AC-DC: Amplification Curve Diagnostics for Covid-19 Group Testing. Gabrys, R., Pattabiraman, S., Rana, V., Ribeiro, J., Cheraghchi, M., Guruswami, V., & Milenkovic, O. 2020. arXiv:2011.05223Paper abstract bibtex The first part of the paper presents a review of the gold-standard testing protocol for Covid-19, real-time, reverse transcriptase PCR, and its properties and associated measurement data such as amplification curves that can guide the development of appropriate and accurate adaptive group testing protocols. The second part of the paper is concerned with examining various off-the-shelf group testing methods for Covid-19 and identifying their strengths and weaknesses for the application at hand. The third part of the paper contains a collection of new analytical results for adaptive semiquantitative group testing with probabilistic and combinatorial priors, including performance bounds, algorithmic solutions, and noisy testing protocols. The probabilistic setting is of special importance as it is designed to be simple to implement by nonexperts and handle heavy hitters. The worst-case paradigm extends and improves upon prior work on semiquantitative group testing with and without specialized PCR noise models.
@UNPUBLISHED{ref:GPRRCGM20,
author = {Ryan Gabrys and Srilakshmi Pattabiraman and Vishal
Rana and Jo\~{a}o Ribeiro and Mahdi Cheraghchi and
Venkatesan Guruswami and Olgica Milenkovic},
title = {{AC-DC}: Amplification Curve Diagnostics for
{Covid-19} Group Testing},
year = {2020},
eprint = {2011.05223},
archivePrefix ={arXiv},
primaryClass = {q-bio.QM},
note = {arXiv:2011.05223},
url_Paper = {https://arxiv.org/abs/2011.05223},
abstract = {The first part of the paper presents a review of the
gold-standard testing protocol for Covid-19,
real-time, reverse transcriptase PCR, and its
properties and associated measurement data such as
amplification curves that can guide the development
of appropriate and accurate adaptive group testing
protocols. The second part of the paper is concerned
with examining various off-the-shelf group testing
methods for Covid-19 and identifying their strengths
and weaknesses for the application at hand. The
third part of the paper contains a collection of new
analytical results for adaptive semiquantitative
group testing with probabilistic and combinatorial
priors, including performance bounds, algorithmic
solutions, and noisy testing protocols. The
probabilistic setting is of special importance as it
is designed to be simple to implement by nonexperts
and handle heavy hitters. The worst-case paradigm
extends and improves upon prior work on
semiquantitative group testing with and without
specialized PCR noise models.}
}
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