Title: Group Sparse Bayesian Learning for Active Surveillance on Epidemic Dynamics,
Abstract: Predicting epidemic dynamics is of great value in understanding and
controlling diffusion processes, such as infectious disease spread and
information propagation. This task is intractable, especially when surveillance
resources are very limited. To address the challenge, we study the problem of
active surveillance, i.e., how to identify a small portion of system components
as sentinels to effect monitoring, such that the epidemic dynamics of an entire
system can be readily predicted from the partial data collected by such
sentinels. We propose a novel measure, the gamma value, to identify the
sentinels by modeling a sentinel network with row sparsity structure. We design
a flexible group sparse Bayesian learning algorithm to mine the sentinel
network suitable for handling both linear and non-linear dynamical systems by
using the expectation maximization method and variational approximation. The
efficacy of the proposed algorithm is theoretically analyzed and empirically
validated using both synthetic and real-world data.