Group Sparse Bayesian Learning for Active Surveillance on Epidemic Dynamics


  • Hongbin Pei Jilin University
  • Bo Yang Jilin University
  • Jiming Liu Hong Kong Baptist University
  • Lei Dong Peking University



Surveillance, Epidemic dynamics, Group sparse, Bayesian Learning, Diffusion


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.




How to Cite

Pei, H., Yang, B., Liu, J., & Dong, L. (2018). Group Sparse Bayesian Learning for Active Surveillance on Epidemic Dynamics. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1).



Computational Sustainability and Artificial Intelligence