A researcher from the Virginia Bioinformatics Institute (VBI) at Virginia Tech has received a grant from the National Institute of General Medical Sciences (NIGMS), part of the National Institutes of Health (NIH), to support ongoing work to develop high-performance computer models for the study of very large networks. Simulations of large networks on high-performance computers can be used to study the emergence and spread of infectious diseases, such as H1N1 influenza.
VBI Professor and Deputy Director of the Network Dynamics and Simulation Science Laboratory (NDSSL) Stephen Eubank received $786,797 through the American Recovery and Reinvestment Act (ARRA) to support the work. The funding will help Eubank and his team develop tools to aid policymakers in making important public health policy decisions. The creation of network-based models of infectious disease can help guide the design of targeted intervention strategies for the effective use of public health resources 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.
"This funding will help us continue to develop novel algorithms for the effective simulation of large populations," explained Eubank. "Along with the information we have gained from our discussions with policymakers, we hope to improve our understanding of these network-based models, which will allow us to extend the use of our existing models to new areas, expand the range of questions to which models are applied, and improve communications about model outcomes."
"The work supported by this grant is being funded through the American Recovery and Reinvestment Act and one of its key objectives will be to look at new ways to contribute to the growth of the regional economy," said Eubank. "VBI and Virginia Tech are committed to making discoveries that improve people's lives in the Commonwealth of Virginia and beyond. We will therefore be contributing our expertise to approaches that have the potential to bring significant economic benefits to the wider community."
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."
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