Graph-coupled HMMs for modeling the spread of infection


Journal Article

We develop Graph-Coupled Hidden Markov Models (GCHMMs) for modeling the spread of infectious disease locally within a social network. Unlike most previous research in epidemiology, which typically models the spread of infection at the level of entire populations, we successfully leverage mobile phone data collected from 84 people over an extended period of time to model the spread of infection on an individual level. Our model, the GCHMM, is an extension of widely-used Coupled Hidden Markov Models (CHMMs), which allow dependencies between state transitions across multiple Hidden Markov Models (HMMs), to situations in which those dependencies are captured through the structure of a graph, or to social networks that may change over time. The benefit of making infection predictions on an individual level is enormous, as it allows people to receive more personalized and relevant health advice.

Duke Authors

Cited Authors

  • Dong, W; Pentland, AS; Heller, KA

Published Date

  • December 1, 2012

Published In

  • Uncertainty in Artificial Intelligence Proceedings of the 28th Conference, Uai 2012

Start / End Page

  • 227 - 236

Citation Source

  • Scopus