Laboratory 10 More Efficient Covid Testing

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Computer Science CS134
Laboratory 10
More Efficient Covid Testing
Objective: Learn how to conduct simulations of random events.
In this lab 1
, we will be exploring ways to improve the cost-effectiveness of covid PCR2
testing. PCR
tests duplicate the part of a viral RNA  bookended between two \primers” to make . (This sort of
copying from a template is similar to how our DNA is copied when cells divide.) In each PCR round,
the double strands are separated and become templates to make another copy (each  makes a , and
each  makes a ). This ampli cation process produces an exponential growth that allows detection of
whether a particular sequence was present in the sample.
round  
0 1 0
1 1 1
2 2 2
3 4 4
4 8 8
n 2n−1 2
n−1
There are typically 30 or 40 rounds of ampli cation so 1 virus molecule can create billions of this fragment,
which is sucient to detect. PCR is exquisitely sensitive, so there are very few False Negatives (samples
with virus that do not produce a signal) and very few False Positives (samples with no virus that produce
a signal). The reason people are not PCR tested post-infection is that with such a sensitive test, subinfectious levels of virus (while the patient’s immune system eliminates the last remnants) can still be
detected.
Although PCR is very e ective, there is a cost and eciency tradeo ; we can run one test per sample,
but that can be expensive. On the other hand, we can combine multiple samples into a single test, but
knowing which sample is positive can be challenging.
In this lab, we will investigate three possible testing protocols with di erent ways of handling this
tradeo . To understand these methods, we need to consider two values: the probability p, representing
the fraction of positive tests, and the size of a batch S, representing the number of samples (one sample
represents one person) in a given test batch. For any given batch, the number of positive tests will be
some integer close to the expected number, p*S.
\Diagnostic testing” is performed when there is reason to suspect an individual is infected, p=0.03=3% in
Berkshire County. \Screening testing” is performed even when there is no known exposure, p=0.0002=0.02%
at Williams in Fall 2020.
Here are the three possible testing protocols that we will compare:
1. Basic. One sample per PCR test.
For example, if p=1% on 384 samples, then about 4 tests are positive (infectious people) and most
tests are negative (healthy people).
1
Inspired by https://www.nytimes.com/2020/08/21/health/fast-coronavirus-testing-israel.html
2. The Dorfman Method:
(a) Pool together part of s samples into one vial for PCR testing. In round one, the number of
vials v=S/s.
(b) Each uninfected vial clears s healthy individuals; but for any vial that tests positive, each of
those s samples must be retested to  nd the infected individuals.
For example, S=384 people could be tested with s=16 samples in each of 24 vials, or s=8 samples in
48 vials, or s=4 samples in 96 vials.
Some fraction of these samples will need to be retested. We will conduct a simulation to compare
the expected expense for these di erent s values as a function of the test positivity rate p.
Other logistical considerations with the Dorfman method: samples must be retained until the  rst
round of testing is completed. Because it requires a second round of testing, the Dorfman method
takes more time to identify the sick individuals.
3. The P-Best method of Shental et al.3
:
(a) For each of the 384 samples, robots pipette a bit into that sample’s set of v=6 of 48 vials.
(b) In the end, there are n=48 samples in each of the 48 vials, but the mapping of samples to vials
creates minimal sample-to-sample  ngerprint overlap.
If only one individual in the batch has the virus, v vials will be positive, and the patient with the
matching pattern can be identi ed. There will be minimal overlap with the patterns of the other
individuals. If two individuals in the batch have virus, then ≤2v vials will be positive.
Getting Started. Clone the lab resources from the gitlab repository, as usual:
git clone https://evolene.cs.williams.edu/cs134-labs/22xyz3/lab10.git ~/cs134/lab10
where your CS username replaces 22xyz3. You will the  nd Python  les basic.py, dorfman.py, pbest.py,
and the data  le pooling384-48-by-sample.txt.
Required tasks (8 points for Basic+Dorfman, 2 points for PBest).
We will be making plots of number of PCR tests performed (which is proportional to the expense) to
test 384 individuals with the three di erent methods. We will also estimate the number of False Positives.
1. We begin by simulating the basic PCR testing approach.
(a) Complete basic(p,samples=384) to use random to simulate whether each of the 384 samples
is positive or negative, and to return the total number of positives for probability p.
(b) Complete simBasic(p, samples=384, numTrials) to repeat the basic simulation over 1000
batches to get reasonable statistics. This function should return a list with the total positive
tests from each batch to make a histogram plot to compare these three probabilities.
3
(c) Run the code to make a histogram plot of the number of infected individuals observed in a
given run of 384 samples, with test-positivity rates of p of 0.02%, 0.2%, 2%. (With p=0.02=
2%, the mean number is about 7.68, so in a given batch, you should get an integer near that
value.) Answer the questions in the docstring at the top of the  le.
2. With the Dorfman method, we will look at three variants (s=16 samples in each of v=24 vials, s=8
samples in v=48 vials, or s=4 samples in v=96 vials).
(a) Complete nDorfman(p,s,v) that simulates a batch with probability p, and computes how many
total PCR tests are required for that batch. Recall that for a given batch, the  rst-round vials
with infected samples will need samples to be retested in the second-round.
(b) Repeat the simulations for 1000 batches for pvals = [0.02*(2**i) for i in range(-5,3)]
and (s,v) in [(16,24), (8,48), (4,96)] to  nd the mean number of PCR tests for each.
Plot this data, with a line for each of the three (s,v) versions with p on the x-axis. Which
(s,v) combination is most cost e ective? Where would Basic and P-Best costs be on the plot?
(c) Make a histogram of the number of tests for (16,24) and (4,96) at p=0.02. What do you
3. In Shental’s P-Best protocol with 384 samples, 48 vials, and v=6 vials per person there are 384·383/2
pairs of individuals.
(a) Complete overlaps(pools) that evaluates how many pairs of individuals have zero vials in
common? only one vial that overlaps? two vials? three vials? in a P-Best pool design  le
(e.g. pooling384-48-by-sample.txt). Put answers in the docstring.
(b) With P-Best protocol, the issue is whether the infected samples can be correctly identi ed. We
will plot performance for p in pvals = [0.001*i for i in range(0,16+1)].
Complete pBest(p, pools) that simulates one random batch. Samples are randomly assigned
as infectious with probability p. For now, you may assume that if there is any infected sample
in a vial that it will give a positive test result. Retain information about which random samples
were sick and which vials give positive PCR results. Your function should return a tuple (TP,
FP, FN, TN). Here:
TP = True Positives = number of samples test sick and actually sick
FP = False Positives = number of samples test sick and actually healthy
FN = False Negatives = number of samples test healthy and actually sick
TN = True Negatives = number of samples test healthy and actually healthy
(c) Complete simulate(p, pools, numTrials) that simulates numTrials=100 random batches.
If desired, increase numTrials.
(d) The False Positive Rate = FPR = FP/(TP+FP+FN+TN) is the probability of false positives.
Plot FPR as a function of p for the Shental pool design.
At about what p are the average number of True Positives and False Positives equal?
Thought question: For high test probabilities the FPR becomes unreasonable with the original
Shental design. What happens to the FPR if you halve the number of samples in each batch from
384 to 192? (You would double the number of vials from 48 to 96 to still test 384 samples.)

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