Using big data for computational modeling of infectious diseases

Computational models of infectious disease hold great potential for studying the interactions between epidemic dynamics and realistic intervention strategies. The models rely on data acquisition and curation, statistical inference, pattern detection, and model development. The Framework for Replication of Epidemic Dynamics (FRED), an agent-based modeling platform, has been developed by the University of Pittsburgh's Modeling of Infectious Disease Agent Study National Center of Excellence. FRED is an open source, object-oriented system intended for use by researchers and public health decision-makers. This session will describe FRED and how, when applied to contemporary public health problems, it contributes to forecasting the effectiveness of epidemic mitigation strategies.

FRED uses census-based synthetic populations that capture the demographic, household, school, and workplace distributions of the U.S. population. FRED can generate an agent-based model for any county in the U.S.

FRED incorporates data from detailed vaccine and anti-viral delivery schedules and simulate multi-strain pathogens. Mitigation strategies can include vaccination, anti-viral drugs, and school closure policies. FRED includes models of health behavior change to demonstrate effects of vaccine acceptance, anti-viral use and spontaneous social distancing.

FRED can be run locally or accessed through an interface that permits users to run simulations remotely. A FRED Navigator is being developed for applications in high schools and research institutions. This session will describe how, by making FRED available, the University of Pittsburgh MIDAS National Center of Excellence, makes large-scale agent-based epidemic models more useful to the policy-making community, the research community, and as a teaching tool for students.