  We describe how to generate a heterosexual network with a prescribed
joint-degree distribution that is embedded in a prescribed large-scale social
contact network. The structure of a sexual network plays an important role in
how sexually transmitted infections (STIs) spread. Generating an ensemble of
networks that mimics the real-world is crucial to evaluating robust mitigation
strategies for controling STIs. Most of the current algorithms to generate
sexual networks only use sexual activity data, such as the number of partners
per month, to generate the sexual network. Real-world sexual networks also
depend on biased mixing based on age, location, and social and work activities.
We describe an approach to use a broad range of social activity data to
generate possible heterosexual networks. We start with a large-scale simulation
of thousands of people in a city as they go through their daily activities,
including work, school, shopping, and activities at home. We extract a social
network from these activities where the nodes are the people and the edges
indicate a social interaction, such as working in the same location. This
social network captures the correlations between people of different ages,
living in different locations, their economic status, and other demographic
factors. We use the social contact network to define a bipartite heterosexual
network that is embedded within an extended social network. The resulting
sexual network captures the biased mixing inherent in the social network, and
models based on this pairing of networks can be used to investigate novel
intervention strategies based on the social contacts of infected people. We
illustrate the approach in a model for the spread of Chlamydia in the
heterosexual network representing the young sexually active community in New
Orleans.
