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Non-pharmaceutical interventions (NPI) have been proven vital in the fight against the COVID-19 pandemic before the massive rollout of vaccinations. Considering the inherent epistemic-aleatoric uncertainty of parameters, accurate simulation and modeling of the interplay between the NPI and contagion dynamics are critical to the optimal design of intervention policies. We propose a modified SIRD-MPC model that combines a modified stochastic Susceptible-Infected-Recovered-Deceased (SIRD) compartment model with mixed epistemic-aleatoric parameters and Model Predictive Control (MPC), to develop robust NPI control policies to contain the infection of the COVID-19 pandemic with minimum economic impact. The simulation result indicates that our proposed model can significantly decrease the infection rate compared to the practical results under the same initial conditions. 

This paper reports two extensions to the authors’ recent work on the design of an optimally robust topology detector for a power transmission circuit with uncertain loads. Such a detector was implemented as a linear discriminator for the IEEE 9-bus system to identify, with a sub-millisecond latency, the intact circuit, or any single open-circuited line, using only the phasor measurements at the generators’ terminals. The first extension aims to replace the previously required bounded uncertain load set by a load distribution that permits rarer measurement outliers. This problem is formulated and solved as a support vector classifier. The second extension explores the solvability of a linear discriminator for topology identification for larger power systems under a bounded uncertain load set. A measure of adequacy of the involved measurement network is introduced, under which a sensor placement problem is formulated for the addition of a minimum number of phasor measurement units to meet a prescribed level of topology identifiability. In this case, sensor placement, detector design, and detector performance and robustness are demonstrated on the IEEE 68-bus system.