Collaborative Research: Optimized Testing Strategies for Fighting Pandemics: Fundamental Limits and Efficient Algorithms

合作研究：抗击流行病的优化测试策略：基本限制和高效算法

基本信息

批准号：
2133170
负责人：
Jing Yang
金额：
$ 27.48万
依托单位：
Pennsylvania State Univ University Park
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-09-01 至 2025-08-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2133170&HistoricalAwards=false
关键词：
Collaborative Research Optimized Testing Strategies

项目摘要

Large-scale high-throughput prevalence and diagnostic testing is essential for the containment and mitigation of pandemics. The testing bottleneck in the COVID-19 pandemic has led to a resurgence of interest in group testing, where several people's biological samples are mixed together and examined in a single test. When the rate of infection in the population is low, this method can significantly reduce the total number of tests per subject and increase the throughput of the existing testing infrastructure. However, traditional group testing has the following limitations: First, efficient group testing based methods for the estimation of prevalence have been largely overlooked in the literature. Second, traditional group testing usually assumes that the testing results are qualitative (positive versus negative), not quantitative (providing viral load information). Third, the theoretical study of group testing rarely takes practical constraints, such as the sensitivity of the pooled tests and the dilution effect, into consideration, which hinders the applicability of the testing schemes in practice. The goal of this project is to overcome these limitations of traditional group testing and design advanced pooled testing strategies for efficient prevalence tracking and accurate infection diagnosis. It will develop optimized pooled testing strategies with strong theoretical performance guarantees yet feasible and cost-effective in practice.The proposed research is organized in three research thrusts as follows. Thrust 1 aims to design effective sampling and testing algorithms to estimate the prevalence in communities and track its evolution, under scarce testing resource constraints. Thrust 2 focuses on the design of optimized pooling and decoding algorithms for compressed sensing based (COVID-19) virus diagnostic testing. Thrust 3 validates the accuracy and efficiency of the proposed pooled testing through experiments on anonymized COVID-19 samples. This project bridges group testing and online learning, the two largely disconnected areas, with the objective to effectively allocate limited testing resources for efficient prevalence tracking. Such integration leads to novel sampling strategies, broadens the paradigm of group testing, and advances the state of the art of online learning. Moreover, the proposed compressed sensing based diagnostic testing leverages quantitative measurements provided by advanced testing technologies, which can significantly increase test throughput, reduce the number of needed tests, decrease the consumption of scarce reagents, and provide results robust against observation noises and outliers. The rich compressed sensing theory provides possible approaches to the rigorous mathematical certification of the correctness of the decoded results. Besides, the clinical constraints on pooled testing also lead to novel problem formulation and theoretical characterization, enriching the study of compressed sensing.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.

大规模高通量流行和诊断测试对于遏制和缓解流行病至关重要。 COVID-19 大流行中的检测瓶颈导致人们重新燃起对团体检测的兴趣，即将几个人的生物样本混合在一起并在一次测试中进行检查。当人群感染率较低时，这种方法可以显着减少每个受试者的测试总数，并提高现有测试基础设施的吞吐量。然而，传统的群体测试具有以下局限性：首先，文献中很大程度上忽视了基于有效群体测试的患病率估计方法。其次，传统的群体测试通常假设测试结果是定性的（阳性与阴性），而不是定量的（提供病毒载量信息）。第三，群体测试的理论研究很少考虑实际约束，例如合并测试的敏感性和稀释效应，这阻碍了测试方案在实践中的适用性。该项目的目标是克服传统群体检测的这些局限性，并设计先进的汇总检测策略，以实现高效的患病率跟踪和准确的感染诊断。它将开发具有强大理论性能保证但在实践中可行且具有成本效益的优化汇总测试策略。拟议的研究分为以下三个研究重点。主旨 1 旨在设计有效的采样和测试算法，以在测试资源有限的情况下估计社区中的患病率并跟踪其演变。 Thrust 2 专注于基于压缩感知 (COVID-19) 病毒诊断测试的优化池和解码算法的设计。 Thrust 3 通过对匿名 COVID-19 样本进行实验，验证了所提议的汇总测试的准确性和效率。该项目将团体检测和在线学习这两个基本上互不相关的领域联系起来，目的是有效分配有限的检测资源，以实现高效的患病率追踪。这种集成带来了新颖的抽样策略，拓宽了小组测试的范式，并推进了在线学习的最新技术。此外，所提出的基于压缩传感的诊断测试利用先进测试技术提供的定量测量，可以显着提高测试吞吐量，减少所需测试的数量，减少稀缺试剂的消耗，并提供针对观察噪声和异常值的稳健结果。丰富的压缩感知理论为对解码结果的正确性进行严格的数学证明提供了可能的方法。此外，汇集测试的临床限制也导致了新的问题表述和理论表征，丰富了压缩传感的研究。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。