  Synthetic contact networks are useful for modeling epidemic spread and social
transmission, but data to infer realistic contact patterns that take account of
assortative connections at the geographic and economic levels is limited. We
developed a method to generate synthetic contact networks for any region of the
United States based on publicly available data. First, we generate a synthetic
population of individuals within households from US census data using
combinatorial optimization. Then, individuals are assigned to workplaces and
schools using commute data, employment statistics, and school enrollment data.
The resulting population is then connected into a realistic contact network
using graph generation algorithms. We test the method on two census regions and
show that the synthetic populations accurately reflect the source data. We
further show that the contact networks have distinct properties compared to
networks generated without a synthetic population, and that those differences
affect the rate of disease transmission in an epidemiological simulation. We
provide open-source software to generate a synthetic population and contact
network for any area within the US.
