Efficient Strategies for Pandemic Monitoring and Recovery

在缺乏有效疫苗或治疗方法的情况下，社交距离已被证明能有效控制大流行病的初期传播。虽然何时开始社交距离的决定可基于少数阳性测试个案的出现，但由于可能出现第二波疫情，因此在疫情复苏期间何时放宽有关措施的决定要困难得多。我们在这个项目中的目标是开发新的人工智能（AI）算法来支持这种决策，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）。研究将分为两个相互关联的主题。第一个重点是通过新的基于人工智能的群体测试方法来确定个人在社区中的地位的有效方法。第二个重点是使用数据驱动的最快变化检测（QCD）框架进行社区层面的流行病评估。该项目如果成功，将对COVID-19和未来流行病的流行病恢复产生直接和明显的影响。这反过来将产生重大的社会和经济影响。目标是通过与政府和行业的关系快速部署研究成果。影响更广泛的活动包括：导航组测试设计中的公平性和准确性之间的道德权衡;支持这一专题领域的本科研究人员的多样化队列;为基于实验室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）;通过培养研究生的多样化队列整合多样性;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。

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.