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

人类接触是包括新冠肺炎在内的任何传染病传播的基础。对于新冠肺炎来说，广泛实施的社交距离政策正是为了大幅减少个人旅行和由此产生的联系而设计的。在一些国家，这些政策有效地降低了感染高峰数量。这些政策还给社会、经济和人民生活带来了巨大代价：美国经济基本上陷入停滞，申领失业救济金人数现在超过了2008-2009年金融危机最严重的时期。这个快速的新冠肺炎应用程序将开发一个新的元种群水平模型来模拟新冠肺炎的传播，并利用强化学习来探索社会距离的最优聚集限制策略。该技术方法将开发一个与强化学习相结合的SIQR(易感、感染、隔离和康复)模型，用于持续监测和政策调整。SIQR模型建立在SIR(易感、传染性和恢复期)和SEIR(易感、暴露、传染性和恢复期)模型的经典文献基础上，并增强了它们捕获新冠肺炎独特检疫特征的能力。该项目的重点是将SIQR模型与强化学习相结合，以实现在稀疏和噪声观测的情况下提供最优策略的控制回路。这是对传染病建模和控制这一新兴的跨学科科学的重要贡献。该项目的成果将对设计对新冠肺炎的响应具有直接的重要性，也将有助于更广泛的跨学科教育和研究计划的发展，涉及传染病建模、强化学习和大数据的机器学习。该奖项反映了美国国家科学基金会的法定使命，并已通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.