Efficient Strategies for Pandemic Monitoring and Recovery

在缺乏有效疫苗或治疗方法的情况下，社会距离已被证明在控制大流行的初始传播方面是有效的。虽然决定何时开始保持社交距离可以根据出现一些阳性检测病例，但在大流行恢复期间决定何时放松此类措施要困难得多，因为有可能出现第二波。我们在这个项目中的目标是通过更有效地利用有限的测试可用性，并利用不受测试限制的新数据源（和物理模型），开发新的人工智能（AI）算法来支持这一决策。这项研究将分为两个相互关联的重点。第一个重点是通过新的基于人工智能的群体测试方法，有效地确定个人在社区中的地位。第二个重点是利用数据驱动的最快变化检测（QCD）框架对社区一级的大流行进行评估。该项目如果成功，将对COVID-19和未来大流行的大流行恢复产生直接和明显的影响。这将反过来产生重大的社会和经济影响。目标是通过政府和工业界的关系迅速部署研究成果。更广泛的影响活动包括：在群体测试设计的公平性和准确性之间进行道德权衡；支持这一主题领域的不同本科生研究人员；为以实验室为基础的数据科学课程开发一个新的通用教育模块（在伊利诺伊州和世界各地）；通过培养多样化的研究生队伍来整合多样性；以及通过知名媒体进行公众宣传。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。

In the absence of effective vaccines or treatments, social distancing has been shown to be effective in controlling the initial spread of a pandemic. While the decision on when to start social distancing can be based on the occurrence of a few positive test cases, the decision on when to ease such measures during pandemic recovery is considerably more difficult due to the possibility of a second wave. Our goal in this project is to develop new artificial intelligence (AI) algorithms to support this decision-making, by making more efficient use of limited test availability and also drawing on novel data sources (and physical models) not limited by testing. The research will be divided into two inter-related thrusts. The first thrust in on efficient ways to determine the status of individuals in the community through new AI based methods for group testing. The second thrust is focused on community level pandemic assessment using a data-driven quickest change detection (QCD) framework.The project, if successful, will have a direct and obvious impact on pandemic recovery, for COVID-19 and future pandemics. This will in turn have significant social and economic impacts. The goal is to rapidly deploy research results via relationships in government and industry. Broader impact activities include: navigating the ethical tradeoff between equity and accuracy in group testing designs; supporting a diverse cohort of undergraduate researchers in this topical area; developing a new, general educational module for laboratory-based data science classes (at Illinois and around the world); integrating diversity by training a diverse cohort of graduate students; and public outreach via established media presence.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.