  Contact patterns in populations fundamentally influence the spread of
infectious diseases. Current mathematical methods for epidemiological
forecasting on networks largely assume that contacts between individuals are
fixed, at least for the duration of an outbreak. In reality, contact patterns
may be quite fluid, with individuals frequently making and breaking social or
sexual relationships. Here we develop a mathematical approach to predicting
disease transmission on dynamic networks in which each individual has a
characteristic behavior (typical contact number), but the identities of their
contacts change in time. We show that dynamic contact patterns shape
epidemiological dynamics in ways that cannot be adequately captured in static
network models or mass-action models. Our new model interpolates smoothly
between static network models and mass-action models using a mixing parameter,
thereby providing a bridge between disparate classes of epidemiological models.
Using epidemiological and sexual contact data from an Atlanta high school, we
then demonstrate the utility of this method for forecasting and controlling
sexually transmitted disease outbreaks.
