Non-Pharmaceutical Stochastic Optimal Control Strategies to Mitigate the COVID-19 Spread
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