A recovery algorithm and pooling designs for one-stage noisy group testing under the probabilistic framework

DOI10.1007/978-3-030-74432-8_4zbMath1480.92127OpenAlexW3134607651MaRDI QIDQ2061995

Yining Liu, Itsik Pe'er, Sachin Kadyan

Publication date: 21 December 2021

Full work available at URL: https://doi.org/10.1101/2021.03.09.21253193

zbMATH Keywords

experimental designsnon-adaptive group testingCOVID-19

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Medical epidemiology (92C60)

Cites Work

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• Rapid, large-scale, and effective detection of COVID-19 via non-adaptive testing
• Nonadaptive Group Testing With Random Set of Defectives
• Noisy Non-Adaptive Group Testing: A (Near-)Definite Defectives Approach
• On the Optimality of the Kautz-Singleton Construction in Probabilistic Group Testing
• Group Testing: An Information Theory Perspective
• Boolean Compressed Sensing and Noisy Group Testing
• Nonrandom binary superimposed codes

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