Reinforcement Learning to Safeguard Schools and Universities Against the COVID-19 Outbreak

The COVID-19 outbreak has disrupted normal activities in nearly all aspects of higher education. To reopen our universities, we need new technology and innovative practices to safeguard students against the potential second wave of the virus outbreak. In this proposal, we seek to develop analytical methods for modeling and mitigating the COVID-19 situation based on students' location and symptom data collected via mobile apps. We adopt an optimal control approach and seek intervention policies that strike a balance between containing the virus and keeping productive on-campus activities. This problem is highly challenging due to the prevalence of hidden states, unknown dynamics, and high dimensionality. By leveraging recent advances in system identification, reinforcement learning, and adaptive control, we will develop predictive methods to infer the hidden health states of individual students and develop algorithms to recommend optimal interventions (e.g., testing and quarantine) for decision makers. We will develop simplified models to assess the impact of such policies on the stability of the system captured in the growth rate of infections. The methods will be validated using simulation and available data. We expect to apply and further develop the methods to analyze real campus data from MIT in the fall semester of 2020. By using the computing capabilities of C3.ai Suite and Microsoft Azure Cloud, we expect to analyze large volumes of location data as they are collected and adapt the intervention policy. We will make our research outcomes, including software, non-confidential data sets and analysis sharable on the C3.ai platform.