The COVID-19 pandemic has strained testing capabilities worldwide.
There is an urgent need to find economical and scalable ways to test more people.
We present Tapestry, a novel quantitative nonadaptive pooling scheme to test many samples using only a few tests.
The underlying molecular diagnostic test is any real-time RT-PCR diagnostic panel approved for the detection
of the SARS-CoV-2 virus.
In cases where most samples are negative for the virus, Tapestry accurately identifies the status of each
individual sample with a single round of testing in fewer tests than simple two-round pooling.
We also present a companion Android application BYOM Smart Testing which guides users through the pipetting
steps required to perform the combinatorial pooling. The results of the pooled tests can be fed into the
application to recover the status and estimated viral load for each individual sample.

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This is an interface to test the performance of the group testing framework using inbuilt algorithms for
multiple prevalence (# of positive samples) values. It includes the option of first pre-processing COMP which
filters out sure negatives and then run the inbuilt algorithm for reduced sample set.
Different sets of matrices (varied "test vs sample" parameters) are used to run the tests.
The experiment runs multiple synthetic experiments by generating multiple instantiations of the viral load vector for
each algorithm, matrix pair.
The performance parameters precision, recall, specificity, sure positives, unsure positives and false positives
are provided after the code execution.

Please read the instructions below for using the form.

Instructions

Select different algorithms in Algorithmic section. For each algorithm, choice of using COMP for
pre-processing is given. For comparison, both can be selected.

Select the choices for matrices. For each matrix, range of prevalence (# of positive cases) value needs
to be submitted. This can be done by choosing values for start, steps and stop.
Default value for start, steps, stop are 1,1,7 respectively.
Please note that the matrix names is "row_size"x"column_size". The values for start and stop need to be
in the range 1 to row_size.

Number of experiments to be conducted for each matrix, algorithm pair can be provided in the number of
experiments dialog, with 20 used as default value.
Once, all data are input, press run code to run the experiments.

Warning

The runs will take time depending on number of algorithms, matrices, d range and number of expirements. Do not
close the browser during the run.

The l1ls_cv experiments take long time to run. Consider reducing the number of
matrices and experiments when using this algorithm.

The algorithm section in some browsers appears two labels together and the checkboxes appear together after that.