 50 times worse than the flu, then trying any of the above may be too risky. But if it's kinda sorta like the flu, maybe somewhat worse but not 50x worse - then it may be prudent to consider measured response, given that for the flu we basically have no response at all and we're ok with that.

So, given that I was quoting global figures for the flu, on an apples-to-apples basis it seems like covid is about 10x the flu at the moment, in the US, ignoring the fact that it could be much worse without the lockdowns.

None of us know for sure, but isn't it plausible on the face of it that something 10x worse that could easily become 100x or 1000x worse warrants the lockdowns? I don't know if this is common sense, but in an alternate universe it could be.

> year-to-date, about 1300 people died each day of the flu.

That seems impossible. CDC estimates 61k flu deaths in 2017-2018 and 34k in 2019-2020: https://www.cdc.gov/flu/about/burden/index.html

That is nowhere near 1.3k per day.

You are right, my point would have been much stronger with the correct figures.

That's not entirely true. The flu has a vaccine (of varying efficacy -- 10-60% depending on the year). The flu also has a large body of immunity built up across the population from having had it. It's also less contagious.

If we assume that COVID is spreading like wildfire (it is), and that we're front-loading the cases (we probably are), and that immunity will be built up as one-off instead of like having the disease mutate substantially every year like the flu, over a few years, it could easily be better.

I'm not saying it is or isn't, just that there is a path.

I think NYC almost certainly has much large prevalence than LA given hospitalization data alone.

One factor could be the nature of transmission. Regions where transmission occurs with high viral loads entering at the point of infection (for example, hospital-acquired infections) could see more severe disease progression. You would therefore expect a high hospitalization rate relative to prevalence. It's possible in other regions, including CA, hospitalization rate as a function of prevalence is lower because acquisition is largely at lower viral loads resulting in less rapid and severe disease progression. This is, of course, a hypothesis and very likely not correct. Time will tell.

Other important factors include age distribution of infection, as well as weather conditions and prevalence of pre-existing conditions.

I also suspect and absolutely think a large portion of the reason there is a decrease in hospitalizations is due to mitigation efforts decreasing the initial viral load. That is absolutely borne out of all evidence we know thus far for this virus and is also in line for what we know to be true about other viruses.

Excellent point!

Yes agree NYC will have much higher prevalence but it has 17x the deaths - if fatality rate is similar then it would be like 50%+ of New Yorkers have contracted Covid. Just a little hard to believe.

5.6m people ride the New York subway everyday.

Actually, no they don't. There are 5.6m rides per day. The average trip contains at least two rides: there, and back again. Many trips contain substantially more than two legs, and many people take more than one trip per day.

I can't tell you how many people ride the subway every day, but it's certainly less than half of the number of rides per day.

Number of rides is actually more relevant in this context than number of distinct riders. Each trip involves a new set of people next to you in the train car and a new chance of getting infected.

There’s a decreasing number of positive test cases each day in NYC too though. At highest point was 50% and now under 30% or so. I’m not sure if those tested get the antibody test as well but it implies a larger percentage of symptomatic people are testing negative in nyc.

Almost no one gets an antibody test yet, unless it's some special controlled study.

Closer to 2 million: https://nyc.streetsblog.org/2020/03/23/the-subway-ridership-...

What makes it hard to believe, exactly?

One way to explain the hospital surge in NYC is that a small percentage of people have had a highly fatal disease. Another plausible explanation is that a much larger number of people have had a less-fatal disease.

To date, we've had very poor estimates for fatality rates and prevalence. It wouldn't be shocking at all to find out that we're off by a factor of 10-, 20-, or even 50-fold.

Typically a sufficiently infectious disease tops out around 50-80% (don't have a source, just what people, like Angela Merkel have been saying). So this implies NYC is already off the high end of that. Hard to believe.

How is that hard to believe that incredibly obvious with the dropping hospitalization rates

NYC has had ~141,000 infections confirmed. If the high end of this study held for NYC (50x), then the true infection count would be around 7M.

That isn't impossible at all, given that the population of NYC is around 8.8M people. It would mean that the city is approaching herd immunity.

(I don't personally think the ratio will be as high in NYC as in LA, but that's just a hunch based on the higher testing rate.)

https://www.worldometers.info/coronavirus/country/us/ right now shows 12850 cases per 1M in NY, which is 1.2%. Which is pretty close to what the study shows for LA. So it's not actually suggesting NY has much more than it's already known, if we assumed overall percentage would be similar for both. It does suggest California - which currently lists 853/mln - undercounts it by more than 10x. Of course, provided that the study is true.

Also, having antibodies and being hospitalized is a very different thing. If a lot of the cases are asymptomatic or very light you won't see hospitalizations and you'd never know the person had an encounter with the particular virus until you do mass testing. And NY is quite different from LA, as far as I know, in terms of how dense population can be and how much people use mass-transportation and other high-risk scenarios, and the probability of being hospitalized may also depend on that (in other words, it may matter not only whether you had any contact with the virus, but in which circumstances and how much of the contact you had).

That doesn't prove the study is true of course, but I don't see anything from comparison with NY that makes it implausible on its face.

Yes. The consensus I've seen forming is that at least 15% of NYC has had it by now - a variety of questionably random samples have suggested numbers near that.

NY has 17x more deaths than California. That would mean if 3% of CA was infected (yes this is LA only but for ease of comparison) it would be 51%+ of NY was infected. Far more than just 15%.

LA is one of the densest parts of CA though, outside of SF/Bay Area. So if LA is 3%, you could assume the state as a whole is maybe 1-2%. Then 17x that becomes 17-34%, which seems more reasonable.

The comparison is overweighting LA county, which has 25% of California's population but 60% of its deaths. (I don't know what the math ends up being off the top of my head.)

CFR is not a great way of comparing across regions, it could be due to any number of factors. Demographics (i.e. age), co-moribidities and the amount of hospital space available.

In fact the per-country spread of CFR is huge, from a low of 0.07% in Singapore to a high of 14.93% in Belgium, a whole 3 orders of magnitude. The H1N1 influenza CFR at a similar point in the outbreak was between 0.1% and 5.1% -- but the final IFR ended up at 0.02% [1].

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

Consensus among whom?

https://www.nejm.org/doi/full/10.1056/NEJMc2009316

15.6% of pregnant woman at a given hospital were PCR confirmed over 2 weeks. I haven't seen strong arguments this wouldn't be broadly characteristic of the overall population. Worse (or better depending on how you look at it), there's significant false negatives with PCR testing.

It's credible NYC could be at 30% by April 4 and given that confirmed cases doubled since then, could be at 50%+ by now.

You can't extrapolate from pregnant women who have been visiting medical facilities to the general population.

How off do you think this can be? Women go once a week to a medical facility (and that's to an ob-gen, not the emergency room) before birth, but also don't go out in general. You could just as well make the case this is lower than the general population.

It's for sure true that a huge number of people in NYC got it, way surpassing the confirmed cases count. The state is currently doing a serology study here and results should be published next week.

Sure I believe NYC has way higher prevalence of cases but difference in deaths would imply majority of population was infected and they’d essentially be at herd immunity now. It’s not implausible but find it hard to believe.

We'll know soon enough. The blood serology randomized trial is underway. I suspect some neighborhoods might be at 50%, but the city overall won't be.

Maybe they'll finally end this ridiculous lockdown when that one is released

Might NYC have more vulnerable people, or more confined spaces for spreading (elevators in high rises, e.g.) ?

Case fatality rate is # of deaths / # of positive tests. It’s enormously biased by the availability of testing, and the allocation of the tests we have to the sickest people. It’s not especially likely to generalize to the population at large. We need large scale random sampling of the population for that.

Everyone is eager for these results, because there’s a good chance that the true case count is higher by anywhere from 2x to 100x. If it is, then proportionally fewer people are expected to die as the virus eventually does churn through the whole population.

It’s probably still too early to tell, but this is promising.

I think the obvious conclusion (just in logical progression if you take this premise as true) is that the percentage of NYC residents who contracted it was much higher than previously assumed.

Yes but the difference in fatalities is so large that it would imply majority of New Yorkers were infected and the city has achieved herd immunity. It’s not implausible just find it hard to believe.

It's hard to believe, I would think, because it would be such a remarkably positive development.

But just from a logical perspective it follows clearly from this data, and there's nothing fundamental about the situation that makes it particularly unlikely.

Of course it's quite possible that the LA data isn't really applicable for any number of reasons. But the idea that COVID will run through dense populations mercilessly, but quickly, and then sort of peter out is completely within the range of possible and non-shocking outcomes.

It's the natural outcome of an extremely communicable disease with a large prevalence of asymptomatic transmission and there's plenty of corroborating evidence for the idea that this is one of those.

It's also quite likely that the false-positive rate with this test is super high. Who knows.

If 50% of NYC has had the virus, they won't have herd immunity yet -- of these studies are correct, then the virus is horrendously infectious -- making 90%+ of population required for immunity.

Just speculating here but I hung out with some NYC folks last month in SoCal and they practically all smoked. It was very strange, I rarely encounter smokers living in California.

Cigarette smoking rate is widely studied; no reason to use anecdotal data here.

The tobacco smoking rate in New York is 14.1%; it's 11.3% in California. Some difference, yes, but far less difference than you see between either and some rural southern states where the numbers can be roughly double that.

https://www.cdc.gov/statesystem/cigaretteuseadult.html

LA is far less dense, fewer travel by public transport, and the weather is much warmer.

The people are probably healthier too.

Yes, that is the implication. And if this is the case, that is very good news.

No the very obvious conclusion is that covid-19 isn't very deadly. Thats what every test has concluded. Its that the death rate is closer to .3% than the 3% that the WHO quoted and much closer to the flu's death rate of .1%

Premier is an importer for "Hangzhou Biotest Biotech Co". Most of the validation data in the Stanford preprint also appear in this sell sheet:

http://img.bj.wezhan.cn/content/sitefiles/2005911/images/132...

Stanford reports doing a 30-person validation test with pre-COVID-19 hip surgery patients (100% negative). But this is not enough to validate these population-scale tests, especially when 98.5% specificity (rather than 99.5%) would fully invalidate the headline result.

> Stanford reports doing a 30-person validation test with pre-COVID-19 hip surgery patients (100% negative)

Hmm - I guess I should now go to the source - but from the other link: " test validation included a total of 30+371 pre-covid blood tests, and only 399 of them came back negative." https://statmodeling.stat.columbia.edu/2020/04/19/fatal-flaw... - I think I trust it more

Yes, the 371 are listed on the sell sheet (see tweet above). Unclear if Stanford was involved, but I would suspect not.

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...

According to the Q&A it is the same test as the Santa Clara study, so concerns about false positives apply equally to both.

It seems like they should be doing multiple tests, as independent as possible, per blood sample, and seeing if they agree?

> Another interesting aspect of the result was 6% of men showed antibodies but only 2% of women tested.

That is interesting. So far, men are more likely to die of covid-19 (almost a 2:1 ratio) than women; if this is a repeatable result it would seem to suggest that the reason is that fewer women are becoming infected, not that women are somehow less likely to exhibit extreme symptoms when infected.

Or that women are less likely to need antibodies to clear this infection. Not everyone develops antibodies for every disease, the immune system is super complicated.

The Imperial College model was also published by press release. That will have ended up doing a whole lot more damage than any of these serosurveys, when all is said and done.

Aren't these results consistent with Netherland's findings (3% of blood donors had COVID-19 antibodies) ?

> Shady stuff

If I'm being honest, whether it's logical or not I wouldn't be surprised if this is politics and misinformation starting to creep it's way into science. In current events there's a struggle to open up the economy with Trump saying we're ready and the that governors are at fault for keeping everything closed while the are governors saying it's way too early and they need to keep everything closed. This may be an attempt to make the public think the situation is safer than it is (if more people have been sick than thought then we could potentially have herd immunity or be closer to it) which the public can then put pressure on the governors.

Who knows, but I will say it's getting harder and harder to say some thing are too tin foil hat lately.

No tin foil hat required. This is exactly what they are trying to do[1].

[1] https://www.commondreams.org/views/2020/04/20/trumps-reelect...

Our initial policy was based on early data from China which seemed to imply 3% mortality, an R0 of 2.4 and a 5% hospitalization rate. In the past month, data from all sources and methods is showing this to be far less deadly but also far more contagious. Perhaps it’s time to adjust our view of the lockdown.

I think the view that the infection fatality rate is significantly lower than the case fatality rate has already been factored into public policy.

Because even if the IFR is around 0.5% then that still works out to > 1,000,000 people dead in the US in an uncontrolled spread scenario.

It would have to be another order of magnitude lower than that to be comparable to the seasonal flu.

Uncontrolled? No, but we do know who is most vulnerable and we should be able to make more targeted and surgical measures to limit the negative impacts of social distancing.

Well, R0 is probably still about 2.4 in most cities in the United States (NYC, like Wuhan, considerably higher).

Source suggesting this: https://www.medrxiv.org/content/10.1101/2020.04.12.20062943v...

I think it has been pretty obvious that IFR is not 3% for a long time. Imperial College had dropped it to 0.67% in China 3 weeks ago (https://www.thelancet.com/journals/laninf/article/PIIS1473-3...)

Here is a basic question: how are the sensitivity and specificity (false negative and false positive rates, IIRC) of these tests determined?

It seems like a learning problem with noisy labels. Does anyone know what they do in practice? Especially chaotic situations like this where there aren't reference tests? Are there reference positive and negative samples?

One concern also referenced for the Stanford study was that, as mentioned in a USC popular writeup on this study linked elsewhere on this post,

> Premier Biotech, the manufacturer of the test that USC and L.A. County are using, tested blood from COVID-19-positive patients with a 90 to 95% accuracy rate.

They must be factoring this into the 2-5% infection rate number somewhere?

In the Stanford study, they used blood samples that were taken before the epidemic to determine the true/false negative rate, and blood from patients that tested PCR positive in the past to determine true/false positive rate.

...which has been characterized as being analyzed in a highly suspect way [1], where only point estimates of the specificity were used.

[1] https://news.ycombinator.com/item?id=22924118

The Stanford study didn't properly include the specificity in their predictions. See here, https://statmodeling.stat.columbia.edu/2020/04/19/fatal-flaw...

Reference negative samples are relatively easy, at least in theory. Any blood samples drawn before the Covid-19 outbreak started are guaranteed to be 100% negative.

The sensitivity and specificity of these new tests is not established. It's part of why the FDA hasn't approved the tests yet. Scientists are doing their best with tests with unknown reliability, but it's a statistical mess right now.

Other more squishy factors are important, too: I don’t think Facebook posts for recruitment are a good idea. This could lead to more people taking part who have been sick or who had contact with someone who had the virus, over-representing that group of people in the sample.

This LA study has one of the same authors as the Stanford study (Neeraj Sood). It uses the same test and so it has the same issues with the false positive rate. An honest person would address those but I didn't see that in the press release.

This post describes the issues: https://medium.com/@balajis/peer-review-of-covid-19-antibody...

See also this thread: https://news.ycombinator.com/item?id=22924118

Both the LA and Stanford studies are junk. Easily explained by false positives inherent to the test. Stuff like this is why pre-print services can have disastrous consequences.

https://statmodeling.stat.columbia.edu/2020/04/19/fatal-flaw...

That article is junk. Even the fact that it quotes Santa Claras deaths at 100 deaths is wrong because it was only 70 at the time. This entire blog is rife with mathematical and statistical errors.

So, in your ideal world, we would never do serosurveys for surveillance of low incidence diseases, right? Any Type 1 error is too much Type 1 error, right?

I'm sorry that literally all of the seroprevalence data hurt your priors. But the virus is widespread, not very deadly, and people are going to go back to work soon. You can stay home. You will be safe at home.

No need to be condescending. This is hacker news not Reddit.

The Stanford study in Santa Clara that used Premier Biotech appears to be very misleading. Given the confidence intervals on the specificity all 50 positive cases out of the 3330 tested could just be false positives.

See Gelman's article on the topic.

https://statmodeling.stat.columbia.edu/2020/04/19/fatal-flaw...

I am similarly skeptical of the findings here.

Especially considering the test used here was also, Premier Biotech's. Their manual [1] states, that other coronavirus strains can lead to false positives in the test:

>Positive results may be due to past or present infection with non-SARS-CoV-2 coronavirus strains, such as coronavirus HKU1, NL63, OC43, or 229E

[1]https://imgcdn.mckesson.com/CumulusWeb/Click_and_learn/Premi...

They appear to be using an antibody test from Premier Biotech. However, I can't gain clarity on whether we know if these tests are even accurate or reliable.

"But some COVID-19 antibody tests, including those being used by public health departments in Denver and Los Angeles and provided to urgent care centers in Maryland and North Carolina, were supplied by Chinese manufacturers that are not approved by China's Center for Medical Device Evaluation, a unit of the National Medical Product Administration, or NMPA, the country's equivalent of the U.S. Food and Drug Administration, NBC News has found.

Two U.S. companies — Premier Biotech of Minneapolis and Aytu Bioscience of Colorado — have been distributing the tests from unapproved Chinese manufacturers, according to health officials, FDA filings and a spokesman for one of the Chinese manufacturers. Many of the unapproved tests appear to have been shipped to the U.S. after the FDA relaxed its guidelines for tests in mid-March and before the Chinese government banned their export just over two weeks later.

If COVID-19 antibody tests are unreliable, they can produce false results, either negative or positive, health officials said. The use of such tests has been widely discussed as a way to ensure that employees are healthy enough to go back to work and to find COVID-19 survivors who may be able to provide blood plasma to severely ill patients.

Officials at the Association of Public Health Laboratories have expressed concern about the reliability of the numerous antibody tests being sold or used across the country with little scrutiny. "

see: https://www.nbcnews.com/health/health-news/unapproved-chines...

Does anyone have a link to the actual study? LA Times link doesn't include it. Other than the selection criteria for participants, it doesn't sound from the pressroom.usc.edu link that it's any better than the Santa Clara study, and perhaps worse, given the smaller number of participants. The pressroom link mentions an "accuracy" number, but not specificity and sensitivity.

That really tells you everything you need to know about this study already, doesn't it? If they put out the press release before the actual study you know what the authors' priorities are.

Just a press release, no pre-print. That being said it was being co-run by LADPH it seems. Many people involved were also involved with the Stanford study which has been very thoroughly criticized for its assumptions.

NYC has 20x the deaths / 100k people, so under the assumption that number of deaths are on comparable trajectories (due to both locations being under lockdown, maybe not as unrealistic as a few weeks ago), NYC's prevalence could be 56%-112%. This could be one explanation for why numbers are going down in NYC, or the 112% being larger than 100% could suggest that either the situations aren't comparable or that the LA study's numbers are a bit high.

LA's numbers seemingly agree with Santa Clara's numbers as well. I would guess NYC probably has a much higher rate of infection (possibly due to http://web.mit.edu/jeffrey/harris/HarrisJE_WP2_COVID19_NYC_1... ?) But 50%+ seems pretty high.

Is it also possible different strains are dominant in California and NYC with different mortality characteristics? There is also the issue of weather. A recently leaked DHS report indicates sunlight radically reduces virus half-life and it's also affected by temperature and humidity. Maybe subway + weather + (possibly) strain of differing virulence can explain the discrepancy?

'More subway' doesn't really capture it. NYC has ~70x the daily ridership on metro rapid transit.

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

Overall population density is much higher as well:

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

NYC also has the lowest car ownership in the nation, so you're basically left with walking or uber/taxi/public transportation just to get to the store/doctor/etc.

https://www.governing.com/gov-data/car-ownership-numbers-of-...

Also ~25% more airline traffic hauling bugs in from everywhere.

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

Combined with a long transmissable dwell time before symptoms, wouldn't surprise me if 90%+ of the people in NYC were materially exposed.

Spain & Italy have very similar weather to California, but have been hit really hard.

Because of demographics. The disease really, really doesn't affect young people. The younger, the better you do, to the extent that the percent of people under 20 that have died in every major area of the world rounds to 0%. Older demographics are hit hundreds of times harder than young to middle aged folks.

Italy has the oldest average population in Europe, and Lombardy has the oldest average population in Italy. 99.2% of those who died in Italy averaged 80.5 years old and had an average of 3 co-morbid conditions. [1]

[1] https://www.bloomberg.com/news/articles/2020-03-18/99-of-tho...

Italy also appears to have suffered from tragic public health errors. Apparently they moved covid-19 infected patients around. First to other hospitals that had not yet had outbreaks and then into empty beds at long-term care facilities.

https://www.cbc.ca/news/covid-19/italy-covid-19-outbreak-les...

It affects young people, it just doesn't kill them. They spread it as well as anyone else.

"spread" isn't "affected" -- someone with no symptoms who spreads it really isn't affected. The people who get it from them may well be.

So far the data is clear: it doesn't really do much to young people. [1] Speaking broadly, almost nobody under 20 has died, practically nobody under 30 has died, and a handful of 30-40 year olds have died. The trendline is super clear: if you're young, you're gonna be just fine. The CDC says young folks aren't even likely to wind up with serious disease or in the ICU ("no ICU admissions or deaths were reported among persons aged ≤19 years. Similar to reports from other countries, this finding suggests that the risk for serious disease and death from COVID-19 is higher in older age groups.") [2]

When I say "affected" I mean that they are not likely to develop serious symptoms, certainly severe symptoms and they're definitely not likely to develop life-threatening symptoms.

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

[2] https://www.cdc.gov/mmwr/volumes/69/wr/mm6912e2.htm

We know for a fact(well based on this report from a Chinese univesity[1]) that there are two strains of covid-19, I wonder if the less aggressive strain has been around awhile more than the more aggressive strain, it may have been around a lot longer than the outbreak was announced (think fall 2019) and may have been misidentified as the flu/bad cold. And it would explain why some people are getting extremely mild cases, they either have the less agressive strain or already had it but now have the antibodies to fight it. This is total conjecture mind you and the weather aspect is also interesting, look at Hawaii's cases and death rate(580 cases to 10 deaths, they are both very low numbers).

[1] https://www.forbes.com/sites/lisettevoytko/2020/03/04/discov...

That study is really not very good and doesn’t tell you anything.

Sure, mutations happen all the time, however since the virus has error correction those are rare and very unlikely to have any effect on the phenotype.

I don’t think 50% seems that high given the death count.

If the true infection rate is 50% they are very unlucky with their tests because less than 40% of the people tested give a positive.

Not really. Antibody tests like this one measure the total number of people who've been infected at some point in the relatively recent past, whereas the diagnostic tests New York is using only detect current active infections, so it's easy for the percentage of infections measured by the former to be higher than the latter.

When articles like this compare the number of known cases detected with the PCR diagnostic tests with the higher estimate based on antibody testing, they're comparing the sum of all cases detected since the start of PCR coronavirus testing with a point-in-time estimate of the proportion of the population that currently has antibodies for exactly this reason.

That’s true. We could have 25% active infections and 25% not currently infected but who were infected in the past. I still think the actual numbers may be be lower (but higher than the reported figures, of course).

> less than 40% of the people tested give a positive.

FWIW, Queens and Brooklyn (Kings county) are at 50.7% and 48.3% positive tests respectively.

https://covid19tracker.health.ny.gov/views/NYS-COVID19-Track...

I meant people being tested nowadays, not cumulative numbers.

Today: Kings 548/1835 = 29.9% and Queens 786/2428 = 32.4%

If we're going to talk about the total number of people who have been infected, wouldn't it make more sense to use the overall test rate?

50% means every other person would have to have been infected. That seems a bit unlikely to me but I suppose it's possible.

It seems very improbable since the positive rate for tests in general are around 10% (at least in San Mateo county).

It doesn't seem that unlikely to me (I live here). I know so many people, me included, who had it but aren't included in any official count for lack of testing, and so many others who must have had it (e.g. my cohabiting SO) who were asymptomatic.

You can't just take numbers and multiply them like that.

What about average age? Number of icu beds? Effect of temperature/humidity on the virus? Population density? &c. There are so many variables

The point is that either these numbers are not accurate, or that there are crucial differences between LA and NYC. Either conclusion is interesting.

I don’t really understand your point. The error bars would be calculated differently for a larger sample. If you had 100/100 positives with a five percent error you wouldn’t say the true amount is 95 to 105. You’ve done some sloppy statistics and claimed this invalidates the work...

NY has performed 32k tests per 1M population, CA 7k per 1M population [1]. If CA PCR-tested as much as NY, there would probably be way more official cases and your calculation would make more sense.

[1] https://www.worldometers.info/coronavirus/country/us/

edit: typo

Death rate higher could be due to an overwhelmed health care system, so it is not linear. Once that threshold is hit, it will probably edge close to rate of severe cases among the patients.

NYC is massively overreporting deaths. Just the other day they added 3700 "new" deaths that didn't even test positive, they just suspected (ie. pulled it out of their behind) that it was cause of death.

Death numbers are overreported in general - Dr. Birx herself even claimed that if you test positive but die of something other than the virus they track it as a COVID death. It's preposterous.

Belgium is doing something similar. They're being more aggressive than any other nation in Europe at labeling deaths Covid. It has contributed to their extreme case mortality rate.

https://www.politico.eu/article/why-is-belgiums-death-toll-s...

I listened to Birx[0] and perceived the same. I continue to wonder what I'm missing. If she's correct, there would be serious problems with such methods. Strange stuff.

0. https://m.youtube.com/watch?v=blZpgra3XbU

Makes it a bit harder to deny the validity of the Stanford/Santa Clara results. They're virtually the same.

One issue is that the study design and probably analysis is very similar to the Santa Clara study (they are using the same test kits and have shared coauthors on both studies). It's not really independent replication when both are potentially using flawed kits and analysis.

That's because the methods and authors have major overlap. The USC study reported here is a collaboration with the same group at Stanford:

https://news.usc.edu/168810/usc-covid-19-antibody-researcher...

The participant selection here was done differently, but the same problems arise with the false positive rate of the tests.

I'm not sure if we can consider this independent support: both studies use the same antibody test. Any problems with the test itself (or our understanding of problems with the tests) will be present in both studies.

also in line with the dutch results a week ago (https://www.nytimes.com/reuters/2020/04/16/world/europe/16re...)

Well, a lot of the same people are involved in both..

Looking forward to what the science heads on my twitter feed say about it. They were positively withering in their criticism regarding the Santa Clara study.

How do you explain the low number of hospitalizations and deaths in LA vs NYC? If these numbers hold then you’d expect NYC to be close to herd immunity given the difference in fatalities.

See: https://news.ycombinator.com/item?id=22928754

More questions than answers. It hasn’t been hot in LA so suggesting it has to do with heat doesn’t make sense imo.

There's a logical jump from the 2nd to 3rd paragraph that bugs me, but upon reflection, I don't know if my objection is valid:

> The initial results from the first large-scale study tracking the spread of the coronavirus in the county found that 2.8% to 5.6% of adults have antibodies to the virus in their blood, an indication of past exposure.

> That translates to roughly 221,000 to 442,000 adults who have recovered from an infection, according to the researchers conducting the study, even though the county had reported fewer than 8,000 cases at that time.

So, in the 2nd paragraph, there, a match for SARS-COV2 antibodies means an exposure. In the 3rd, it means an infection. I have always understood that exposure != infection. Yes, the former is a necessary precondition to the latter, but an exposure doesn't automatically and unconditionally become an infection.

But then it occurred to me that the body probably isn't going to be generating antibodies unless a pathogen got past the body's initial lines of defense and required a more active response from the immune system. In which case then, perhaps it would be accurate to say antibodies == infection. Are we speaking then of asymptomatic carriers or those who never experienced more than mild symptoms and might not have realized they were infected?

Am I splitting hairs here? Is this simply shorthand for "exposure that subsequently became an infection?", much in the same way I might freely use "LDAP Server", "Name Server", "Domain Controller" "Auth Server", to all refer to the same system in an on-prem Windows environment? Or, am I rightly objecting to unclear language that would lead to incorrect conclusions?

Is it possible that there’s a “West Coast/Asian strain” that’s a lot less serious than the “East Coast/European strain”?

There's been a bunch of genomic analysis. Yes, the West coast strains are closer to Asian and the East coast strains are closer to European. But the differences between strains are believed to not have an effect on the disease.

https://nextstrain.org/narratives/ncov/sit-rep/en/2020-04-17

https://nextstrain.org/help/coronavirus/FAQ#is-one-strain-of...

As more serology surveys are completed I expect this question to come to the forefront.

How exactly? Serology has nothing to do with figuring out which version of the virus you have, ignoring the fact that there are no distinct "strains" to begin with.

I suspect we're going to see different IFRs in NYC vs. the West Coast. There are already at least 2 strains: https://www.forbes.com/sites/lisettevoytko/2020/03/04/discov...

https://covid19tracker.health.ny.gov/views/NYS-COVID19-Track...

89% of total fatalities in New York have at least 1 comorbidity. I wonder to what extent anyone with unknown cause of death or death due to cancer, but also had COVID-19 gets lumped into the death due to COVID-19 category.

The overall mortality rate in NYC is more than double the usual [1]. I guess that most people above a certain age have at least one comorbidity such as diabetes or high blood pressure.

[1] https://www.nytimes.com/interactive/2020/04/10/upshot/corona...

Does this study make the same mistakes as the Standford study?

One particular criticism of the Stanford study was that they recruited via Facebook adverts, and that method may be influenced in recruitment by the "desire to get a test if you had reason to believe that you, or someone near you, had the virus"

In more detail: https://statmodeling.stat.columbia.edu/2020/04/19/fatal-flaw...

This USC study seems to have recruited differently: "Participants were recruited via a proprietary database that is representative of the county population. The database is maintained by LRW Group, a market research firm" Via: http://www.publichealth.lacounty.gov/phcommon/public/media/m...

The flaw was that participation was voluntary, with the effects you describe. I think this is also the case with this study. I'm not sure how a non-voluntary study could be conducted, you can't really force people to participate in this.

edit: typo

The biggest flaw is that they didn't use the confidence intervals associated with the specificity in their calculations properly.

Given the CI's of the specificity all 50 positives could have been false positives so we can't conclude anything from the study.

Some of the authors are the same, so most likely yes.

With the NYC analysis here, everyone is missing one unknowable piece of information.

We know that the initial major outbreak in NYC was with a strain from Europe, while the initial major outbreak in California was with an Asia-origin strain.

These might be differently dangerous. If the NYC/Europe strain is just 20%-30% more fatal than the Asian one, the maths all start being believable (you start saying that 30-45% of NYC has been infected -- which, unlike 60-100+%, is quite believable.

It seems likely that some vulnerable neighborhoods in NY have effectively everyone infected, but that is probably not universally true.

It also seems quite believable that we are nearing the point where the majority of folks in NYC have been exposed. If nothing else, the subway is still seeing quite active use, and must be a breeding ground for infection -- and yet, hospitalization has started declining, meaning that new infections are slowing down -- meaning that at least among folks that still go out and about, there must just be fewer people to infect.

With subway use still being as high as it is, it seems like it must be true that, amongst the population using it, R could only fall below 1 if herd immunity (in the rider population) was beginning to form.

This also jives with the numbers we are seeing, where reported cases are down despite both increased testing and looser counting.

I see a lot of pushback on these studies showing high infection rates. To those people, what rate do you actually believe and why?

There's not enough data to "believe" anything, and most of the pushback is because the studies are methodologically flawed. I think that fatality rate estimates between 0.5% and 1% seem to best match some of the early data on isolated populations, but I'm far from an epidemiological expert. That said, I think people would be a lot more accepting of data contradicting those rates if it didn't have clear issues.

I feel more informed when I see an infection rate that's substantially higher than the rate of false positives from the test. So the NYC test of pregnant women at (13? 15%?) seems to be much higher than the 1% to 4% range of false positives that people seem to be estimating. But when test results are "down in the noise" I don't feel we're necessarily measuring anything.

I don’t see our squares with our Australian numbers. How have we been able to bring the case numbers right down if there are as many asymptomatic cases and as high an R0 as these studies suggest. Our overall testing of people with symptoms has an under 2% positive rate with one of the highest per capita testing rates in the world. We have a lockdown but it isn’t so extreme as to be able to stop the kind of spread these studies are suggesting.

The vast majority of our non imported cases have also been successfully contact traced, people by and large seem to be contracting it from other confirmed cases.

Based on these studies I think >90% of people in California don't have it, and how much higher is unknown.

Interesting that this article says only 2~3% with antibodies globally. I'm left wondering how reliable the testing (of all kinds) is.

https://www.theguardian.com/society/2020/apr/20/studies-sugg...

https://archive.md/Whas8

This recent Swedish study was done in (what seems) to be a way that's much less prone to bias than soliciting people via Facebook posts.

It was also based on tests collected from blood donor samples, so none of the specific bias of the Stanford study (which to me also seems like a huge flaw even if other sources of evidence are pointing to similar IFR numbers and undercounting)

In any case, the Swedish findings cited for Stockholm in the analysis linked to in this COVID19 subreddit post indicate a similarly low overall IFR of far below 1%.

It is worth noting that blood donors would probable tend more towards youth and general good health, which could skew its demographic profile. Also, depending on donors' motives for donating (maybe they thought it would include a COVID test) could skew results in different and unpredictable ways.

https://www.reddit.com/r/COVID19/comments/g4znbg/at_least_11...

If that's a testing mistake and gives rise to political pressure, it's a disaster

Understanding this study is very difficult because they haven't released all of the details about statistical procedures, recruitment, raw data, etc, but I would be worried that this study will have similar issues to the recently published Santa Clara study (both use the same lab test and share several authors).

I highly recommend that people read https://statmodeling.stat.columbia.edu/2020/04/19/fatal-flaw... for information about for information on how the Santa Clara study messed up. TLDR: Incorrect statistics, incorrect analysis, and incorrect sampling probably led to a vast overestimate of both prevalence and certainty in that high prevalence estimate.

The huge difference is that the LA one claims to have tested a random sample of people while the Stanford one did not.

Some of the same authors, possible/likely they are using the same test.

That isn't huge. The biggest issue with the Stanford study was that they didn't take into account the Confidence Intervals of the Specificity.

Doesn't sound like that is a sufficient difference, however, if they also screwed up the stats and the analysis.

What impact will this, or should it, have on public policy?

In isolation I don't think this study can really shift the differing and primarily political response momentums the various states have going. Truly randomized nation-wide studies would need to be completed probably by the CDC for there to be wide-spread consensus on the results. The politics are going to move quicker than the science.

May have been? Quick it with the pandemic porn.

Tl;dr: It looks like ~4.2% of Los Angeles residents may have already been affected.

Current stats from [https://covid-19.direct/county/CA/Los%20Angeles]: Confirmed cases: 12,349 Deaths: 601 Population: 4M Implied fatality rate: 4.87%

If this study is correct (~4.2% prevalence): Cases: 167,580 Deaths: 601 Implied fatality rate: 0.36%

There is massive difference between the appropriate response to an illness with a 5% fatality rate and an illness with a 0.36% fatality rate.

Certainly, but I don't think anyone was assuming 5% fatality rate. There were concerns about it being 1-3% fatality, but even those would be worst cases.

Worth noting that .36 is roughly 3x as deadly as the flu.

Just listened to an interview [1] with a doctor that claimed we probably over-estimate the risk associated with the seasonal flu.

[1]: https://reason.com/podcast/we-make-the-weather-why-voluntary...

3x as deadly as the flu, in a population of people who have already been exposed to the flu (and some even taking flu vaccines constantly).

The evidence will continue to build that this virus is pretty benign compared to all of the fear mongering has been suggesting. Of course, HN downvoted me when I suggested this last time https://news.ycombinator.com/item?id=22685457

Closer to 4x and only between 10M and 40M people in the US get the flu every year. Coronavirus could be >4x more deadly and have >10x more people infected.

Lets ballpark and say it's 3x more deadly and only 3x more people get it than the flu, 40M * 3x * .0003 == 36k deaths. However, we've blown past that many deaths already, and we have nowhere near 120M infected people so far, so the math just doesn't work. It's far more than 3x more deadly than the flu, or we have far more infections than we can account for in any study.

Also keep in mind that the death statistics have largely only counted people with positive tests who died in a hospital, so it's going to be strictly under counting.

New York, Italy and Wuhan are all the evidence you need. The virus is perfectly capable of overwhelming local health care systems, at which point shut downs are the only responsible option.

In high density areas or with a substantial older population, yes.

Sweden didn't shut down and their health system isn't at the point of any of those locations. [They have a lot of people dying though - so that's not to say not shutting down is a good idea].

It's also pretty unclear from Seattle's trajectory pre-lockdown it was every going to be overwhelmed if it didn't shut down.

I don't know how you can say that when hospitals have already hit the breaking point. It's not like our health care professionals have been sitting around twiddling their thumbs.

Only a few hospitals in the very worst hotspots have hit the breaking point. I don't mean to say that we could have or should have done nothing, but the idea that there's been some widespread breakdown in medical care is just not accurate.

How can you interpret many countries across the globe facing hospital and ICU bed shortages and not call that widespread?

And many more hospitals were only 1-2 doubling periods away from faring as bad or worse. Doubling periods that were avoided because the surrounding regions took the actions they did.

That's not what you'd believe if you watched cable news ;)

People should downvote confident assertions like this that lack supporting data, along with the dismissiveness that people are fearmongering.

There's a chance you're right, and people want this to be true, but when you're wrong you're very wrong and people die.

What is the relevance of people having immunity to the flu or taking flu vaccines?

Of course there isn't any. Sure, it would matter if this were a buzzfeed listicle about "most dangerous viruses of all time". The flu might be higher in that list. But the actual question is "given this virus, which we don't have acquired immunity to, and don't have a vaccine for, how many people will it kill, and what are reasonable steps to lower that number"?

You need to factor in that some of the currently infected are going to die.

I think your math is a bit off. From the paper 221,000 to 442,000 adults are believed to be infected. This makes the IFR ~0.13-0.27%. Source: http://www.publichealth.lacounty.gov/phcommon/public/media/m...

Note that the death counts we're measuring correspond to what happens when there is sufficient treatment / health care capacity. We have to assume it would be higher if hospitals were overwhelmed and patients who needed ICU beds were not given it.

The study is for Los Angeles County, not LA, so the population is 10m.

You'll want to keep in mind that the death rate spikes if the medical system is overwhelmed and there are medical shortages, which has not been the case in Los Angeles. There is plenty of examples globally and in NY that this virus is capable of overwhelming metropolitan areas.

A lot of this is besides the point because what matters is hospitalization rate, and we've already reached the point where some hospital systems are stressed. Beyond that point, people die that otherwise wouldn't. So I'm not sure there's a big difference in appropriate response.

we've already reached the point where some hospital systems are stressed.

Only NYC has gotten anywhere near their capacity, and even there (as far as I know) nobody has been denied care due to insufficient resources. Many hospitals in other areas are mostly empty and even cutting shifts, e.g. https://www.insidesources.com/unexpected-consequence-of-covi...

you don't take into consideration how contagious it is, it's not only important the percentage but the absolute number and also the time (due to resources constrains)

>> There is massive difference between the appropriate response to an illness with a 5% fatality rate and an illness with a 0.36% fatality rate.

Is there? What is the appropriate response to 0.36%? The flu has a mortality rate of 0.1%, which seems comparable, except we all have some immunity from having the flu multiple times in the past. Flu shots are available for vulnerable people. So while people on average have a 1 in 1000 chance of dying if they get the flu, they also only have a 1/40 to a 1/7 chance of getting the flu at all. The probability of getting corona virus appears to be much, much higher for people exposed to it, and we have no idea what the rate of infection could get to if we let it run free and behaved normally.

Paywalled

https://github.com/iamadamdev/bypass-paywalls-firefox

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This used to work on some sites, but i just tried it here about 20 times and couldn't get it work.

863 people were in this study. A market data firm selected those people.

LA proper has about 16,000,000 people.

This study should be taken with a grain of salt either way for now.

Provided the people were chosen truly randomly/make up a representative sample of the population, 863 is more than enough to get an accurate estimate of the infection rate. ~1000 is a big enough sample size for the entire US, let alone just Los Angeles

I used to work on survey designs in my old job. I am having a hard time coming up with a way that they could possibly control for selection bias. We are in the middle of a pandemic, the people willing to risk leaving their homes to get tested is going to be highly skewed towards individuals that believe they have already had the infection. It's possible that they asked the participants about this, and adjusted their weights accordingly, but considering that this study shares authors with the Santa Clara study, and that study did not control for selection bias, I have my doubts.

Where's the marketing company's criteria for sample selection? How are the communicating and choosing the people? Are they all within the same locality?

And no 863 is not representative of LA. LA is made up of individual communities and some never leave those communities. Some communities are pass through for commuters. Really important where those 863 are from. All are 863 from a small area? Are some from Pasadena and other from Santa Monica. What about Covina or Redondo? LA or Beverly Hills.

Not sure what significance there is. Sure, some intermediate statistics change (e.g. what is the true percentage of bad reactions). But nothing changes the true death rate, which is what, the single largest mortality stat in America right now? Arguing over this fraction and that, isn't going to make this less deadly.

It matters for moving forward.

If it seems to be a highly spreadable virus, and a small percentage of the population has contracted it, then a significant reduction in the current societal restrictions will cause a spike in new infections, which leads to hospitalizations, ventilators, and deaths.

If a large percentage of the population has contracted it, and the results we have seen are due to that, then there is less risk in re-opening things.

I can't speak to this study's bearing on that, but it's one good reason to care how much of the population has already contracted the virus.

It literally is, though. If the denominator is three times larger than we thought, that makes the virus three times less deadly.

And of course the second most important thing to know I think is whether or not antibodies confer immunity. We've all been assuming herd immunity is the key, either by vaccine or the "we all get it, but slowly enough to not overrun the healthcare system" method.

Right now we're on the edge of a precipice. My read of "the people" is they will hold off a bit longer if some reasonable-sounding fact-ish based plan comes out soon. It can be a little wrong, but it has to be credible. Knowing how many people have had it, and whether or not those people are now safe, is imo the two biggest components of planning the next steps. If we can't get credible answers to those questions soon, I believe the population will just start making decisions based on hunches/beliefs/feelings/etc. Some will remain isolated, others will rebel. It won't be pretty.

Keeping in mind though that if that means it's more contagious in turn, it also makes it that much harder to achieve herd immunity.

Not that we should be considering "natural" herd immunity under any circumstances. It's a horrible idea.

No one ever said you wouldn't get this though. The whole idea from the get go was to prevent a run on the hospitals, not to prevent society from catching this.

Every passing day more data and studies are reflecting the realities of this disease not being as deadly as imagined. So perhaps, just like chickenpox, its better to just get it as a kid then to face it later as an adult.

I'm growing more sympathetic to the wild theory that this is actually no worse than the endemic coronaviruses, and the deaths we're seeing are just what happens when a bad cold hits a population that didn't build immunity to it in childhood.

Again, doesn't save a single person.

3.4% mortality rate reported by WHO seems to be not as accurate then https://www.who.int/dg/speeches/detail/who-director-general-...

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

That was used as a basis quite a few decisions, how to treat patients, when to close / open various locations, etc. It does say "reported" in the report, but I think often that part of ignored and many officials assumed it to be as a rate based on infected cases.

The article does mention this issue, and I wonder what the new / updated mortality rate is then based on this data.

I mean, you're tecnically correct in that the number is no longer accurate. But actually the change has been in the other direction. That link is talking about the CFR, not the IFR, and the CFR is up to 7% these days. (170k dead out of 2.5M confirmed cases).

No credible source that I saw ever suggested that the IFR was 3%, and that is not what any decisions were based on. Everyone knows that some cases will be missed. The early IFR estimates were all huge ranges like 0.1%-2%, precisely since there was so little information early on about how many cases were not being detected. (And it is surprising how little we've managed to narrow that range down).

There are other claims in that WHO link that are obviously wrong in retrospect. E.g. the claim that Covid transmits less efficiently than the flu.

I think all the serious scientific papers I've read have long put fatality estimates far below what early WHO press releases suggested.

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