Hierarchical graph-coupled HMMs for heterogeneous personalized health data

Conference Paper

© 2015 ACM. The purpose of this study is to leverage modern technology (mobile or web apps) to enrich epidemiology data and infer the transmission of disease. We develop hierarchical Graph-Coupled Hidden Markov Models (hGCHMMs) to simultaneously track the spread of infection in a small cell phone community and capture person-specific infection parameters by leveraging a link prior that incorporates additional covariates. In this paper we investigate two link functions, the beta-exponential link and sigmoid link, both of which allow the development of a principled Bayesian hierarchical framework for disease transmission. The results of our model allow us to predict the probability of infection for each persons on each day, and also to infer personal physical vulnerability and the relevant association with covariates. We demonstrate our approach theoretically and experimentally on both simulation data and real epidemiological records.

Full Text

Duke Authors

Cited Authors

  • Fan, K; Eisenberg, M; Walsh, A; Aiello, A; Heller, K

Published Date

  • August 10, 2015

Published In

  • Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volume / Issue

  • 2015-August /

Start / End Page

  • 239 - 248

International Standard Book Number 13 (ISBN-13)

  • 9781450336642

Digital Object Identifier (DOI)

  • 10.1145/2783258.2783326

Citation Source

  • Scopus