A Learning-Based Model Predictive Control Framework for Real-Time SIR Epidemic Mitigation

We propose a learning-based model predictive control framework for mitigating the spread of epidemics. We capture the epidemic spreading process using a susceptible-infected-removed (SIR) epidemic model and consider testing for isolation as the control strategy. In the framework, we use a daily testing strategy to remove (isolate) a portion of the infected population. Our goal is to keep the daily infected population below a certain level, while minimizing the total number of tests. Distinct from existing works on leveraging model predictive control in epidemic spreading, we learn the model parameters and compute the feedback control signal simultaneously. We illustrate the results by numerical simulation using COVID-19 data from India.