Generating a Heterosexual Bipartite Network Embedded in Social Network
Asma Azizi, Zhuolin Qu, Bryan Lewis, James Mac Hyman

TL;DR
This paper presents a method to generate realistic heterosexual sexual networks embedded within social contact networks using social activity data, aiding in better modeling and intervention strategies for STI spread.
Contribution
It introduces a novel approach to create sexual networks based on social contact data, capturing biased mixing patterns for more accurate epidemiological modeling.
Findings
Generated heterosexual networks reflect real-world social mixing patterns.
Model applied to Chlamydia spread in New Orleans.
Demonstrated potential for improved STI intervention strategies.
Abstract
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…
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