
Origami Assays - pwoolf
https://www.smarterbetter.design/origamiassays/default/index
======
pwoolf
I'm the creator of Origami Assays and am happy to answer any questions.

Screening for COVID19 is an urgent problem, but the infrastructure for running
these assays is limited. While there are important efforts underway to make
more tests available, one simple and low cost way to help is to use the assay
infrastructure we already have more efficiently.

The idea is that rather than run 1 assay on 1 patient sample, we intelligently
pool patients samples and run our limited assays on these pooled samples. It
is analogous to data compression, but for assays instead of files.

Nonadaptive pooling designs are well studied branch of applied mathematics and
engineering, and are well suited for COVID19 population screening for the
following reasons:

(1) Binary assay (2) Low positive rate (3) Large number of samples

These three features mean that the data stream coming from COVID19 assays is
nicely compressible.

I've put together a series of examples of nonadaptive pooling designs for
COVID19 that I'm calling "Origami Assays". These designs provide a few things:

* Concrete examples, with performance metrics for a range of sizes of designs. * Software infrastructure for decoding designs with error estimates. * A low cost, DIY paper template system for constructing complex pooling design mixtures by hand.

Advantages:

* Yields up to an 11.9x improvement in patient testing throughput (for the XL3 assay design). * Can be rolled out immediately, on any existing assay platform (RT-PCR, antibody, LAMP, or qSANGER) * Pool design can be done by hand without extensive training.

Disadvantages:

* Pooled designs can call false positives if too many positives are present in the population. * Pooling can dilute samples * Constructing pooling designs is a mind numbing task for humans.

I'm trying to roll out Origami Assays to all who may benefit from them. Any
questions, thoughts, or ideas are welcome!

~~~
tych0
Isn't the low positive rate kind of an assumption though, because of currently
limited testing? If this is much more widespread than we thought, will this
mechanism fall over?

~~~
pwoolf
The low positive rate is a constraint, but in practice we see that population
screens are yielding between 0.5% and 4% positive rate, depending on the
sampling population/scenario.

There are many use cases where we expect a low positive rate too. For example,
an employer screening what appear to be healthy employees.

A nice thing about these designs is that if they get overloaded, they call
false positives and the decoder can indicate when the design limits are
exceeded.

In this case, we would need to do a second round of testing for validation--
often on a small handful of cases.

~~~
tych0
Ok, basically a Bloom Filter for test results. Pretty cool :)

~~~
pwoolf
Yes, much like a Bloom filter, but instead of 2, it gives 3 output types:

1) not in set 2) possibly in set 3) in set

Depending on the input (sample population), it is possible to get results with
all 3 states. Origami Assay's decoder differentiates the "(3) in set" from
"(2) possibly in set" for efficient post-testing.

------
anticycle
Isn't the dilution of the virus a problem if most of the patients are
negative? You were using a test on a sample containing 100% virus positive
blood, but now you are using a test on a sample that contains x% virus
positive blood, with x potentially small

~~~
pwoolf
True, dilution is a challenge but the assays used for COVID19 detection
generally have a pretty wide dynamic range (3-5 orders of magnitude). The
multiplex designs in Origami Assays mix between 4 (for S3) and 36 (for XL3)
samples per well, depending on design. This means that samples are diluted by
a max of 1:36 in the largest XL3 design (2.8% original concentration) so the
assay used must be able to handle that.

Alternatively, samples may be concentrated depending on the specific assay.

------
luizfzs
The article says that this strategy is best fit for when most of the patients
are negative. I'd like to see how the number of false-positive change given a
change in the number of patients that test negative (increase and decrease).

I'd also be interested in a comparison of how reliable it is compared with the
current test strategy.

~~~
pwoolf
The interface has an encode/decode function that lets you explore different
scenarios. For example for a small design (S3):

[https://www.smarterbetter.design/origamiassays/default/encod...](https://www.smarterbetter.design/origamiassays/default/encode_decode_example?name=S3)

Reliability is an interesting question. For the monoplex case, we put all of
our eggs in one basket, so if we miss it once we miss it forever. In the
multiplex scheme, we have the possibility of recovery because we build in
multiple tests.

