  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.
