Non-Pharmaceutical Stochastic Optimal Control Strategies to Mitigate the
COVID-19 Spread
Abstract
This paper proposes a stochastic non-linear model predictive controller
to support policy-makers in determining robust optimal
non-pharmaceutical strategies to tackle the COVID-19 pandemic waves.
First, a time-varying SIRCQTHE epidemiological model is defined
to get predictions on the pandemic dynamics. A stochastic model
predictive control problem is then formulated to select the necessary
control actions (i.e., restrictions on the mobility for different
socio-economic categories) to minimize the socio-economic costs. In
particular, considering the uncertainty characterizing this
decision-making process, we ensure that the capacity of the healthcare
system is not violated in accordance with a chance constraint approach.
The effectiveness of the presented method in properly supporting the
definition of diversified non-pharmaceutical strategies for tackling the
COVID-19 spread is tested on the network of Italian regions using real
data. The proposed approach can be easily extended to cope with other
countries’ characteristics and different levels of the spatial scale.
Postprint accepted for pubblication in IEEE Transactions on
Automation Science and Engineering (T-ASE)
How to cite: P. Scarabaggio, R. Carli, G. Cavone, N. Epicoco
and M. Dotoli, (2021) “Non-Pharmaceutical Stochastic Optimal Control
Strategies to Mitigate the COVID-19 Spread,” in IEEE Transactions on
Automation Science and Engineering. DOI:
http://doi.org/10.1109/TASE.2021.3111338
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