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

• 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

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.

大规模的高通量患病率和诊断测试对于遏制和缓解大流行物至关重要。 Covid-19大流行中的测试瓶颈导致了对小组测试的兴趣，其中几个人的生物样品被混合在一起并在一次测试中进行了检查。当人群中的感染率较低时，此方法可以显着减少每个受试者的测试总数，并增加现有测试基础设施的吞吐量。但是，传统的小组测试具有以下局限性：基于第一个，有效的基于群体测试的估计患病率的方法已在文献中很大程度上忽略了。其次，传统的小组测试通常假定测试结果是定性的（正相比），而不是定量（提供病毒载荷信息）。第三，小组测试的理论研究很少受到实用的约束，例如合并测试的敏感性和稀释效应，这会阻碍测试方案在实践中的适用性。该项目的目的是克服传统小组测试和设计高级合并测试策略的这些局限性，以有效地跟踪和准确的感染诊断。它将制定优化的合并测试策略，具有强大的理论性能保证，但在实践中可行且具有成本效益。拟议的研究是在三个研究推力中组织的，如下所示。推力1旨在设计有效的抽样和测试算法，以估计社区的流行并在稀缺的测试资源限制下跟踪其演变。推力2的重点是针对基于压缩传感的（COVID-19）病毒诊断测试的优化合并和解码算法的设计。推力3通过对匿名COVID-19样本进行实验验证了提出的合并测试的准确性和效率。该项目桥梁小组测试和在线学习，这是两个在很大程度上断开的领域，目的是有效地分配有限的测试资源以进行有效的患病率跟踪。这种整合会导致新颖的采样策略，扩大小组测试的范式，并提高在线学习的最新状态。此外，提出的基于压缩感应的诊断测试利用了先进测试技术提供的定量测量，该测试可以大大增加测试吞吐量，减少所需测试的数量，减少稀缺试剂的消耗，并为观察噪声和异常提供可靠的结果。丰富的压缩感应理论为对解码结果的正确性进行了严格的数学认证提供了可能的方法。此外，汇总测试的临床限制还导致了新的问题制定和理论表征，丰富了压缩感应的研究。该奖项反映了NSF的法定任务，并且认为值得通过基金会的智力优点评估来获得支持，并具有更广泛的影响。

项目成果

Federated Linear Contextual Bandits with User-level Differential Privacy

• DOI: 10.48550/arxiv.2306.05275
• 发表时间: 2023-06
• 作者: Ruiquan Huang;Huanyu Zhang;Luca Melis;Milan Shen;Meisam Hajzinia;J. Yang

Exploiting Feature Heterogeneity for Improved Generalization in Federated Multi-task Learning

• DOI: 10.1109/isit54713.2023.10206757
• 发表时间: 2023-06
• 期刊: 2023 IEEE International Symposium on Information Theory (ISIT)
• 作者: Renpu Liu;Jing Yang;Cong Shen

Provably Efficient Offline Reinforcement Learning with Perturbed Data Sources

• DOI: 10.48550/arxiv.2306.08364
• 发表时间: 2023-06
• 期刊: ArXiv
• 作者: Chengshuai Shi;Wei Xiong;Cong Shen;Jing Yang