March 06, 2026
Overview:
A One Health Trust team generated a realistic synthetic population from census data that accounts for the demography, socioeconomics, and geography of individuals in selected U.S. metropolitan areas. They then simulated how a respiratory disease might spread through these networks.
The Question:
Does population structure alone, such as household size, employment type, and neighborhood characteristics, create differences in infection risk across communities, even without differences in healthcare access or biological vulnerability?
The Findings:
The simulations showed that differences in social contact patterns can lead to unequal infection risks across racial, ethnic, and socioeconomic groups.
Larger households and higher proportions of essential workers increased exposure risk in some communities. They also found that interventions such as school and workplace closures can sometimes widen geographic or social disparities in infection risk.
Overall, the findings suggest that demographics, socioeconomic factors, and policy decisions interact and influence the spread. The authors have made the synthetic population software open-source, so other researchers can build similar models to better design targeted public health interventions.
Access the open source synthetic population software built by the OHT team here.
Read the article published in Epidemics here.
