2007/05/15 by Erik Volz, Lauren Ancel Meyers, Volz, Erik +1
Mathematics · Medicine · Physics and Astronomy · #COVID-19 epidemiological studies #Complex Network Analysis Techniques #FOS: Biological sciences #Mathematical and Theoretical Epidemiology and Ecology Models #Populations and Evolution (q-bio.PE) #Quantitative Methods (q-bio.QM)
paper · pdf · doi:10.48550/arxiv.0705.2105
openalex publication_date 2007/05/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.