Control of epidemics on graphs

The control of complex dynamic networks has many applications ranging from the management of electric power grids to the regulation of biological cellular networks. Prior work has focused on understanding the relationship between cascading behavior and network structure, the role of critical nodes, and conditions necessary for sustained cascade propagation. Recent work has begun to examine general approaches for dynamically influencing processes to guide the network or a targeted subnetwork towards a desired state. This paper models the control of an epidemic as a Markov Decision Process and applies a variety of different state-of-the-art planning algorithms to arrive at targeted control strategies. The results reveal the conditions under which certain approaches succeed and fail.