
Stanford Medicine COVID-19 test now in use - divbzero
https://med.stanford.edu/news/all-news/2020/03/stanford-medicine-COVID-19-test-now-in-use.html
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sebastianconcpt
_How test works

The Stanford test uses a technique called reverse-transcriptase polymerase
chain reaction, or RT-PCR, to rapidly identify the presence of viral RNA in
swabs from the noses of potentially infected people. RT-PCR uses short
stretches of DNA called primers that bind tightly and specifically only to
matching sequences in SARS-CoV-2 RNA. An enzyme called reverse transcriptase
then converts the viral RNA into complementary DNA, and as the reaction
continues, an enzyme called polymerase is used to generate billions of DNA
copies that can be detected by fluorescently tagged molecules called probes._

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semi-extrinsic
What I don't get from this description is hos they get the virus to release
the RNA?

I thought virii only release their RNA after entering a cell? Is there some
sort of artifical cell wall involved?

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hobofan
You can just destroy the virus, e.g. by grinding it down or via a certain
chemical and then you have a resulting crude mixture of RNA + other virus
parts. In the past you had to separate the RNA first, but with newer methods
you can do the PCR directly on the crude mixture.

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voidmain
Why can't RT-PCR tests be used to detect virus in a sample mixed from a large
number of individual samples? This would enable cheap and accurate mass
testing, since you can find P positives from S samples with O(P*log(S/P))
tests. What's the catch?

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mxwsn
Just curious, can you expand a bit on the analysis? I see that by binary
search you get O(log(S)) to identify a single positive, and naively rerunning
search from the beginning P times yields O(P*log(S)). One can certainly do
better by avoiding redundant tests but at some point one needs to start
reasoning about the distribution of the positives in the samples and I'm not
sure how to do that.

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mxwsn
Thought about it overnight and this is what I have:

To reason about the distribution of positives in the sample, we consider two
limiting cases, then interpolate between them. The cases are:

1\. All positives are clustered exactly together. We start with log(S/P)
queries to find the parent node of all positives, then test every node in the
subtree with 2P-1 queries to identify all positives. In total, this is O(p +
log(S/P)).

2\. All positives are uniformly distributed. By the pigeonhole principle, the
lowest level where every node tests positive must occur at log(P). Above this
level, every node tests positive, so we must perform 2P-1 tests. Every P nodes
on the level must be binary searched; each node has a subtree of height
log(S/P), for Plog(S/P) operations. In total, this is O(P + Plog(S/P)) =
O(Plog(S/P)).

It is not immediately obvious that the worst case distribution is either case
1 or 2. To address this, we define an interpolation variable K in [0, 1] which
describes a distribution where all positives begin clustered together, then KP
positives are adversarially moved elsewhere. We claim that K = 0 is case 1 and
K = 1 is case 2.

Consider the case where KP = 1. As in case 1, we need 2P-1 + log(S/P) tests to
fully identify the cluster; we also need a single binary search starting at
the 2nd level corresponding to the other half of S, which takes log(S) - 1
operations, for a total of 2P-1+log(S/P)+log(S)-1. As KP increases, the level
at which we transition from testing every node to initiating binary search on
a node will decrease; as an example, when KP = 4, we fully test halves, then
quarters, and then begin binary search. In general, we will need KPlog(S/KP)
queries for all binary searches. Finally, no matter what KP is, we need no
more than O(P) queries for subtrees where we need to test every node. This
yields O(P + KPlog(S/KP))

This allows us to navigate around an adversary choosing the worst distribution
of positives possible by taking the upper bound where K = 1, yielding O(P +
Plog(S/P)) = O(Plog(S/P)), though this bound is not expected to be tight.

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daemonk
Looks like it's a RT-PCR. How different are their primers compared to the IDT
sets ([https://www.idtdna.com/pages/landing/coronavirus-research-
re...](https://www.idtdna.com/pages/landing/coronavirus-research-reagents))?

There are also a couple of papers on biorxiv with primers for RT-LAMP tests,
which should be much faster, easier to perform than RT-PCR. And it requires
non-sophisticated equipment.

It all comes down to the false negative rates though.

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DataDrivenMD
To everyone questioning the false positive and false negative rates: your
point is well-taken, and it is the reason why it was necessary to push the FDA
to allow non-CDC labs to perform COVID-19 testing based on a fast-tracked
version of the clinical validation process.

To those that point out that there are newer/faster/better ways to actually
achieve the same results: you're right but that doesn't mean that the
machines, reagents, collection media, etc. are ready for prime time in this
type of setting. Remember: the entire process needs to be clinically validated
in order to earn the trust of doctors and public health officials who are
literally making life vs. death decisions.

TL;DR - Your points are valid, and there are real challenges that make certain
solutions impractical at this point in time. Consider this a call to action to
entrepreneurs who are serious about helping patients at home and around the
world.

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anaphor
Do we have any idea of what the false-positive rate of these types of tests
generally are? It sounds pretty thorough but some actual data on that would be
nice.

Also, given that the prevalence of the virus is still very low, I am assuming
(and the article seems to support this) that the test would only be used on
people that have some pre-existing risk factor, like exposure to a known
infected person, or travel to/from one of the high risk locations. Otherwise
(let's assume a FP rate of 1%) you would still be getting a large number of
false positives. Even with the assumption that there are a large number of
very mild undiagnosed cases.

The reason I bring this up is because I have seen many people calling for mass
testing of _everyone_ in the United States, regardless of symptoms or risk
factors. I think doing that would be a very big mistake because then you end
up with a few possible scenarios:

1\. You tell everyone who tested positive without any symptoms/high risk
factors that they probably don't have it actually.

2\. You end up treating massive amounts of people with potentially risky
treatments, or quarantine people for no reason.

3\. You have to do lengthy/expensive follow up tests of millions of people and
waste valuable time and resources.

I guess 1 isn't a big deal, but then how do you prioritize who to treat given
the symptoms alone don't provide a very useful criteria for judging who has
it?

Obviously the doctors and scientists working on this know all of this already,
but the general public and politicians seem to be demanding mass testing of
the entire population, which worries me.

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ISL
False negatives may actually pose a greater public-health problem. A suspected
case, assayed as a false negative can be released back into the community
without quarantine.

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lacker
Right now we are releasing everyone who has non-extreme flu symptoms without
testing whether it’s flu or covid-19, so even a small false negative rate
would be a great improvement.

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devy
RT-PCR is the typical way to test viral infection. Anyone knows if Stanford
Clinical Virology Laboratory has invented anything new or just a press release
to note that they are prepared to do SARS-CoV-2 viral testing since CDC has
finally authorized all private and university labs can perform tests just
recently?

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pkaye
The last two paragraphs mention that they look for two gene while the CDC test
looks for a different gene. So I don't think anything fundamentally new but
having multiple tests let them crosscheck each other.

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alyx
Is nobody else bothered by the logic in the 3rd paragraph?

> As with all currently available tests, it’s not yet clear how long a person
> needs to be infected before testing positive, or whether someone who's
> infected could be identified by the test before displaying symptoms.

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sliken
A recently saw a paper calling that COVID-19 was easy to detect early in the
course with an oral swap. But later in the disease course it was easier to
detect with a fecal test. Turns out a lung scan was more accurate than either.

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leemailll
Isn’t China using rtpcr for the test from the beginning?

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WrtCdEvrydy
Hug of death.

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ck2
I hope the publish their own (anonymized) stats and don't let the whitehouse
tamper with hiding numbers like they did with the CDC.

NY State has their own test too right? Are they going to publish stats?

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woliveirajr
> As with all currently available tests, it’s not yet clear how long a person
> needs to be infected before testing positive, or whether they could be
> identified by the test before displaying symptoms.

Kind of made me sad. We're celebrating that we are able to test something, in
very... precarious? .... results. Of course it's amazing that we're able to
known the genetic sequence in so few days. But as travels are much faster
around the globe, it takes so few days to spread. Everything being so fast
isn't exactly helpful to settle emotions.

