Model predictive control to mitigate the COVID-19 outbreak in a
The COVID-19 outbreak is deeply influencing the global social and
economic framework, due to restrictive measures adopted worldwide by
governments to counteract the pandemic contagion.
In multi-region areas such as Italy, where the contagion peak has been
reached, it is crucial to find targeted and coordinated optimal exit and
restarting strategies on a regional basis to effectively cope with
possible onset of further epidemic waves, while efficiently returning
the economic activities to their standard level of
Differently from the related literature,
where modeling and controlling the pandemic contagion is typically
addressed on a national basis, this paper proposes an optimal control
approach that supports governments in defining the most effective
strategies to be adopted during post-lockdown mitigation phases in a
Based on the joint use of a non-linear Model Predictive Control scheme
and a modified Susceptible-Infected-Recovered (SIR)-based
epidemiological model, the approach is aimed at minimizing the cost of
the so-called non-pharmaceutical interventions (that is, mitigation
strategies), while ensuring that the capacity of the network of regional
healthcare systems is not violated.
In addition, the proposed approach supports policy makers in taking
targeted intervention decisions on different regions by an integrated
and structured model, thus both respecting the specific regional health
systems characteristics and improving the system-wide performance by
avoiding uncoordinated actions of the regions.
methodology is tested on the COVID-19 outbreak data related to the
network of Italian regions, showing its effectiveness in properly
supporting the definition of effective regional strategies for managing
the COVID-19 diffusion.