To build a detailed model of a population, Eubank and his colleagues typically start with census information, public surveys, and transportation data, which help provide a realistic picture of the daily activities of simulated people within a population and allow for detailed estimates of social contacts. These models are then used to demonstrate how social mixing patterns change under different interventions, such as the closing of schools or workplaces. Important information related to a specific infectious disease, such as H1N1 influenza for example, can be added, allowing researchers to pinpoint the best intervention strategies in a variety of situations.

"Dr. Eubank, a member of our Models of Infectious Disease Agent Study consortium, has a track record for developing sophisticated computational models to evaluate how disease outbreaks could spread and be contained," said James Anderson, who administers the NIGMS-supported consortium. "His new project, made possible by the Recovery Act, not only will advance the science of modeling, but it could also inform policy decisions that protect our health and economy during disease outbreaks."

Source: Virginia Tech