RAPID: Accelerated Testing for COVID-19 using Group Testing

RAPID：使用分组测试加速 COVID-19 测试

• 批准号: 2027997
• 负责人: Krishna Narayanan
• 金额: $ 11.46万
• 依托单位: Texas A&M Engineering Experiment Station
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2027997&HistoricalAwards=false
• 关键词: RAPID; Accelerated; Testing; COVID; 19

COVID-19 has resulted in an unprecedented global health crisis that may become even more widespread over the upcoming months. Extensive and immediate testing of symptomatic and asymptomatic people is known to be important for implementing containment policies and to ensure that medical resources can be apportioned to different geographic regions appropriately. Individual testing can provide the necessary information; however, this requires enormous amounts of medical and human resources. This project will facilitate widespread testing for COVID-19 while using fewer tests. The main approach is based on the idea of pooling samples from multiple patients and performing tests on combined samples. If the result of a test is negative, one can conclude that no one in the pool is infected, and if the result is positive, then further fine-grained testing can be performed. Pooling-based testing, also known as group testing, can be very effective in reducing the number of tests required for both identifying infected people in a population and for obtaining coarse-grained population-level information about infection rates. In this project, effective group-testing schemes that minimize the total number of tests required and/or the total time taken to conduct tests will be designed, and their performance will be analyzed. In this project, group-testing schemes that do not require precise knowledge of the infection rates and multi-stage group-testing schemes will be designed and optimized. The trade-off between the number of tests required and the total time taken to complete testing will also be characterized. The robustness of group-testing schemes to correlation in the infection status among the tested population and to errors in the tests will be studied. Using mathematical tools from group testing and hypothesis testing, strategies for rapid classification of infection rates will also be designed and analyzed. Finally, a smart-phone application which guides laboratory technicians through the group-testing process will be developed. The focus will be on small pool sizes and population sizes. Successful completion of the proposed activities in this project will advance the state of the art in the field of group testing, and provide practical and efficient solutions for COVID-19 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.

COVID-19 已导致前所未有的全球健康危机，在未来几个月内可能会变得更加普遍。众所周知，对有症状和无症状的人进行广泛和立即的检测对于实施遏制政策并确保医疗资源能够适当地分配到不同的地理区域非常重要。单独测试可以提供必要的信息；然而，这需要大量的医疗和人力资源。该项目将促进对 COVID-19 的广泛测试，同时使用更少的测试。主要方法基于汇集多个患者的样本并对组合样本进行测试的想法。如果检测结果为阴性，则可以断定池中没有人被感染，如果结果为阳性，则可以进行进一步的细粒度检测。基于池的测试（也称为群体测试）可以非常有效地减少识别人群中感染者和获取有关感染率的粗粒度人群水平信息所需的测试数量。在该项目中，将设计有效的小组测试方案，最大限度地减少所需的测试总数和/或进行测试所需的总时间，并分析其性能。在该项目中，将设计和优化不需要精确了解感染率的群体检测方案和多阶段群体检测方案。所需测试数量和完成测试所需的总时间之间的权衡也将被表征。将研究分组测试方案与测试人群感染状态相关性和测试错误的稳健性。使用群体测试和假设检验的数学工具，还将设计和分析感染率快速分类的策略。最后，将开发一个智能手机应用程序，指导实验室技术人员完成小组测试过程。重点将放在小池规模和人口规模上。该项目拟议活动的成功完成将推动团体检测领域的最先进水平，并为 COVID-19 检测提供实用且高效的解决方案。该奖项反映了 NSF 的法定使命，经评估认为值得支持利用基金会的智力优势和更广泛的影响审查标准。