The National Institute of General Medical Sciences, part of the National Institutes of Health, has renewed funding from its Models of Infectious Disease Agent Study for a research project at the Virginia Bioinformatics Institute at Virginia Tech, led by Stephen Eubank, professor.
Infectious diseases pose one of the most significant threats to public health worldwide. The Models of Infectious Disease Agent Study (MIDAS) is a multi-university research partnership with a mandate to develop computational models or simulations to assist policy makers, public health workers, and other researchers in making better-informed decisions about natural or intentionally caused emerging infectious diseases, and in planning for national emergencies or acts of bioterrorism.
The project, "Synthetic Information Systems for Better Informing Public Health Policymakers," began in 2004 as a five-year project, received two additional years of funding in 2009 from the American Recovery and Reinvestment Act, and will now be supported with approximately $500,000 per year for an additional five years.
"Dr. Eubank and his colleagues have done an outstanding job of advancing the goals of the MIDAS initiative by developing state of the art epidemiological models," said James Anderson, who helps manage the MIDAS program at the National Institutes of Health. "These models helped policymakers evaluate efforts to mitigate the impact of disease outbreaks, such as the 2009 H1N1 flu pandemic. Dr. Madhav Marathe and Dr. Eubank aim to refine the current models in the next phase to produce software tools that will help public health officials detect and respond to disease outbreaks in distinct geographical regions and demographic populations."
The creation of network-based models of infectious disease can help guide the design of targeted intervention strategies to combat the spread of disease. Powerful computer simulations can provide important information before an outbreak actually happens, such as the potential benefits of isolating those infected with a virus and how to optimize the use of antiviral treatments.
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 thrilled to have the opportunity to carry this research through to the next stage: combining the best research from pure and applied mathematics, epidemiology, physical and social sciences, and computer science into tools that policymakers can and will use routinely to inform their response to infectious disease outbreaks. This is an example of how NDSSL applies advanced computing to bring scientific evidence to bear on understanding the many large, interdependent complex systems that are crucial to modern life."
In addition to Virginia Bioinformatics Institute, research groups from Harvard School of Public Health, the University of Pittsburgh, University of California at Irvine, University of Chicago and Argonne National Laboratory, University of Pennsylvania School of Veterinary Medicine, University of Washington and the Fred Hutchinson Cancer Research Center, Yale University, the University of Texas at Austin, and the Research Triangle Institute are members of the MIDAS team.
The Virginia Bioinformatics Institute at Virginia Tech is a premier bioinformatics, computational biology, and systems biology research facility that uses transdisciplinary approaches to science combining information technology, biology, and medicine. These approaches are used to interpret and apply vast amounts of biological data generated from basic research to some of today's key challenges in the biomedical, environmental, and agricultural sciences. With more than 240 highly trained multidisciplinary, international personnel, research at the institute involves collaboration in diverse disciplines such as mathematics, computer science, biology, plant pathology, biochemistry, systems biology, statistics, economics, synthetic biology, and medicine. The large amounts of data generated by this approach are analyzed and interpreted to create new knowledge that is disseminated to the world's scientific, governmental, and wider communities.
Learn more about Dr. Eubank's work here: http://wwwdev.vbi.vt.edu/faculty/research_groups/stephen_eubank
The NIGMS Office of Communications contact is Kristie Saltsman. Contact her by email at email@example.com or at 410-828-6472 or 301-496-7301 (Mondays).
The NIGMS announcement is here: http://www.nigms.nih.gov/News/Results/20110815.htm
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