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Model Predictive Control Tailored to Epidemic Models



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We propose a model predictive control (MPC) approach for minimizing the social distancing and quarantine measures during a pandemic while maintaining a hard infection cap. To this end, we study the admissible and the maximal robust positively invariant set (MRPI) of the standard SEIR compartmental model with control inputs. Exploiting the fact that in the MRPI all restrictions can be lifted without violating the infection cap, we choose a suitable subset of the MRPI to define terminal constraints in our MPC routine and show that the number of infected people decays exponentially within this set. Furthermore, under mild assumptions we prove existence of a uniform bound on the time required to reach this terminal region (without violating the infection cap) starting in the admissible set. The findings are substantiated based on a numerical case study.

Sauerteig, P., Esterhuizen, W., Wilson, M. & Streif, S. (2021). Model predictive control tailored to epidemic models. ResearchGate.