Online Program

Using big data to model public health dynamics for preparedness decision support

Monday, November 4, 2013 : 10:50 a.m. - 11:10 a.m.

Molly Eggleston, MPH, CPH, MCHES, Public Health Dynamics Laboratory, University of Pittsburgh, Pittsburgh, PA
Donald Burke, MD, Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA
John Grefenstette, PhD, Public Health Dynamics Laboratory, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA
The University of Pittsburgh Public Health Dynamics Laboratory is housed at the University of Pittsburgh Graduate School of Public Health. It is an interdisciplinary collaboration of scientists applying computational and mathematical modeling to protect the world from communicable diseases. It advances modeling techniques as well as applies them. This session will offer some tools for and models of public health decision support based on big data and super computation.

Modeling is a key step in any decision-making process; better models lead to improved knowledge and wiser decision-making for public health. Big data is a precursor to modeling complex public health problems. Supercomputing allows faster analysis and processing of big data than ever before. The lab can generate an agent-based model for just about any place in the U.S. It did this is 2009, using models to forecast outcomes from various intervention strategies to stop the spread of influenza for the U.S. government. This session will discuss the data needs for stronger models and better model application.

An ideal database for preparedness decision support would include detailed spatio-temporal data describing past historical instances of similar events, including measures undertaken to protect public health, and quantitative outcome reports. The data should be easily accessible, computable, and disaggregated.

This session will describe several tools that the lab has generated to assist decision makers. The tools range from historical data collections, agent-based synthetic models, behavioral models, models of viral evolution, visualizers, and models of the legal, economic and operational components of public health.

Learning Areas:

Communication and informatics
Ethics, professional and legal requirements
Public health or related research
Systems thinking models (conceptual and theoretical models), applications related to public health

Learning Objectives:
Explain how computational representations of complex systems can provide valuable support to decision-making. Define data acquisition and data curation. Identify the first link in the meta-modeling chain as reliable real-world data, which ideally should be easily accessible, computable, and disaggregated (ie, individual-level).

Keyword(s): Data Collection, System Involvement

Presenting author's disclosure statement:

Qualified on the content I am responsible for because: I am staff to the MIDAS Center of Excellence. I am familiar with the MIDAS NAtional Network, its research and outcomes.
Any relevant financial relationships? No

I agree to comply with the American Public Health Association Conflict of Interest and Commercial Support Guidelines, and to disclose to the participants any off-label or experimental uses of a commercial product or service discussed in my presentation.