RAPID: Accelerated Testing for COVID-19 using Group Testing

COVID-19导致了前所未有的全球健康危机，在未来几个月可能会变得更加广泛。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执行遏制政策和确保医疗资源能够适当分配给不同的地理区域非常重要。个别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COVID-19的广泛检测，同时使用更少的检测。主要的方法是基于汇集来自多个患者的样本并对组合样本进行测试的想法。如果测试结果为阴性，则可以断定池中没有人被感染，如果结果为阳性，则可以执行进一步的细粒度测试。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得有关感染率的粗粒度人群信息所需的测试数量。在本项目中，将设计有效的分组测试方案，以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。在本项目中，将设计和优化不需要精确了解感染率的群体测试方案和多阶段群体测试方案。还将说明所需测试次数与完成测试所需总时间之间的权衡。将研究分组测试方案对受试人群感染状态相关性和测试误差的稳健性。使用组检验和假设检验的数学工具，也将设计和分析快速分类感染率的策略。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分组测试过程。重点将放在小样本池和人群规模上。成功完成该项目中的拟议活动将推动团体测试领域的最新技术水平，并为COVID-19测试提供实用高效的解决方案。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。

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.