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Adaptively monitoring the states of nodes in a large complex network is ofinterest in domains such as national security, public health, and energy gridmanagement. Here, we present an information theoretic adaptive tracking andsampling framework that recursively selects measurements using the feedbackfrom performing inference on a dynamic Bayesian Network. We also presentconditions for the existence of a network specific, observation dependent,phase transition in the updated posterior of hidden node states resulting fromactively monitoring the network. Since traditional epidemic thresholds arederived using observation independent Markov chains, the threshold of theposterior should more accurately model the true phase transition of a network.The adaptive tracking framework and epidemic threshold should provide insightinto modeling the dynamic response of the updated posterior to activeintervention and control policies while monitoring modern complex networks.

Information Theoretic Adaptive Tracking of Epidemics in Complex Networks