RAPID: High-Throughput and Low-Cost Testing of COVID-19 Viruses and Antibodies through Compressed Sensing and Group Testing

大规模、高通量及准确的COVID-19病毒及抗体测试是对抗持续COVID-19大流行的重要工具。公共卫生专家认为，大规模病毒和抗体检测对于阻止COVID-19病毒的传播，并使人们能够安全快速地过渡到正常的社会生活至关重要。然而，目前的检测能力有限，此外，经常缺乏进行检测所需的试剂。 迫切需要增加美国和世界各地目前的测试能力。该快速反应研究（RAPID）项目旨在通过数学和信号处理方法显著提高COVID-19病毒和抗体检测的吞吐量，并减少试剂消耗，同时不牺牲检测准确性。 该项目促进了病毒和抗体的检测和定量科学。 该项目通过实现大规模COVID-19病毒和抗体检测、有效的接触者追踪以及更安全、更快地恢复正常经济活动，为国家利益服务，并促进国家健康、繁荣和福利。 本项目提出的方法也适用于其他传染病的检测。如果成功的话，这个项目的研究成果可以作为生动的例子，激发公众和K-12学生对STEM教育和研究的兴趣，展示STEM在抗击疫情中的力量。提高有效测试能力的一个简单方法是对多个科目的合并样本进行测试，而不是单独测试每个人的样本。合并检测已成功用于传染病检测，如人类免疫缺陷病毒在过去。虽然这个想法的一个简单版本称为分组测试可以追溯到几十年前，但该项目提出了一种基于压缩感知理论的新型更强大和更通用的池化测试，其中包括分组测试作为特例。在病毒检测中，标准拭子检测使用逆转录聚合酶链反应（PCR）过程来选择性扩增COVID-19病毒特异性病毒RNA产生的DNA链。广泛使用的定量PCR（qPCR）方法不仅允许靶RNA序列的二元检测（存在或不存在），而且允许RNA的定量，从而产生测试样品中RNA的量的估计。这种量化可以实现用于病毒测试中的合并测试的压缩感测，这可以潜在地显著增加测试通量，减少所需测试的数量，减少稀缺试剂的消耗，并提供对观察噪声和离群值稳健的定量结果。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Large-scale, high-throughput and accurate COVID-19 virus and antibody testings are vital tools in the fight against the ongoing COVID-19 pandemic. Public health experts believe that mass virus and antibody testings are essential to stopping spread of COVID-19 viruses, and enabling a safe and fast transition to normal social life. However, current testing capacity is limited, and, in addition, there is often a shortage of reagents needed for performing tests. There is an urgent need to increase the current testing capacity in the United States and around the world. This Rapid Response Research (RAPID) project seeks to significantly increase the throughputs of COVID-19 virus and antibody testing, and reduce reagent consumptions through mathematical and signal processing methods, without sacrificing test accuracy. This project promotes the science of detecting and quantifying virus and antibody. This project serves the national interest and advances national health, prosperity and welfare by enabling large-scale COVID-19 virus and antibody testing, effective contact tracing and a safer, faster transition back to normal economic activities. The methods proposed in this project are also applicable to testing for other infectious diseases. If successful, research outputs from this project can be used as lively examples to inspire the public and K-12 students' interest in STEM education and research by showing the power of STEM in fighting the pandemic.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, this project proposes to 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 potentially 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. The proposed work includes designing optimized pooled measurements, and optimized inference algorithms for compressed sensing and group testing in COVID-19 virus and antibody testing.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.