SIR epidemics in dynamic contact networks

TL;DR

This paper introduces a new mathematical model for predicting disease spread on dynamic contact networks, capturing fluid social interactions often overlooked by static models, and demonstrates its application to real-world data.

Contribution

The authors develop a novel approach that models disease transmission on dynamic networks, bridging static and mass-action models, and validate it with empirical contact data.

Findings

Dynamic contact patterns significantly influence epidemic dynamics.

The model effectively forecasts and controls sexually transmitted disease outbreaks.

It provides a flexible framework interpolating between existing epidemiological models.

Abstract

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.