Generating geographically detailed synthetic contact networks: A generalizable approach with applications to epidemic outcome disparities

作者：Alexander Tulchinsky, Alisa Hamilton, Fardad Haghpanah, Nodar Kipshidze Nodar Kipshidze, Eili Klein · 发表于：Epidemics · 年份：2026 · DOI：10.1016/j.epidem.2026.100900 · 被引用次数：1 · 研究领域：COVID-19 epidemiological studies、Zoonotic diseases and public health、Health disparities and outcomes

Social contact networks based on synthetic populations are useful for studying the effects of population features and policy interventions on disease transmission. We present an adaptable and accessible method for generating geographically detailed synthetic populations and associated contact networks from public census data, and apply it to a selection of US metropolitan areas. We simulate a respiratory pathogen spreading in each population and find that network structure alone produces differences in infection risk among racial/ethnic subpopulations, as well as between geographic locations of differing socioeconomic status, particularly in urban centers. We then simulate a work and school closure policy intervention, and find an increase in geographic infection risk differences, and in some cities, in racial/ethnic risk differences as well. Different outcomes between cities are associated with demographic and geographic differences in household size, contact with school-age children, and employment industry. The results suggest that demography, socioeconomics, and policy interact in a context-dependent manner to shape epidemiological outcomes. We have made our methods available as open-source software that can be extended by other researchers. • We describe a method to generate a realistic synthetic population from census data • The resulting contact network accounts for demography, socioeconomics, and geography • Network structure alone produces racial, ethnic, and economi...