Synthetic Information Systems for Better Informing Public Health Policymakers

We develop tools that assist public health decision makers address issues related to surveillance and detection, dynamics of infectious diseases, response strategies and behavior.

Project Summary

NDSSL leads a research program as part of the Models of Infectious Disease Agent Study (MIDAS), which is a collaboration of research and informatics groups that develop computational models of the interactions between infectious agents and their hosts, disease spread, prediction systems and response strategies. The models can be useful to policymakers, public health workers and other researchers who want to better understand and respond to emerging infectious diseases. If a disease outbreak occurs, the MIDAS network may be called upon to develop specific models to aid public officials in their decision-making processes. In particular, the NDSSL research group designs, builds and validates models of disease spread and prediction systems based on activities in a social network. Using mathematical and computational methods, the group is exploring the effects of human contact patterns in urban areas on disease transmission dynamics and the effectiveness of particular response strategies.

The MIDAS project is funded by the National Institutes of Health and National Institute of General Medical Sciences - Models of Infectious Disease Agent Study Grant 5U01GM070694-11

Team

  • Stephen Eubank, Professor and Deputy Director, Biocomplexity Institute of Virginia Tech and Population Health Sciences
  • Madhav Marathe, Professor and Director, Biocomplexity Institute of Virginia Tech and Computer Science
  • Chris Barrett, Professor and Executive Director, Biocomplexity Institute of Virginia Tech and Computer Science
  • Achla Marathe, Professor, Biocomplexity Institute of Virginia Tech and Agricultural and Applied Economics
  • Keith Bisset, Simulation and Systems Software Development Scientist, Biocomplexity Institute of Virginia Tech
  • Bryan Lewis, Research Assistant Professor, Biocomplexity Institute of Virginia Tech
  • Anil Vullikanti, Associate Professor, Biocomplexity Institute of Virginia Tech and Computer Science
  • Henning Mortveit, Associate Professor, Biocomplexity Institute of Virginia Tech and Mathemathics
  • Samarth Swarup, Research Assistant Professor, Biocomplexity Institute of Virginia Tech and Computer Science
  • Jiangzhuo Chen, Lead Computational Scientist, Biocomplexity Institute of Virginia Tech
  • Kathy Alexander, Associate Professor, Fish and Wildlife Conservation

Recent Publications

Yi M, Marathe A (2015) Fairness versus Efficiency of Vaccine Allocation Strategies. Value in health: The Journal of the International Society for Pharmacoeconomics and Outcomes Research, 18(2): 278–283.

Marathe A, Chen J, Eubank S, Liao S, Ma Y (2014) Impact of Paid Sick Leave Policy: A Social Planner's Perspective. American Journal of Public Health, 104(1): e1-e1.

Swarup S, Eubank S, Marathe M (2014) Computational Epidemiology as a Challenge Domain for Multiagent Systems. In Proceedings of The Thirteenth International Conference on Autonomous Agents and Multiagent Systems (AAMAS).

Lum K, Swarup S, Eubank S, Hawdon J (2014) The Contagious Nature of Imprisonment: An agent-based model to explain racial disparities in incarceration rates. Journal of the Royal Society Interface, 11(20140409).

Bisset K, Chen J, Deodhar S, Feng X, Ma Y, Marathe M (2014) Indemics: An interactive high-performance computing framework for data-intensive epidemic modeling. ACM Transactions on Modeling and Computer Simulation (TOMACS), 24(1): 32.

Lau E, Zheng J, Tsang T, Liao Q, Lewis B, Brownstein J, Sanders S, Wong J, Mekaru S, Rivers C, Wu P, Jiang H, Li Y, Yu J, Zhang Q, Chang Z, Liu F, Peng Z, Leung G, Feng L, Cowling B, Yu H (2014) Accuracy of epidemiological inferences based on publicly available information: retrospective comparative analysis of line lists of human cases infected with influenza A(H7N9) in China. BMC Medicine, 12(88).

Parikh N, Youssef M, Swarup S, Eubank S (2013) Modeling the effect of transient populations on epidemics in Washington DC. Scientific Reports, 3(3152).

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