Weiyu Xu - Colloquium Speaker

Associate Professor, Department of Electrical and Computer Engineering and the Applied Mathematical and Computational Sciences program, University of Iowa
Thursday, October 1, 2020 - 3:15pm
Colloquium Title: 
Compressed Sensing for High-Throughput and Low-Cost (COVID-19) Virus Testing: A Mathematical Perspective of Mass Testing


Large-scale, high-throughput and accurate COVID-19 virus testings are vital tools in the fight against the ongoing COVID-19 pandemic. Public health experts believe that mass virus testings are essential to stopping the spread of COVID-19 viruses and enabling a safe and fast transition to normal social life. However, often testing capacity is limited, and, in addition, there is often a shortage of (costly) reagents needed for performing tests.

In this talk, we will introduce a novel compressed sensing approach for performing high-throughput, low-cost and accurate COVID-19 testing. One simple method to increase the effective testing capacity is to perform testing on pooled samples of a number of subjects collectively instead of testing samples from each person individually. Pooled testing has been successfully used for infectious disease testing such as Human Immunodeficiency Virus in the past. While a simple version of this idea called group testing goes back many decades, in this talk, we develop a novel more powerful and general type of pooled testing based on the compressed sensing theory, which includes group testing as a special case. In virus testing, standard swab tests use the Reverse Transcription Polymerase Chain Reaction (PCR) process to selectively amplify DNA strands produced by viral RNA specific to COVID-19 viruses. The widely used quantitative PCR (qPCR) process allows not only binary detection (presence or absence) of a target RNA sequence, but also quantification of the RNA, producing estimates of the quantity of the RNA in test samples. This quantification can enable compressed sensing for pooled testing in virus testing, which can significantly increase test throughput, reduce the number of needed tests, reduce consumption of scarce reagents, and provide quantitative results robust against observation noises and outliers. Compared with group testing, compressed sensing can deal with higher prevalence, and further reduces the number of tests.  In this talk, we will introduce the system concept of using compressed sensing for virus testing, mathematical research problems behind it, and lab validation results. Our results have shown that the lab testing capacity can be increased by 10 times using compressed sensing in mass testing.

This is a joint work with Jirong Yi, Raghu Mudumbai, Xiaodong Wu, and collaborating virology lab researchers, supported by a National Science Foundation RAPID award and an IIAI grant.


Everyone is welcome to join! Please note that the meeting opens at 3:15pm, and the presentation is at 3:30-4:20pm. There will be time afterward for Q&A with the speaker.

Topic: Colloquia: Department of Statistics and Actuarial Science, The University of Iowa

Time: Oct 1, 2020 03:15 PM Central Time (US and Canada)

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Meeting ID: 970 2130 9544