Examples of Pooling for COVID-19

Generally there are 3 kinds of reports on pooling for COVID-19:

General Report: These reports show that one or more positive samples can be pooled together and still recover a positive result. These papers are useful for establishing the limits of how large a pool can be (at least in the hands of that group), but don't actually carry out extenstive screening on patient samples.
Dorfman Screening These reports describe a Dorfman type, two stage screening protocol. In this protocol, samples are first pooled (ideally based on the prevalence of positive samples in the population). Pools that come back positive are then re-tested individually in a second round of testing. These kinds of screens are fairly common.
Combinatoric Screening These reports describe an Origami Assay type one stage screening protocol. In this protocol, samples are pooled in a structured way, and then all pools are tested. The combined pooling data then can be decoded to identify positive samples. These protocols are less common.

Below I divide reports into these three categories:

Combinatoric Screening

A strategy for finding people infected with SARS-CoV-2: optimizing pooled testing at low prevalence (Mutesa et al.): One of the first efforts I have seen to bring more sophisticated nonadaptive designs (hypercube) to COVID-19 screening. They describe their approach in a June 9 BBC interview describing using pools of 20 samples for large scale screening in Rwanda.
Efficient high throughput SARS-CoV-2 testing to detect asymptomatic carriers (Shental et al.): Describes using a nonadaptive group testing design for 384 patient samples using 48 tetss. They test 5 sets of 384 samples, each set containing between 1-5 positive carriers. They are able to recover all of the positive samples correctly.
Tapestry: A Single-Round Smart Pooling Technique for COVID-19 Testing (Ghosh et al.): Tapestry is a suite of pooling tools used to optimize COVID-19 testing that incorportes the cycle threshold data from the qPCR assay to improve their estiamtes of viral load. They also describe an Android app "BYOM Smart Testing" to help with pipetting steps.
Rapid, Large-Scale, and Effective Detection of COVID-19 Via Non-Adaptive Testing (Täufer): Describes using a shifted transversal design for combinatoric group testing, along with some very useful discussion on estimating the false positive rate for a given design.
Efficient high throughput SARS-CoV-2 testing to detect asymptomatic carriers (Shental et al.) Describes a nonadaptive screening approach they call P-BEST. They test this in a design with 384 samples into 48 pools. Each sample is distributed into 6 pools, making pools with 48 samples each (quite large). They decode their results using a compressed sensing type scheme called Gradient Projection for Sparse Reconstruction (GPSR). Their design is baesd on a Reed Solomon code and is described somewhat more here which was designed for 1024 samples and 48 pools, a pool size of 128 and each sample replicated in six pools.
EGROUP TESTING PERFORMANCE EVALUATION FOR SARS-COV-2 MASSIVE SCALE SCREENING AND TESTING (Nalbantoglu) Describes a nonadaptive screening approach also using P-BEST. They show that sparse random pools perform better than Reed Solomon or row-collumn designs. (I suspect OA designs would perform even better.)

General Report

LANCET: Pooling of samples for testing for SARS-CoV-2 in asymptomatic people (Lohse et al):Pools of up to 30 samples for COVID19 for qPCR asasys are detectable in under 30 Ct.
Testing pooled samples for COVID-19 helps Stanford researchers track early viral spread in Bay Area (Pinsky) Press release from Stanford showing that pooling 9 or 10 samples works with perfect accuracy in their hands.

Dorfman Screening

Assessment of Specimen Pooling to Conserve SARS CoV-2 Testing Resources (Abdalhamid et al.) One of the earliest examples in the SARS-CoV-2 pandemic of using Dorfman screening with pools of 5 samples. This work was done at the University of Nebraska in response to a shortage of test reagents.
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IEEE: Everybody in the Pool: Researchers Use Algorithms to Tackle the Coronavirus Test Shortage A Discussion wiht Dror Baron at NC State University on using group testing for COVID19.
Analysis and Applications of Adaptive Group Testing Methods for COVID-19 (Mentus, Romeo, and DiPaola): Calculations for how adaptive group testing could be used for COVID19 screening, with a focus on nursing homes, prisons, and ships. They compare multi-step adaptive designs ("Divide and test" and "Hwang's generalized binary splitting") to the standard two stage Dorfman design and find that for small sample numbers (n=100-1000) the multi-step methods perform better.
UK DHSC: Pooled samples staff monitoring in Covid-19 care areas as part of Triple lock - starve the virus (Moses):Reports efforts to test up to 10 pooled samples for group testing of medical staff.
PCR Pooling Commecial site offering group testing designs and decoding for up to 8 fold compression for COVID91 tests. Note that this platform is using a multi-stage algorithm for testing.
Large-scale implementation of pooled RNA extraction and RT-PCR for SARS-CoV-2 detection (Ben-Ami et al.): Tested using a dorfman type screen with pools of 8. After validation they used a Dorfman strategy to test 26,576 samples (prevalence 0.12%), and achieved a 7.3 fold increase in throughput.