
Covid-19 Antibody Seroprevalence in Santa Clara County, California - buboard
https://www.medrxiv.org/content/10.1101/2020.04.14.20062463v1
======
cuchoi
Caveat from the paper:

"This study had several limitations. First, our sampling strategy selected for
members of Santa Clara County with access to Facebook and a car to attend
drive-through testing sites. This resulted in an over-representation of white
women between the ages of 19 and 64, and an under-representation of Hispanic
and Asian populations, relative to our community. Those imbalances were partly
addressed by weighting our sample population by zip code, race, and sex to
match the county. We did not account for age imbalance in our sample, and
could not ascertain representativeness of SARS-CoV-2 antibodies in homeless
populations. Other biases, such as bias favoring individuals in good health
capable of attending our testing sites, or bias favoring those with prior
COVID-like illnesses seeking antibody confirmation are also possible. The
overall effect of such biases is hard to ascertain."

~~~
cameldrv
Yes, and look at the way they corrected for this. They rebalanced for
demographics, and that doubled their estimate. This is exactly the opposite of
what they should have done. For example, zip codes farther from the testing
sites were less likely to get tested, and more likely to be positive.

What you see is that certain groups were less likely to go get tested, UNLESS
they had a good reason to think that they were positive. This should cause
them to want to underweight the data from the underrepresented groups, but
instead they overweighted it.

You can almost say that there were two populations that they were testing:
White women who lived near a testing site who thought "what the hell, I'm
bored with this quarantine so might as well get my finger pricked", and people
who thought "I wonder if that thing I had a few weeks ago was COVID." The
number you want is the prevalence from the bored white women. Bored white
women might be somewhat skewed from other demographics, but probably not by
that much. People who thought they might have had it is a massively skewed
group. Effectively their correction removes the bored white women near a
testing site, and only counts the people who have reason to think they had
COVID.

~~~
guenthert
With the average adult getting two bouts of common cold each year and mild
COVID symptoms resembling a common cold, I'd think a large fraction of the
population might think they had it. I wouldn't dear to guess in which way this
shifts the estimate.

I think it's understood, that with such a small sample size, results are to be
taken cautiously. You have to start somewhere though.

~~~
cameldrv
There was another serological survey of blood donors in Castiglione d'Adda in
Italy. That one came up 70% positive. That town had 1.4% of its population die
in March. That suggests an IFR of 2%, not counting people who were sick and
might die from COVID in April.

~~~
usaar333
We do need to be careful talking about "IFR" as with a disease with such high
age-dependence demographic skews heavily alter population IFR.

Castiglione d'Adda has more people over 70 than Santa Clara County has over
65; population over 65 is about 60% higher relatively. Population under 18
conversely is about 22% lower.

Hard to do the math exactly, but if you simply switch 8.1% of your population
from being children to being 80+, you raise IFR by 0.6% per the Imperial
College China estimates. Combined with the hospital triaging Italy was doing,
I don't think sub-1% IFRs in Santa Clara county are improbable. (though I do
think this survey's claims are improbably low)

sources: [https://www.citypopulation.de/php/italy-localities-
lombardia...](https://www.citypopulation.de/php/italy-localities-
lombardia.php?cityid=09801410001)

[https://www.census.gov/quickfacts/santaclaracountycalifornia](https://www.census.gov/quickfacts/santaclaracountycalifornia)

[https://www.thelancet.com/journals/laninf/article/PIIS1473-3...](https://www.thelancet.com/journals/laninf/article/PIIS1473-3099\(20\)30243-7/fulltext)

~~~
cameldrv
I agree that you have to make an age adjustment for IFR, and your numbers seem
reasonable. However, in the U.S. you also have a lot more diabetes, obesity,
and high blood pressure, which are major risk factors. I wouldn't be surprised
if that required a bigger adjustment than the age difference.

~~~
usaar333
Agreed that US as a whole can have a high IFR.

Santa Clara county though is one of the healthiest places in the US. Life
expectancy of 84 exceeds Italy in fact.

------
sxp
There are similar high infection rates from other semi-randomized samples.

[https://www.nejm.org/doi/full/10.1056/NEJMc2009316](https://www.nejm.org/doi/full/10.1056/NEJMc2009316)

> Between March 22 and April 4, 2020, a total of 215 pregnant women delivered
> infants at the New York–Presbyterian Allen Hospital and Columbia University
> Irving Medical Center . All the women were screened on admission for
> symptoms of Covid-19. Four women (1.9%) had fever or other symptoms of
> Covid-19 on admission, and all 4 women tested positive for SARS-CoV-2
> (Figure 1). Of the 211 women without symptoms, all were afebrile on
> admission. Nasopharyngeal swabs were obtained from 210 of the 211 women
> (99.5%) who did not have symptoms of Covid-19; of these women, 29 (13.7%)
> were positive for SARS-CoV-2. Thus, 29 of the 33 patients who were positive
> for SARS-CoV-2 at admission (87.9%) had no symptoms of Covid-19 at
> presentation.

~~~
usaar333
NYC has 30x the per capita death rate of Santa Clara county.

Santa Clara county only being 15% as infected is not consistent.

~~~
notforever
Just a hypothesis, but if being more frequently exposed makes it more likely
that the disease will be more severe, then this is plausible. There is some
evidence that e.g. doctors/nurses were more likely to die compared to their
age cohort, possibly because of higher viral load.

~~~
cactus2093
I've definitely seen the opposite stated, that doctors/nurses are not actually
more likely to die. I forget if it was Italy or NYC, but the fatality rate for
doctors & nurses had been something like 0.5%. They are much more likely to be
infected than anyone else, and there has (rightly) been lots written about the
doctors and nurses who have died, which gives people this perception.

------
gryson
"We recruited participants by placing targeted advertisements on Facebook
aimed at residents of Santa Clara County..."

I wonder if these ads mentioned the purpose of the study, or if that
information was only given out after initial contact (I don't know enough
about IRB requirements here).

A concern is that this could cause a selection bias for people who suspected
they had the virus. People may respond to such an ad out of curiosity ("I
think I had the virus, so it would be good to know for sure") or obligation
("I'm pretty sure I had the virus, so I should help out with this study").

It wouldn't have to be a large selection bias, either. Of the 3,330 people
they tested, they found only 50 who tested positive.

I would like to see a bit of a better method of sample selection before
drawing any conclusions.

~~~
not2b
Yes, the ads accurately described the study. Several friends of mine
participated (I live in Santa Clara County).

Of course there is selection bias, which is why they make adjustments to make
the sample better represent the population (see the abstract for details on
this, or read the paper for all the details, there's well-established science
behind these adjustments and it's up to the reviewers to be sure that they did
it correctly). The point is that "they only found 50" is quite a large number
compared to the number of confirmed cases.

~~~
gryson
What adjustments were made to account for selection bias of the sort
described? It's not covered in the paper, and "well-established science"
certainly doesn't answer the question.

~~~
compiler-guy
These are very common statistical methods when dealing with surveys and
populations. It isn't this paper's job to describe the background on something
like that, just like it isn't this papers job to explain blood testing.

They describe their limitations and adjustments exactly like most other
studies of this sort do. For example:

"Those imbalances were partly addressed by weighting our sample population by
zip code, race, and sex to match the county. "

~~~
gryson
On the contrary - detecting selection bias of this sort can be very difficult.
Do you measure it using a questionnaire? You might ask your subjects "Do you
think you previously had COVID-19?" But what do you do with that response
data? Exclude everyone that says yes? Then you're skewing one way. There's
obviously no population data to match that response with like there is for
general demographics such as race and age.

Many studies are only as good as their sampling methods. A good sampling
method will alleviate concerns of selection bias.

~~~
JamesBarney
I think you'd call individuals(using a method with far less self selection
bias) and get them to fill out the same questionnaire and then do the same
types of adjustments you'd do with race or gender.

But I also agree they 100% did not do any of these adjustments if they did it
would be listed right here

> We report the prevalence of antibodies to SARS-CoV-2 in a sample of 3,330
> people, adjusting for zip code, sex, and race/ethnicity. We also adjust for
> test performance characteristics using 3 different estimates: (i) the test
> manufacturer's data, (ii) a sample of 37 positive and 30 negative controls
> tested at Stanford, and (iii) a combination of both.

------
erentz
It’s hacky and I’m happy to be told how wrong it is. But based on these recent
antibody studies in Germany, Finland, and now here in CA, I’ve been assuming
an actual fatality rate of about 0.4, and thus an actual infected rate of 250x
our known deaths, which is a much firmer number.

This is only a small comfort since it means we _may_ have had about 8.75
million infected and presumably now immune in the USA. Or about 2.6%. There’s
still a long way to go in that case.

~~~
MiguelVieira
I've been using 0.5% for mental math, rounding down from the lower bound from
this study in The Lancet:

[https://www.thelancet.com/journals/laninf/article/PIIS1473-3...](https://www.thelancet.com/journals/laninf/article/PIIS1473-3099\(20\)30243-7/fulltext)

I would be surprised if, after this current wave of infections, the percentage
of people with antibodies in the US is higher than the low single digits.

Herd immunity without a vaccine is a pipe dream. Our best bet is to massively
ramp up testing and contact tracing and really start pushing the number of
infections down to a point where parts of society can start functioning again.

~~~
contravariant
Herd immunity isn't a pipe dream it is just one hell of a sacrifice. And we'd
better check the immunity actually lasts before we try that. Frankly I've
never been a great fan of the 'flatten the curve' explanation, sure the graphs
seem nice but I reckon the peak in those graphs is drawn too low by several
orders of magnitude.

In the Netherlands a fatality rate of 0.5% would mean it'd take about a year
for herd immunity to kick in assuming we managed sustain the peak that
occurred about 2 weeks ago (and it's somewhat dubious whether the healthcare
system actually _can_ sustain that peak).

~~~
Jeema101
One aspect that people seem to be ignoring with the whole 'healthcare
capacity' argument is the mental health of the healthcare workers. The
capacity everyone talks about is IMO the acute capacity, not really the
chronic sustained capacity.

That's because just like in a war, it isn't realistic to expect the frontline
soldiers to stay in combat for longer than weeks at a time before they start
to exhibit PTSD and their effectiveness begins to drop.

Unless we have large reserves of doctors and nurses to rotate in and out I
think we should be very careful not to take them for granted.

------
tgb
I made this comment the last time a serological testing study came out: they
have 30 negative controls and claim 1.5% positives among their tests. So the
number of controls is insufficient to rule out this being entirely false
positives. You have to then rely on the test manufacturer's claims of false
positives rates, which comes from 371 negative controls. They say:

> our estimates of specificity are 99.5% (95 CI 98.1-99.9%) and 100% (95 CI
> 90.5-100%)

Look at the those confidence intervals, even the narrower one (from the
manufacturer's data)! The bottom is a four-fold increase in false positives,
compared to the point-estimate, and is greater than the total positives they
had in their finding.

~~~
ajross
To repeat you in plainer English: the serum test isn't infallible and reports
"infected", incorrectly, for uninfected patients. It does this at some rate
that is very near, or perhaps larger than, the fraction of "true" infected
patients they report in their results.

Teasing out data from all that noise requires that the false positive error
rate be measured very accurately. And they didn't do that, so really this
doesn't tell us much.

Edit: Alternatively, borrowing jargon from a more common field around here:
the measured infection rate of ~3% is very close to the noise floor of the
experiment. It might be that, or it might be near zero, and we can't tell the
difference. This study is very good evidence that the infection fraction is
not much larger, however. We can easily rule out high infection rates like the
30% numbers that seems to get thrown around.

------
virusduck
I don't see anything in the methods about potential crossreactivity with NL63,
OC43, or HKU1 coronaviruses. _Presumably_ this was done by the company, but
crossreactivity is an extremely important control when it comes to this type
of test. It is common, and if it's not vetted thoroughly, you may be measuring
something else completely.

------
standardUser
Add this to growing pile of evidence that this coronavirus is more widespread
than most testing to date would suggest. How much more widespread still
appears very unclear. There is a major ramping up of antibody testing
happening right now, and multiple new tests being introduced, so we should
have a better idea very soon.

But the takeaway should be that this is a good thing. Every unknown case we
uncover decreases the overall rate of hospitalization and death, and
potentially decreases the effective transmission rate (assuming most people
who had the virus develop immunity and that the immunity lasts, which is also
still unclear).

~~~
rumanator
I don't follow your argument. If the epidemic is more widespread than
initially thought, why do you assume that the effective transmission rate
would potentially decrease? If anything, the transmission rate was higher than
assumed and, due to the higher number of infections and how they spread
exponentially,it would only ramp up.

And additionally, there are reports of reinfections among covid19 patients who
were found to be cured.

~~~
timr
_" If the epidemic is more widespread than initially thought, why do you
assume that the effective transmission rate would potentially decrease?"_

Herd immunity. If testing is only detecting 1/50th of cases in NYC, for
example, it means that there have been about 6M cases, or about 69% of NYC's
population. That's basically the threshold where we'd expect to see the
infection counts level off naturally.

~~~
cdelsolar
there is no herd immunity if reinfection can occur, which appears to be the
case

~~~
timr
There is no credible scientific evidence that reinfection can occur. There
have been, at most, anecdotal reports from unreliable sources.

~~~
SpicyLemonZest
And there are good reasons to believe reinfection shouldn't be possible, to be
clear - it's not just being dismissed out of hand.

------
DennisP
The problem with every one of these studies I've seen is that they come up
with around 3% of the population having antibodies, while a typical false-
positive rate for these tests is also around 3%. In this case they claim they
adjust for the specificity (basically false-positive rate), but I didn't see
that they reported what the specificity was.

If the mortality really is very low, then it should be easy to prove. Just go
to NYC, do some random testing, and show us an infection rate that far exceeds
the false-positive rate.

~~~
CubsFan1060
This is certainly not a study. And certainly shouldn't be taken as good data
of any kind, but it is interesting: [https://www.livescience.com/coronavirus-
in-pregnant-woman-hi...](https://www.livescience.com/coronavirus-in-pregnant-
woman-high-nyc.html)

Sure as hell isn't random (all pregnant women... so all women for one. All at
the same hospital is another).

"Between March 22 and April 4, those hospitals screened 215 pregnant women for
SARS-CoV-2 (the virus that causes COVID-19), and 33 women, or 15%, tested
positive. Of these who tested positive, 29 women — or nearly 14% — showed no
symptoms."

~~~
eightysixfour
Anyone know if there's been a follow-up to see if the women eventually
developed symptoms?

------
opportune
I don't see anything about sampling bias here. I would assume that people who
have been ill, experienced mild symptoms, or who were exposed to confirmed
cases would be more likely to respond to the facebook ads recruiting testing
volunteers.

I'm sure we are significantly undercounting cases but I highly doubt we are
off by a magnitude of 50x.

~~~
brink
I know. 50-85x is hard to believe. How could we be _that_ far off?

~~~
usaar333
It's impossible to believe. Currently, Santa Clara's crude CFR is 3.7%.

NYC has similar demographics (and as bad nursing home hits?) -- 0.14% of the
population has died from covid. That puts an upper bound of 26x (and that's if
the entire population was infected)

~~~
irq11
You have no reason to believe that the Santa Clara CFR is correct, and
dividing the CFR of one city by the IFR of another is meaningless.

------
chris_va
With only 30 people in the negative control group, the confidence interval on
the false positive rate would easily swamp out the results of the study for
1.5% positive sample ratio, no?

Seems kind of useless unless your control group is a lot larger, and the false
positive rate can be shown to be <<1.5%.

------
robotcookies
"Participants were recruited using Facebook ads targeting a representative
sample of the county by demographic and geographic characteristics."

How is it representative when it's only Facebook users in this sample?

~~~
usaar333
Unless you think FB users for some reason would be infected at significantly
different rates, that shouldn't be much of a problem.

I'm much more concerned about selection bias toward prior sick people; IIRC,
Stanford offered to report positive results to the patient.

~~~
geoelectric
There would potentially be an age bias, though probably less in FB than a lot
of other services you could cherrypick from. It'd also arguably bias older,
since FB has been aging up in audience from what I can tell.

Without reading the study, though, possible they actually controlled that in
the demographic profile for the ad.

Edit:

Going to the other thread confirmed this. From the paper,

"This study had several limitations. First, our sampling strategy selected for
members of Santa Clara County with access to Facebook and a car to attend
drive-through testing sites. This resulted in an over-representation of white
women between the ages of 19 and 64, and an under-representation of Hispanic
and Asian populations, relative to our community. Those imbalances were partly
addressed by weighting our sample population by zip code, race, and sex to
match the county. We did not account for age imbalance in our sample, and
could not ascertain representativeness of SARS-CoV-2 antibodies in homeless
populations. Other biases, such as bias favoring individuals in good health
capable of attending our testing sites, or bias favoring those with prior
COVID-like illnesses seeking antibody confirmation are also possible. The
overall effect of such biases is hard to ascertain."

------
mgreg
There's also some data coming out of Wuhan around antibody testing. The
methodology is not as well formed as the study in the OP but there is still
value in the data.

> Wuhan’s Zhongnan Hospital found that 2.4% of its employees and 2% to 3% of
> recent patients and other visitors, including people tested before returning
> to work, had developed antibodies, according to senior doctors there.

Additional antibody testing is also underway in Wuhan.

Source: [https://www.wsj.com/articles/wuhan-starts-testing-to-
determi...](https://www.wsj.com/articles/wuhan-starts-testing-to-determine-
level-of-immunity-from-coronavirus-11587039175)

~~~
usaar333
It is far more credible that Wuhan hit 3% infection rate (that's about double
the PCR testing rate of the evacuees) than Santa Clara county, with an order
of magnitude lower death count, having a 2% rate.

------
h2odragon
I think the numbers will coma out to show this ripped through the USA in
February; we slammed the barn doors closed well after the horse was gone.

Of course that's not the narrative that fits agendas so just like with "why
cant we test people?" back then, there will be many official reasons not to
look for antibody data and to doubt what data gets gathered.

~~~
pwned1
Based on the difference between CA and NY, I'd be willing to bet that it got
here earlier, maybe in December.

~~~
greedo
You would have seen more reports in hospitals of interstitial pneumonia, as
well as a higher YoY death rate during January.

~~~
hedora
The CDC is reporting an abnormally high pneumonia death rate in January, and
it can’t be explained by confirmed flu cases:

[https://www.cdc.gov/flu/weekly/#S6](https://www.cdc.gov/flu/weekly/#S6)

Look carefully at the black and red graph after the phrase “mortality
surveillance data”.

~~~
greedo
The graph and text clearly state it refers to pneumonia and influenza, if you
refer to the legend where it says Percent P&I. The P&I for the first 8 weeks
of the year is clearly within normal ranges. It spikes from baseline in the
first week of March continuing on to the present day.

If you look at the first set of graphs, you'll see a spike around the same
time for influenza tests. I don't know how fast pneumonia occurs in people
weakened by influenza, but I would expect some delay.

I think the spike in both influenza and in death due to influenza and
pneumonia is probably due to testing. If you presented with influenza like
symptoms in March, you were definitely given a flu test to rule out influenza.

------
terramars
my friend probably had the virus (2 inconclusive tests) and tried to get into
this study. some group asked her for $125 to get tested. i'm not sure if she
saw any of the facebook ads. so, grain of salt about representativeness of the
sample. there's for sure a response bias towards people who think they had it.

~~~
jhwang5
Just curious, did your friend have pretty severe symptoms? As in, was it worse
perceivably than say a case of bad flu?

------
fspeech
There could be a meaningful distinction between exposure and infection. I
would define the first as anyone who was exposed to the virus (or even just
parts of the virus) and generated antibodies. The second would be an actual
infection and presumably a sufficient antibody response to repel the virus and
confer immunity to another infection. Really you can generate antibodies
without even getting exposed to live virus, just like you can develop allergy
to pollen or other foreign objects that enter your body. I would say only the
second case is meaningful for public health. For that you need to look at how
people do convalescent plasma. You need to test for specific antibodies that
actually confer immunity.

------
georgewfraser
There are now multiple sources of evidence pointing to a very low true
infection fatality rate, as low as 0.1% in some regions. Iceland has been
testing a semi-random sample using PCR since early in the epidemic, and there
have been several small antibody surveys in “hot spots” showing infection
rates over 15%. You can also use CDC surveillance of flu-like illnesses to
estimate the total number of infected nationwide; this morning I posted an
analysis of this data estimating only 1 in 40 cases were detected in March,
consistent with the Stanford results:
[https://fivetran.com/blog/covid-19-count](https://fivetran.com/blog/covid-19-count)

~~~
projektfu
It may have a low overall fatality rate, yet it seems to have a high rate of
hospitalization in clinical cases.

~~~
grandmczeb
> it seems to have a high rate of hospitalization in clinical cases.

The raw hospitalization rate is inflated because both treatment and testing
are mostly limited to very severe cases in places where there's a lot of
confirmed cases (e.g. NYC.)

~~~
projektfu
Still, comparing it to influenza... we dont see this many severe cases
typically.

~~~
grandmczeb
Its pretty obvious this is worse than the flu. I don't think anyone is really
disputing that at this point.

------
tomerico
Saw this comment [1] on Reddit that outlines the limitations of the study
well:

This is the most poorly-designed serosurvey we've seen yet, frankly. It
advertised on Facebook asking for people who wanted antibody testing. This has
an enormous potential effect on the sample - I'm so much more likely to take
the time to get tested if I think it will benefit me, and It's most likely to
benefit me if I'm more likely to have had COVID. An opt-in design with a low
response rate has huge potential to bias results.

Sample bias (in the other direction) is the reason that the NIH has not yet
released serosurvey results from Washington:

We’re cautious because blood donors are not a representative sample. They are
asymptomatic, afebrile people [without a fever]. We have a “healthy donor
effect.” The donor-based incidence data could lag behind population incidence
by a month or 2 because of this bias.

Presumably, they rightly fear that, with such a high level of uncertainty,
bias could lead to bad policy and would negatively impact public health. I'm
certain that these data are informing policy decisions at the national level,
but they haven't released them out of an abundance of caution. Those
conducting this study would have done well to adopt that same caution.

If you read closely on the validation of the test, the study did barely any
independent validation to determine specificity/sensitivity - only 30! pre-
covid samples tested independently of the manufacturer. Given the performance
of other commercial tests and the dependence of specificity on cross-
reactivity + antibody prevalence in the population, this strikes me as
extremely irresponsible.

This paper elides the fact that other rigorous serosurveys are neither
consistent with this level of underascertainment nor the IFR this paper
proposes. Many of you are familiar with the Gangelt study, which I have
criticized. Nevertheless, it is an order of magnitude more trustworthy than
this paper (both insofar as it sampled a larger slice of the population and
had a much much higher response rate). It also inferred a much higher fatality
rate of 0.37%. IFR will, of course, vary from population to population, and so
will ascertainment rate. Nevertheless, the range proposed here strains
credibility, considering the study's flaws. 0.13% of NYC's population has
already died, and the paths of other countries suggest a slow decline in daily
deaths, not a quick one. Considering that herd immunity predicts transmission
to stop at 50-70% prevalence, this is baldly inconsistent with this study's
findings.

For all of the above reasons, I hope people making personal and public health
decisions wait for rigorous results from the NIH and other organizations and
understand that skepticism of this result is warranted. I also hope that the
media reports responsibly on this study and its limitations and speaks with
other experts before doing so.

[1]
[https://www.reddit.com/r/COVID19/comments/g32wjh/covid19_ant...](https://www.reddit.com/r/COVID19/comments/g32wjh/covid19_antibody_seroprevalence_in_santa_clara/fnotu78/)

~~~
vl
It’s better to have data and know its limitations, than not to have data at
all.

~~~
jhayward
This may be a true statement when applied to public health professionals, but
is quite likely dangerous to public welfare when in the hands of
disinformation campaigns and propaganda.

------
beamatronic
“These prevalence estimates represent a range between 48,000 and 81,000 people
infected in Santa Clara County by early April, 50-85-fold more than the number
of confirmed cases. Conclusions The population prevalence of SARS-CoV-2
antibodies in Santa Clara County implies that the infection is much more
widespread than indicated by the number of confirmed cases.”

------
cbare
This thread critiques this article in a way I find convincing:
[https://twitter.com/DiseaseEcology/status/125122527387113472...](https://twitter.com/DiseaseEcology/status/1251225273871134721)

------
ludwigschubert
Great to see actual testing on a representative population sample, though I’m
not sure what the consequences ought to be:

> These prevalence estimates represent a range between 48,000 and 81,000
> people infected in Santa Clara County by early April, 50-85-fold more than
> the number of confirmed cases.

~~~
devit
So with 69 deaths so far and 9 deaths last week in Santa Clara County,
projecting that to an extra 4 weeks to 105 deaths for those infected in early
April, that means a 0.16% death rate?

~~~
femto113
Lots of reasons I don't think you can extrapolate meaningful death rates from
this yet. One big one is that time from infection to death covers a very wide
range: in Wuhan time from hospitalization to death had both a mean and a
standard deviation of about 2 weeks, and time from infection to
hospitalization isn't well known yet, but is likely at least a week. Add to
that the fact that some cases are diagnosed post-mortem and you might not know
about all of the fatalities for early April cases until well into May.

------
BearOso
In our small town, I know of 3 people who came into direct contact with the
first confirmed case, exhibited symptoms, but were refused testing. I can
definitely believe their guess that the number of cases is an order of
magnitude greater than the number confirmed.

------
vikramkr
That is certainly alarming that it is so much more widespread than expected -
it really highlights the failure of our controls against the virus and the
importance of early testing. It I'd good news that this implies the death rate
is less severe.

However, a caveat is that antibody tests might have high rates of false
positives, which would be a particularly large problem when very few people
are actually positive. So read this with a grain of salt. The paper isn't peer
reviewed yet etc.

~~~
makomk
Most signs so far suggest that the virus is basically uncontainable, short of
everyone on the planet taking aggressive measures to stop it escaping China
back in January or December.

~~~
fspeech
How do you square that with the fact that there is no outbreak in any overseas
Chinese community? Not Taiwan or Hongkong or Macau, nor in the epicenter of
Italy ([https://www.reuters.com/article/us-health-coronavirus-
italy-...](https://www.reuters.com/article/us-health-coronavirus-italy-
chinese/from-zero-to-hero-italys-chinese-help-beat-coronavirus-
idUSKBN21I3I8)), nor San Francisco Chinatown
([https://www.nytimes.com/2020/04/17/us/san-francisco-
coronavi...](https://www.nytimes.com/2020/04/17/us/san-francisco-coronavirus-
chinese-hospital.html))? All of these communities have far more ties to
mainland China.

~~~
guscost
It could have already passed through all of those communities basically
unnoticed, and/or confused with other ILIs. There's no way to rule out that
possibility, and when you consider how rare cardiovascular diseases (the most
common comorbidity) are in those communities, you have to conclude that it's
not vanishingly unlikely:
[https://minorityhealth.hhs.gov/omh/browse.aspx?lvl=4&lvlid=4...](https://minorityhealth.hhs.gov/omh/browse.aspx?lvl=4&lvlid=49)

Obviously mitigation efforts still play some part in limiting the spread, but
I would not bet that all of those communities were able to suppress such a
contagious disease before a big chunk of the population was exposed to it.

~~~
fspeech
Maybe Chinese emigrants in Italy are young so they didn't notice the wave. But
Taiwan/Hongkong/Macau? They are testing and tracing. When their recent
travelers to Europe and America came back, a lot of them were infected and
caused a spike in cases.
[https://www.nytimes.com/interactive/2020/04/09/world/asia/co...](https://www.nytimes.com/interactive/2020/04/09/world/asia/coronavirus-
hong-kong-singapore-taiwan.html)

------
lmlsna
I suspected the asymptomatic rate to be high, but not by a factor of 50-85!
That's very great news no?

------
microdrum
This is very strong evidence that the "realists" like Alex Berensen were
correct. The shutdowns probably should end, while encouraging at-risk
populations (mostly retired) to stay at home.

~~~
jhayward
Data from Wuhan was that in the early phase of the epidemic about 70% of
infections were transmitted at home. Once they realized this, they went to
rapid isolation of positive tests and quarantine of contacts in “COVID hotels”
away from home.

So your suggestion is entirely unworkable unless there is a plan to remove all
at-risk persons from contact with low-risk persons.

Please show your work and let us know what that plan looks like. Who will care
for these people, how will they be housed, how will you prevent the care force
from bringing infection to them ala nursing homes, etc.

~~~
microdrum
Guess what? We have been doing it your way for 45 days. Everyone infected is
now locked in a house with others. They too are now infected. Intrafamilial
and nosocomial infection is much more powerful than community -- that has been
proven here and in earlier epidemics.

And yet anti-science morons in SF clutch their scarf over their mouths when
you jog past them.

------
Medicalidiot
Antibody tests are not as accurate as RT-PCR, but the sensitivity that was
observed here was 91% and 99% specificity when compared to confirmed COVID
cases. I just want to point out that these researchers are using the corrects
tests to do this.

~~~
jacquesm
They use the correct tests, but they use them on a group that isn't even close
to a random sample of the population, which is what you'd need to do to get to
their conclusion.

~~~
Medicalidiot
I'm not going to comment on that because I have a basic understanding of
public health and epidemiology on the diagnostic side of things. Why do you
think that this isn't a "good" random sample?

~~~
mlyle
It's not a random sample; it's a convenience sample of people who responded to
Facebook ads.

~~~
Medicalidiot
What about Facebook ads doesn't mean that they're an adequate sample? Is there
a confounding factor that you have in mind?

~~~
mlyle
Many confounding factors. Socioeconomic factors that can't be fixed with
stratified sampling (access to transportation, usage of Facebook, etc..
conversely the willingness of a $10 amazon gift card to motivate people to
participate will vary). People who are concerned about possible past exposure
to COVID-19 may have a different probability to respond.

Of course, the bigger issue is that we can't rule out that the antibody tests
have a false positive rate that can explain the whole result. We need serology
tests in places with a higher positive rate to know.

Basically, 50 positive results out of 3330 is a really small signal. Just a
small false positive rate or sampling issue could explain all the positive
results.

