A Compressed Sensing Approach to Group-testing for COVID-19 Detection Article Swipe
Sabyasachi Ghosh
,
Rishi Agarwal
,
Mohammad Ali Rehan
,
Shreya Pathak
,
Pratyush Agrawal
,
Yash Gupta
,
Sarthak Consul
,
Nimay Gupta
,
Ritika Goyal
,
Ajit Rajwade
,
Manoj Gopalkrishnan
·
YOU?
·
· 2020
· Open Access
·
YOU?
·
· 2020
· Open Access
·
We propose Tapestry, a novel approach to pooled testing with application to COVID-19 testing with quantitative Polymerase Chain Reaction (PCR) that can result in shorter testing time and conservation of reagents and testing kits. Tapestry combines ideas from compressed sensing and combinatorial group testing with a novel noise model for PCR. Unlike Boolean group testing algorithms, the input is a quantitative readout from each test, and the output is a list of viral loads for each sample. While other pooling techniques require a second confirmatory assay, Tapestry obtains individual sample-level results in a single round of testing. When testing $n$ samples with $t$ tests, as many as $k= O(t / \log n)$ infected samples can be identified at clinically-acceptable false positive and false negative rates. This makes Tapestry viable even at prevalence rates as high as 10\%. Tapestry has been validated in simulations as well as in wet lab experiments with oligomers. Clinical trials with Covid-19 samples are underway. An accompanying Android application Byom Smart Testing which makes the Tapestry protocol straightforward to implement in testing centres is available for free download.
Related Topics
Concepts
Group testing
Pooling
Computer science
Test strategy
Protocol (science)
Coronavirus disease 2019 (COVID-19)
Sample (material)
Compressed sensing
Android (operating system)
Statistics
Algorithm
Mathematics
Artificial intelligence
Medicine
Combinatorics
Pathology
Operating system
Software
Chromatography
Chemistry
Alternative medicine
Disease
Infectious disease (medical specialty)
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://arxiv.org/abs/2005.07895v1
- OA Status
- green
- Cited By
- 28
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W3024371971
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3024371971Canonical identifier for this work in OpenAlex
- Title
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A Compressed Sensing Approach to Group-testing for COVID-19 DetectionWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-05-16Full publication date if available
- Authors
-
Sabyasachi Ghosh, Rishi Agarwal, Mohammad Ali Rehan, Shreya Pathak, Pratyush Agrawal, Yash Gupta, Sarthak Consul, Nimay Gupta, Ritika Goyal, Ajit Rajwade, Manoj GopalkrishnanList of authors in order
- Landing page
-
https://arxiv.org/abs/2005.07895v1Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/abs/2005.07895v1Direct OA link when available
- Concepts
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Group testing, Pooling, Computer science, Test strategy, Protocol (science), Coronavirus disease 2019 (COVID-19), Sample (material), Compressed sensing, Android (operating system), Statistics, Algorithm, Mathematics, Artificial intelligence, Medicine, Combinatorics, Pathology, Operating system, Software, Chromatography, Chemistry, Alternative medicine, Disease, Infectious disease (medical specialty)Top concepts (fields/topics) attached by OpenAlex
- Cited by
-
28Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 1, 2023: 1, 2022: 4, 2021: 12, 2020: 10Per-year citation counts (last 5 years)
- Related works (count)
-
20Other works algorithmically related by OpenAlex
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| publication_date | 2020-05-16 |
| publication_year | 2020 |
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