Optimal Resource Allocation to Control Epidemic Outbreaks in Networked Populations

Tuesday, September 22, 2015 - 4:00pm

Event Calendar Category

LIDS Seminar Series

Speaker Name

Victor Preciado


University of Pennsylvania

Building and Room Number



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 the Raj and Neera Singh Assistant Professor of Electrical and Systems Engineering at the University of Pennsylvania, where he is affiliated with the Networked & Social Systems Engineering program and the Warren Center for Network & Data Sciences. He received his Ph.D. degree in Electrical Engineering and Computer Science from MIT in 2008. He has also been a visiting scientist at UC Berkeley and Santa Fe Institute. His main research interests lie in the modeling, analysis, control, and optimization of large-scale complex dynamic networks, with applications in social networks, technological infrastructure, and biological systems.