Uncertainty in Artificial Intelligence
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Graph-Coupled HMMs for Modeling the Spread of Infection
Wen Dong, Alex Pentland, Katherine Heller
Abstract:
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.
Keywords:
Pages: 227-236
PS Link:
PDF Link: /papers/12/p227-dong.pdf
BibTex:
@INPROCEEDINGS{Dong12,
AUTHOR = "Wen Dong and Alex Pentland and Katherine Heller",
TITLE = "Graph-Coupled HMMs for Modeling the Spread of Infection",
BOOKTITLE = "Proceedings of the Twenty-Eighth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-12)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2012",
PAGES = "227--236"
}


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