Optimal Vaccination Strategies for Epidemic Containment

Infectious diseases are one of the major causes of death worldwide. Optimal control delivers methods that are able to minimize the life losses and economic damages of an epidemic, especially when deployed in combination with spatially explicit models that are able to describe the epidemic sufficiently accurately. Unfortunately, the prohibitive computational complexity of standard implementations has hindered the widespread use of optimal control for large-scale models such as spatially explicit epidemic models. In this seminar, we will present how these difficulties can be overcome so as to make the problem tractable, using as a test case a hypothetical vaccination campaign against COVID-19 in Italy.

Short Bio

Mario Zanon received the Master's degree in Mechatronics from the University of Trento, and the Diplôme d'Ingénieur from the Ecole Centrale Paris, in 2010. After research stays at the KU Leuven, University of Bayreuth, Chalmers University, and the University of Freiburg, he received the Ph.D. degree in Electrical Engineering from the KU Leuven in November 2015. He held a Post-Doc researcher position at Chalmers University until the end of 2017, after which he became Assistant Professor and, from 2021, Associate Professor at the IMT School for Advanced Studies Lucca. His research interests include reinforcement learning, numerical methods for optimization, economic MPC, optimal control and estimation of nonlinear dynamical systems, in particular for aerospace and automotive applications.