Collaborative Research: CIF: Medium: Group testing for Real-Time Polymerase Chain Reactions: From Primer Selection to Amplification Curve Analysis

组测试是一种筛选技术，它依赖于仔细的组合混合和批次样品的测试。通过使用小组测试而不是个人测试，对于大多数实际问题设置，可以保证大大节省执行的测试数量，从而大大减少报告延迟和实验成本。在监测Covid-19等传染病的传播时，群体检测尤其可取，因为这需要对大量人群进行频繁检查。虽然已经提出了许多针对传染病群体检测的特别方法，但解决端到端群体检测方案设计问题的工作很少，其中包括选择用于病毒/细菌鉴定的遗传区域、测试结果的数学建模和分析以及为公共检测策略制定指导协议。该项目的总体目标是确定哪种群体检测方法可以真正减轻Covid-19和其他疾病的传播，以及在多大程度上减轻传播，估计通过使用实时聚合酶链反应（RT-PCR）检测可以减少感染人数，并帮助使用可以到达偏远地区的移动检测单元。其他更广泛的社会影响包括增强应对未来流行病的准备，以及在涉及机器学习、编码理论和生物信息学的跨学科主题上培训一批新的年轻研究人员。该项目旨在开发专门的机器学习、组合和信息理论方法，用于(a)识别具有可预测的低突变率的基因组区域，这些区域可能用作金标准实时聚合酶链反应（RT-PCR）的扩增引物，并根据感染的可能性确定最佳混合策略；(b)为RT-PCR产生的扩增曲线和相应的测试误差建立适当的模型；(c)制定特定于实验方案的非适应性和适应性半定量组测试方案，以解释非二元测试结果；(d)处理与高病毒载量受试者和高负荷社区有关的测试问题；(e)将开发的数学技术整合到疾病传播和控制的基于主体的模型中，以评估群体检测的潜在影响，并建议有效的测试-检疫-再检测策略。该奖项反映了美国国家科学基金会的法定使命，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。

Group testing is a screening technique that relies on careful combinatorial mixing and testing of batches of samples. By using group testing instead of individual testing, for most problem settings of practical interest, one is guaranteed significant savings in the number of tests performed and consequently, significant reductions in reporting delays and experimental costs. Group testing is especially desirable when monitoring the spread of infectious diseases such as Covid-19, which requires frequent examinations of massive populations. Although many ad-hoc approaches to group testing for infectious diseases have been put forward, little work has addressed the problem of end-to-end group-testing protocol design, which includes the selection of genetic regions for viral/bacterial identification, mathematical modeling and analysis of the test results and the development of guiding protocols for communal testing strategies. The overarching goals of the project are to determine which group-testing methods can actually mitigate the spread of Covid-19 and other diseases and to what extent, to estimate the reduction in the number of infected individuals achievable through the use of pooled real-time polymerase chain reaction (RT-PCR) tests, and to aid in the employment of Mobile Testing Units that can reach geographically remote regions. Other broader societal impacts include increased readiness for fighting future pandemics and training a new cohort of young researchers on interdisciplinary topics involving machine learning, coding theory and bioinformatics. The project aims to develop specialized machine-learning, combinatorial and information-theoretic methods for (a) identifying genomic regions with predictably low-mutation rates that may be used as amplification primers for gold-standard real-time polymerase chain reactions (RT-PCR) and determining best mixing strategies based on the likelihood of infection; (b) developing adequate models for amplification curves generated by RT-PCR and corresponding test-errors; (c) formulating experimental-protocol-specific non-adaptive and adaptive semiquantitative group testing schemes that account for nonbinary test outcomes; (d) addressing the testing issues associated with high-viral load subjects and heavy-hitter communities; and (e) integrating the mathematical techniques developed into an agent-based model for disease spreading and control in order to assess the potential impact of group testing and recommend effective test-quarantine-retest strategies.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.