A Scenario-based Model Predictive Control Scheme for Pandemic Response through Non-pharmaceutical Interventions

This extended abstract presents a scenario-based model predictive control (MPC) scheme designed to control an evolving pandemic via non-pharmaceutical interventions (NPIs). The proposed approach combines predictions and possible pandemic evolutions to decide on a level of NPIs severity to be implemented during multiple weeks to maintain hospital pressure below a prescribed threshold, while minimizing their impact on society. Specifically, we first introduce a compartmental model which divides the population into Susceptible, Infected, Detected, Threatened, Healed, and Expired (SIDTHE) sub-populations. This model is expressive enough to explicitly capture the fraction of hospitalized individuals while preserving parameter identifiability w.r.t. publicly available datasets. Second, we devise a scenario-based MPC scheme with recourse actions that captures potential uncertainty of the model parameters. e.g., due to population behavior or seasonality. Our results show that the scenario-based nature of the proposed controller manages to adequately respond to all scenarios, keeping hospital pressure at bay also in very challenging situations, whereby conventional MPC methods fail.