To build a detailed model of a population, Eubank and Marathe, who are deputy directors of the Network Dynamics and Simulation Sciences Laboratory (NDSSL), and their 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 combined with other models of people's behavior 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.
In 2008, MIDAS researchers published a paper in the Proceedings of the National Academy of Sciences that concluded that a timely implementation of targeted household antiviral prevention measures and a reduction in contact between individuals could substantially lower the spread of the disease until a vaccine was available. Intervention methods used were antiviral treatment and household isolation of identified cases, disease prevention strategies and quarantine of household contacts, school closings, and reducing workplace and community contacts.
"Past support from MIDAS has helped us scale our simulations from local to regional and national levels, to understand what details matter to the big picture, and to learn more about the important issues facing public health decision-makers," said Eubank "We've also developed important collaborations with researchers at Northwestern University, the University of Utah, and Clemson University, who will participate in this project. We're thrill
|Contact: Tiffany Trent|