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

点击翻译按钮获取中文摘要

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.