
Estimating unobserved SARS-CoV-2 infections in the United States - krjachkov
https://www.pnas.org/content/early/2020/08/20/2005476117
======
lettergram
It seems cases and pandemic related deaths are likely dramatically under
reported and detected:

[https://austingwalters.com/u-s-covid19-less-tests-more-
death...](https://austingwalters.com/u-s-covid19-less-tests-more-deaths-no-
end-in-sight/)

Real deaths seem to be closer to 250-300k at this point (whereas officially
it’s 170k-180k).

Also testing is down for those curious... about 25% down from a month ago.

 _edit_ : added “pandemic related deaths” - not all deaths are necessarily
covid, but could be from lack of healthcare availability, etc.

~~~
jliptzin
I know for a fact deaths are dramatically under reported (at least in FL)
because I have relatives that died of COVID and the nursing homes put
congestive heart failure and respiratory failure as cause of death and not
COVID. After receiving positive COVID test results...

~~~
kspacewalk2
It's much less clear cut than that. In fact death with COVID ≠ death from
COVID every single time, even for elderly people.

~~~
tomarr
Well sure, and that is a perfectly valid comment at the micro level. However,
we can relatively clearly see that the COVID-linked death reporting is under-
reported from mortality baselines.

Therefore it's highly likely that for a given death it is more likely to be
incorrectly categorise as non-COVID when COVID was responsible, than to be
incorrectly designated COVID.

~~~
Jabbles
For contrast the UK overcounted its COVID deaths

[https://www.washingtonpost.com/world/britain-says-it-
overcou...](https://www.washingtonpost.com/world/britain-says-it-overcounted-
coronavirus-death-toll-
by-5377/2020/08/13/f6f171a6-dce0-11ea-b4f1-25b762cdbbf4_story.html)

~~~
thebruce87m
It overcounted some of the test based deaths, but in terms of excess mortality
there were probably still more covid deaths than have been reflected in test-
positive death numbers, so it’s still an undercount.

Here is some data here on excess deaths: [https://www.economist.com/graphic-
detail/2020/07/15/tracking...](https://www.economist.com/graphic-
detail/2020/07/15/tracking-covid-19-excess-deaths-across-countries)

------
dankle
> 108,689 (95% posterior predictive interval [95% PPI]: 1,023 to 14,182,310)

So between 1000 and 14 million. Got it.

~~~
jhfdbkofdcho
Not all of those numbers are equally likely though.

------
SpicyLemonZest
Interesting. An estimate that infections were _multiple_ orders of magnitude
higher than detected in mid-March is higher than I'd heard before, although I
suppose it's probably implied by the "basically everyone in New York caught
it" theory.

~~~
MengerSponge
As nice as it would be, seroprevalence studies have ruled out the "basically
everyone in New York caught it" theory.

~~~
jandrewrogers
A substantial percentage of infections are believed to not show up in
seroprevalence studies. One of the major areas of research right now is
determining how large this population is.

~~~
0xFFC
> A substantial percentage of infections are believed to not show up in
> seroprevalence studies

What is the reason behind this?

~~~
tripletao
The thresholds for antibody tests were established using known true negative
samples (e.g., blood banked before the pandemic) and known true positive
samples (e.g., patients who tested positive by PCR). But patients with worse
symptoms are over-represented among people who tested positive by PCR (since
they're more likely to seek a test), and patients with worse symptoms will
generally have higher levels of antibodies in the blood. So if anything, the
sensitivity of the test is probably an overestimate, which would make the
number infected an underestimate.

I've seen a few papers testing asymptomatic patients (identified by contact
tracing or other mass testing), with mixed results. NYC uses an in-house test
for which I don't believe any paper exists, so I don't think we can say
anything there. The IFR from NYC's serology is higher than most other
estimates, which could imply under-ascertainment but could also be real (e.g.,
because they forced nursing homes to accept positive patients, because they
were doing early intubation that we now know is harmful, etc.).

------
_Gyan_
Delhi, at least, appears to have around a 5% detection rate over the past
month.

There was a population-wide serosurvey conducted from Aug 1st to 7th, which
resulted in a 29.1% prevalence estimate, and an earlier one from June 27th to
July 10th, which resulted in a 22.8% estimate. Assuming a 2 week period for
IgG to be detected after infection, these surveys correspond to prevalence as
of 20th July and 20th June roughly. With a population of 20M, that's around
1.25M new infections in that period. The confirmed cases by PCR testing
increased by ~60000, yielding (roughly) a detection rate of 5%.

------
dehrmann
I've been looking at the infection rates in the US falling over the past few
weeks, and I'm wondering if it's because we achieved herd immunity for the
current r0, people changed their behavior when they saw cases rising, or fewer
people are getting tested because with the lag in processing time, a positive
result isn't actionable.

~~~
IAmGraydon
The way that the areas that are now most resilient are the same areas with
high population density which were hit very hard early on (NY, NJ, for
example) tells me that we have somehow hit herd immunity. There is some
evidence coming out that other coronaviruses (which cause the common cold)
could have causes immunity in some unknown percentage of the population,
meaning the percentage of SARS-CoV-2 infection required to reach herd immunity
is much lower than thought.

~~~
robbintt
Share the evidence or delete the comment as misinformation.

~~~
tripletao
Multiple papers (including one in Nature[1]) have reported T-cell immunity to
SARS-CoV-2 in something between a third and half the samples collected before
the pandemic. That doesn't mean the virus won't replicate in people with that
pre-existing immunity, and there's evidence from homeless shelters and such of
almost everyone testing positive (both by PCR and later for IgG in their
blood); but people with that T cell immunity very likely get less sick, and
might be less infectious (though near-certainly still infectious, and that's
more speculative).

And it seems from the hostility of your response ("delete the comment as
misinformation") that you find it incredible that herd immunity could exist
with less than 1 - 1/R0 of the population infected? But even ignoring any pre-
existing immunity, that calculation assumes a homogeneous and well-mixed
population. That's clearly not the case, since some people (a nurse in a
crowded ER, a police officer, a store clerk, a nightlife enthusiast, etc.)
have far more contacts than others (a remote worker who gets stuff delivered).
People with more contacts will get infected first, with disproportionate harm,
and then become immune first, with disproportionate benefit. Many papers have
modeled[2] this; though no one's found a great way to measure that
heterogeneity yet, so for now, it's hard to say much beyond that the effect
exists, and is potentially big.

And finally, herd immunity isn't a binary threshold, especially in a
heterogeneous population. As others have noted, even places without enough
immunity for R < 1 will still have slower spread than in a naive population,
or may get to R < 1 from the immunity plus slightly more cautious behavior.
Conversely, places that do have R < 1 overall may still have pockets of spread
in sub-populations with unusually high R0. In any case, it's no conspiracy
theory to believe that NYC developed significant amounts of immunity along the
way to its ~24k (about 0.3% of the population!) deaths.

1\.
[https://www.nature.com/articles/s41586-020-2550-z](https://www.nature.com/articles/s41586-020-2550-z)

2\.
[https://www.medrxiv.org/content/10.1101/2020.02.10.20021725v...](https://www.medrxiv.org/content/10.1101/2020.02.10.20021725v2.full.pdf)

------
kgin
I seem to be hitting trying to read this. Can someone help summarize the
results of this analysis?

~~~
SpicyLemonZest
The core result is

> Simulating from 1 January, we obtained 108,689 (95% PPI: 1,023 to
> 14,182,310) local infections cumulatively in the United States by 12 March
> (Fig. 1A).

What that means is that they don't really know - that confidence interval is
absurd - but they have reason to think there could have been 100k Americans
with the coronavirus on March 12.

~~~
air7
Results with such confidence intervals should not be allowed to be published.

As if we don't have enough fake news/hype going around.

~~~
hodgesrm
I don't understand the comments on this thread that seem to want to suppress
preliminary data about COVID.

Sure, the confidence interval is ridiculously large but the paper is open
about its methods and conclusions. It's definitely in the "more work is
needed" category, but I don't see how this information should not be
published.

