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:2011.05223
AC-DC: Amplification Curve Diagnostics for Covid-19 Group Testing [link]Paper  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@UNPUBLISHED{ref:GPRRCGM20,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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