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Graph-Coupled HMMs for Modeling the Spread of Infection

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Executive Summary

The authors 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, they 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. Their 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.

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