
Hundreds of thousands in L.A. may have been infected with coronavirus: study - contemporary343
https://www.latimes.com/california/story/2020-04-20/coronavirus-serology-testing-la-county
======
strangeloops85
Some more information about the study here: [https://pressroom.usc.edu/what-a-
usc-la-county-antibody-stud...](https://pressroom.usc.edu/what-a-usc-la-
county-antibody-study-can-teach-us-about-covid-19/)

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.

~~~
tempsy
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)?

~~~
JPKab
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.

~~~
brigade
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.)

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

~~~
mgleason_3
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).

~~~
dragonwriter
> 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.

------
papeda
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?

~~~
jupp0r
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.

~~~
loser777
...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](https://news.ycombinator.com/item?id=22924118)

------
mlwiese
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...](https://medium.com/@balajis/peer-review-of-
covid-19-antibody-seroprevalence-in-santa-clara-county-
california-1f6382258c25)

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

------
sjg007
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...](https://statmodeling.stat.columbia.edu/2020/04/19/fatal-flaws-in-
stanford-study-of-coronavirus-prevalence/)

~~~
microdrum
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.

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

------
longtimegoogler
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...](https://statmodeling.stat.columbia.edu/2020/04/19/fatal-flaws-in-
stanford-study-of-coronavirus-prevalence/)

I am similarly skeptical of the findings here.

~~~
Rury
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...](https://imgcdn.mckesson.com/CumulusWeb/Click_and_learn/Premier_Biotech_COVID19_Package_Insert.pdf)

------
bestnameever
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...](https://www.nbcnews.com/health/health-news/unapproved-chinese-
coronavirus-antibody-tests-being-used-least-2-states-n1185131)

------
icodestuff
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.

~~~
DangerousPie
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.

------
bla3
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.

~~~
TheBlight
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...](http://web.mit.edu/jeffrey/harris/HarrisJE_WP2_COVID19_NYC_13-Apr-2020.pdf)
?) 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?

~~~
empath75
I don’t think 50% seems that high given the death count.

~~~
kgwgk
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.

~~~
makomk
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.

~~~
kgwgk
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).

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

~~~
tempsy
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.

~~~
TheBlight
See:
[https://news.ycombinator.com/item?id=22928754](https://news.ycombinator.com/item?id=22928754)

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

------
korethr
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?

------
baron816
Is it possible that there’s a “West Coast/Asian strain” that’s a lot less
serious than the “East Coast/European strain”?

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

~~~
DangerousPie
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.

~~~
TheBlight
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://www.forbes.com/sites/lisettevoytko/2020/03/04/discovery-
of-2-strains-of-covid-19-coronavirus-hints-at-how-it-evolved/#6fd538037094)

------
natrik
[https://covid19tracker.health.ny.gov/views/NYS-
COVID19-Track...](https://covid19tracker.health.ny.gov/views/NYS-
COVID19-Tracker/NYSDOHCOVID-19Tracker-
Fatalities?%3Aembed=yes&%3Atoolbar=no&%3Atabs=n)

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.

~~~
fsh
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...](https://www.nytimes.com/interactive/2020/04/10/upshot/coronavirus-
deaths-new-york-city.html)

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

~~~
benchtobedside
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...](https://statmodeling.stat.columbia.edu/2020/04/19/fatal-flaws-in-
stanford-study-of-coronavirus-prevalence/)

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...](http://www.publichealth.lacounty.gov/phcommon/public/media/mediapubhpdetail.cfm?prid=2328)

~~~
jupp0r
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

------
vanniv
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.

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

~~~
zucker42
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.

------
Smoosh
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://www.theguardian.com/society/2020/apr/20/studies-suggest-very-
few-have-had-covid-19-without-symptoms)

------
neonate
[https://archive.md/Whas8](https://archive.md/Whas8)

------
shadowprofile77
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...](https://www.reddit.com/r/COVID19/comments/g4znbg/at_least_11_of_tested_blood_donors_in_stockholm/)

------
biolurker1
If that's a testing mistake and gives rise to political pressure, it's a
disaster

------
lalaland1125
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...](https://statmodeling.stat.columbia.edu/2020/04/19/fatal-flaws-in-
stanford-study-of-coronavirus-prevalence/) 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.

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

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

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

~~~
TheBlight
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.

------
pcdoodle
May have been? Quick it with the pandemic porn.

------
mikekij
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](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.

~~~
RobAtticus
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.

~~~
artursapek
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](https://news.ycombinator.com/item?id=22685457)

~~~
qqqwerty
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.

~~~
usaar333
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.

------
joe_the_user
Paywalled

~~~
tzm
Press `esc` key prior to page load completion

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

------
throwaway122378
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.

~~~
HideousKojima
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

~~~
qqqwerty
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.

------
JoeAltmaier
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.

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

~~~
tunesmith
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.

~~~
whb07
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.

~~~
SpicyLemonZest
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.

------
rdtsc
3.4% mortality rate reported by WHO seems to be not as accurate then
[https://www.who.int/dg/speeches/detail/who-director-
general-...](https://www.who.int/dg/speeches/detail/who-director-general-s-
opening-remarks-at-the-media-briefing-on-covid-19---3-march-2020)

\--- 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.

~~~
jsnell
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.

