Generating geographically and economically realistic large-scale synthetic contact networks: A general method using publicly available data

TL;DR

This paper presents a new method for creating realistic large-scale synthetic contact networks for US regions using publicly available census, commute, and school data, which improves epidemic modeling accuracy.

Contribution

The authors introduce a general, data-driven approach to generate geographically and economically realistic contact networks, including open-source software for broad application.

Findings

Synthetic networks closely match source data properties

Differences in network structure influence disease transmission simulations

Method successfully applied to multiple US regions

Abstract

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.