University of Pennsylvania
Tuesday, March 24, 2020, 2:45-3:45 PM EST
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Meeting ID: 712 092 155 / Dial in: 1 646 876 9923 US (New York)
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We study the problem of controlling epidemic outbreaks in networked populations by distributing protection resources throughout the nodes of the network. We assume that two types of protection resources are available: (i) Preventive resources able to defend individuals in the population against the spreading of the disease (such as vaccines or disease-awareness campaigns), and (ii) corrective resources able to neutralize the spreading (such as antidotes). We assume that both preventive and corrective resources have an associated cost and study the problem of finding the cost-optimal distribution of resources throughout the networked population. We analyze these questions in the context of a viral outbreak and study the following two problems: (i) Given a fixed budget, find the optimal allocation of preventive and corrective resources in the network to achieve the highest level of disease containment, and (ii) when a budget is not specified, find the minimum budget required to eradicate the disease. We show that both resource allocation problems can be efficiently solved for a wide class of cost functions. We illustrate our approach by designing optimal protection strategies to contain an epidemic outbreak that propagates through the air transportation network.
Victor M. Preciado is an Associate Professor and Graduate Chair in the Department of Electrical and Systems Engineering at the University of Pennsylvania, where he is affiliated with the Networked & Social Systems Engineering program, the Warren Center for Network & Data Sciences, and the Applied Math and Computational Science program. He received the Ph.D. degree in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology under the supervision of Prof. George Verghese and was a postdoctoral researcher at the GRASP lab working with Prof. Ali Jadbabaie. He was a recipient of the 2017 National Science Foundation CAREER Award, the 2018 Best Paper Award by the IEEE Control Systems Magazine, and a runner-up of the 2019 Best Paper Award by the IEEE Transactions on Network Science and Engineering for his work on control of epidemics. He is an IEEE Senior Member and Associate Editor for the IEEE Transactions on Network Science and Engineering. His main research interests lie at the intersection of Networks, Dynamics, and Data Sciences; in particular, in using innovative mathematical and computational approaches to model and control complex, high-dimensional dynamical systems. Relevant applications of this line of research can be found in the context of epidemic modeling and control, information spreading over socio-technical networks, resilience of networked infrastructure, and brain dynamics.