Detecting Sources of Healthcare Associated Infections


  • Hankyu Jang University of Iowa
  • Andrew Fu University of Virginia
  • Jiaming Cui Georgia Institute of Technology
  • Methun Kamruzzaman University of Virginia
  • B. Aditya Prakash Georgia Institute of Technology
  • Anil Vullikanti University of Virginia
  • Bijaya Adhikari University of Iowa
  • Sriram V. Pemmaraju University of Iowa



DMKM: Applications, DMKM: Graph Mining, Social Network Analysis & Community Mining, APP: Healthcare, Medicine & Wellness


Healthcare acquired infections (HAIs) (e.g., Methicillin-resistant Staphylococcus aureus infection) have complex transmission pathways, spreading not just via direct person-to-person contacts, but also via contaminated surfaces. Prior work in mathematical epidemiology has led to a class of models – which we call load sharing models – that provide a discrete-time, stochastic formalization of HAI-spread on temporal contact networks. The focus of this paper is the source detection problem for the load sharing model. The source detection problem has been studied extensively in SEIR type models, but this prior work does not apply to load sharing models. We show that a natural formulation of the source detection problem for the load sharing model is computationally hard, even to approximate. We then present two alternate formulations that are much more tractable. The tractability of our problems depends crucially on the submodularity of the expected number of infections as a function of the source set. Prior techniques for showing submodularity, such as the "live graph" technique are not applicable for the load sharing model and our key technical contribution is to use a more sophisticated "coupling" technique to show the submodularity result. We propose algorithms for our two problem formulations by extending existing algorithmic results from submodular optimization and combining these with an expectation propagation heuristic for the load sharing model that leads to orders-of-magnitude speedup. We present experimental results on temporal contact networks based on fine-grained EMR data from three different hospitals. Our results on synthetic outbreaks on these networks show that our algorithms outperform baselines by up to 5.97 times. Furthermore, case studies based on hospital outbreaks of Clostridioides difficile infection show that our algorithms identify clinically meaningful sources.




How to Cite

Jang, H., Fu, A., Cui, J., Kamruzzaman, M., Prakash, B. A., Vullikanti, A., Adhikari, B., & Pemmaraju, S. V. (2023). Detecting Sources of Healthcare Associated Infections. Proceedings of the AAAI Conference on Artificial Intelligence, 37(4), 4347-4355.



AAAI Technical Track on Data Mining and Knowledge Management