RAPID: Collaborative Research: Modeling and Learning-based Design of Social Distancing Policies for COVID-19

RAPID：协作研究：针对 COVID-19 的社交距离政策的建模和基于学习的设计

• 批准号: 2030140
• 负责人: Cynthia Chen
• 金额: $ 10万
• 依托单位: University of Washington
• 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: 2020-06-15 至 2022-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2030140&HistoricalAwards=false
• 关键词: RAPID; Collaborative; Research; Modeling; Learning

Human contacts underlie the spread of any infectious diseases including COVID-19. For COVID-19, the widely implemented social distancing policies are designed precisely to drastically reduce individual travels and the resulting contacts. In a number of States, these policies have effectively reduced the peak number of infections. These policies have also come with huge costs on the society, economy and people’s lives: US economy has largely come to a halt and the number of unemployment claims has now exceeded the worst of the 2008-2009 financial crisis. This rapid COVID-19 application will develop a novel meta-population level model simulating the spread of COVID-19 and utilize reinforcement learning to explore optimal congregation restriction policies for social distancing. The technical approach will develop an SIQR (Susceptible, Infected, Quarantined, and Recovered) model integrated with reinforcement learning for continuous monitoring and policy adjustment. The SIQR model is built on the classic literature of the SIR (susceptible, infectious and recovered) and SEIR (susceptible, exposed, infectious, and recovered) models and enhances their capability to capture the unique quarantine features for COVID-19. The key focus of the proposed project is on the connection of the SIQR model to reinforcement learning to realize a control loop that provides optimal policy in spite of sparse and noisy observations. This is an important contribution to this emerging, interdisciplinary science of infectious disease modeling and control. The results of this project will have both immediate importance for designing the response to COVID-19 and also contribute to the broader development of an interdisciplinary education and research program involving infectious disease modeling, reinforcement learning and machine learning of big data.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.

人际接触是包括 COVID-19 在内的任何传染病传播的基础，广泛实施的社会疏远政策正是为了大幅减少个人旅行以及由此产生的接触，在许多州，这些政策有效地减少了人与人之间的接触。这些政策也给社会、经济和人民生活带来了巨大的代价：美国经济基本陷入停滞，失业救济人数现已超过2008-2009年金融危机期间的最严重时期。迅速的COVID-19 应用程序将开发一种模拟 COVID-19 传播的新型元群体水平模型，并利用强化学习来探索社交距离的最佳聚集限制政策。该技术方法将开发 SIQR（易感、感染、隔离和康复）。 ）模型与强化学习相结合，用于持续监测和政策调整。SIQR模型建立在SIR（易感、传染性和康复）和SEIR（易感、暴露、传染性和恢复）的经典文献基础上。该项目的重点在于将 SIQR 模型与强化学习相结合，以实现一个控制环，该控制环在稀疏和稀疏的情况下仍可提供最佳策略。这是对传染病建模和控制这一新兴跨学科科学的重要贡献，该项目的结果对于设计针对 COVID-19 的应对措施具有直接重要性，也有助于跨学科教育的更广泛发展。和涉及传染病的研究计划该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。