This paper proposes a stochastic non-linear model predictive controller
to support policy-makers in determining robust optimal strategies to
tackle the COVID-19 secondary waves. First, a time-varying <i>SIRCQTHE </i>epidemiological
model (considering Susceptible, Infected, Removed, Contagious,
Quarantined, Threatened, Healed, and Extinct compartments of
individuals) is defined to get reliable predictions on the pandemic
dynamics on a regional basis. A stochastic Model Predictive Control
problem is then formulated to select the necessary control actions to
minimize the arising socio-economic costs. <br>In particular,
considering the unavoidable uncertainty characterizing this
decision-making process, we ensure that the capacity of the network of
regional healthcare systems is not violated in accordance with a chance
constraint approach.<br>Furthermore, since the infection rate depends on
people’s mobility, differently from the related literature, we model
the control actions as interventions affecting the mobility levels
associated to different socio-economic categories.<br><div>The effectiveness
of the presented method in properly supporting the definition of
diversified regional strategies for tackling the COVID-19 spread is
tested on the network of Italian regions using real data from the
Italian Civil Protection Department. However, provided the availability
of reliable data, the proposed approach can be easily extended to cope
with other countries' characteristics and different levels of the
spatial scale.</div><div><br></div><div>Preprint of paper submitted to IEEE Transactions on Automation Science and Engineering (<em>T-ASE</em>)</div>
Multi-objective optimization strategy of multi-sources power system operation based on fuzzy chance constraint programming and improved analytic hierarchy process
