Understanding mixing patterns is crucial for modeling disease spread and develop optimal mitigation strategies. We are using experimental data and detailed computational models of synthetic populations to generate mixing patterns and social contact networks for the United States. Our goal is to provide better estimates for modeling heterogeneous mixing patterns and development of optimal mitigation strategies in the presence of population heterogeneity.
We are using large agent-based simulations of the United States, which use the U.S. census to create synthetic populations along with activity surveys to create itineraries and simulate mobility and transmission dynamics. Our simulations capture contact patterns, contact duration, spatial dynamics, which can be used to estimate the probability of transmission between two people at different activities.