Overview


Simulation Science and Analytics

Policy promises to make our society safer, fairer, more efficient. But policy can only be as strong as the data that inform it. Unfortunately, vital information is often scattered across several disconnected systems, presenting a major obstacle to leaders looking to take a “big picture” approach to social issues. Our research bridges the gap between decision-makers and data, allowing societal systems to leverage an ever-expanding range of actionable information.

News

Selected Publications

  1. Parikh N, Hayatnagarkar H, Beckman R, Marathe M, Swarup S. A comparison of multiple behavior models in a simulation of the aftermath of an improvised nuclear detonation. Autonomous Agents and Multi-Agent Systems. 2016:1–27.  
  2. Barrett C, Eubank S, Marathe A, Marathe M, Swarup S. Synthetic Information Environments for Policy Informatics: A Distributed Cognition Perspective. Routledge Press; 2015. Governance in the Information Era: Theory and Practice of Policy Informatics.  http://www.routledge.com/books/details/9781138832084/
  3. Keith R. Bisset, Jiangzhuo Chen, Suruchi Deodhar, Xizhou Feng, Yifei Ma, Madhav V. Marathe (2014) Indemics: An interactive high-performance computing framework for data-intensive epidemic modeling. ACM Transactions on Modeling and Computer Simulation (TOMACS), 24(1): 32.
  4. Keith R. Bisset, Jiangzhuo Chen, Xizhou Feng, V.S.Anil Kumar, Madhav V. Marathe (2009) EpiFast: A fast algorithm for large scale realistic epidemic simulations on distributed memory systems. In Proceedings of 23rd ACM International Conference on Supercomputing (ICS'09), 430-439.

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Tools

  • EpiCaster

    EpiCaster provides near-real-time forecasts of epidemics, combining publicly available disease surveillance data with high-resolution synthetic populations and epidemic simulations.

  • SIBEL

    SIBEL allows researchers to model the effects of epidemics on a realistic synthetic population. This app supports effective public health decision-making by delivering sophisticated simulation capabilities directly into the hands of analysts.

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Global Synthetic Information Systems

Synthetic information is a powerful form of data integration that allows us to simulate systems as simple as a cell or as complex as a city. Leveraging a rich library of population data, our synthetic information system is now global in scale. With a “virtual laboratory” that extends across the entire planet, our analyses can guide real-time responses to emerging crises or simulate “what-if” scenarios to support long-term policy planning.

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Selected Publications

  1. Nidhi K. Parikh, Mina Youssef, Samarth Swarup, Stephen G. Eubank (2013) Modeling the effect of transient populations on epidemics in Washington DC. Scientific Reports, 3(3152). 10.1038/srep03152
  2. Christopher L. Barrett, Richard Beckman, Maleq Khan, Vullikanti S. Anil Kumar, Madhav V. Marathe, Paula Stretz, Tridib Dutta, Bryan L. Lewis (2009) Generation and analysis of large synthetic social contact networks. In Proceedings of Winter Simulation Conference (WSC), 1003-1014.
  3. Huadong Xia, Kalyani S. Nagaraj, Jiangzhuo Chen, Madhav V. Marathe (2015) Synthesis of a High Resolution Social Contact Network for Delhi With Application to Pandemic Planning. Journal of Artificial Intelligence in Medicine, 65(2): 113-30. 

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Tools

  • Synthetic Information Viewer

    Synthetic Information Viewer (SIV) is a synthetic population visualization tool that allows users to explore demographic information at various levels of resolution - from a global library of over 800 million people down to a refined set of individuals.

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Network Science

From social media to transportation infrastructure, public policy to individualized healthcare, the networks we rely on for everyday life are increasingly interconnected. With so many moving pieces to consider, it can be difficult to identify the source of a system-wide crisis and find the most effective solution. Our research simplifies network analysis, offering flexible tools to explain, predict, and visualize the behavior of massively interacting systems.

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Selected Publications

  1. Stephen G. Eubank, Hasan Guclu, Vullikanti S. Anil Kumar, Madhav V. Marathe, Aravind Srinivasan, Zoltan Toroczkai, Nan Wan (2004) Modelling disease outbreaks in realistic urban social networks. Nature, 429(6988): 180-184.
  2. Adiga A, Kuhlman C, Mortveit H, Kumar VS Anil. Sensitivity of diffusion dynamics to network uncertainty. Journal of Artificial Intelligence Research. 2014;51:207–226. 
  3. Jose Cadena, Gizem Korkmaz, Christopher J. Kuhlman, Achla Marathe, Naren Ramakrishnan, Vullikanti S. Anil Kumar (2015) Forecasting Social Unrest Using Activity Cascades. PLoS One, 10(6): e0128879.
  4. Abdelhamid S, Alam M, Alo R, Arifuzzaman S, Beckman P, Bhattacharjee T, Bhuiyan H, Bisset K, Eubank S, Esterline A, Fox E, Fox G, Hasan S, Hayatnagarkar H, Khan M, Kuhlman C, Marathe M, Meghanathan N, Mortveit H, Qiu J, Ravi S, Shams Z, Sirisaengtaksin O, Swarup S, Vullikanti A, Wu T (2014) CINET 2.0: A CyberInfrastructure for Network Science. In The 10th IEEE International Conference on eScience, 2014., 324-331. 

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Tools

  • EDISON

    EDISON uses big data and data mining techniques to analyze how various social dynamics impact the behavior of large networks. These factors can range from disease epidemics to drug use to the emergence of political protests.

  • Granite

    Granite is a supplementary tool in the CINET suite, designed to help users in academia and industry gain an in-depth understanding of complex networks. Its user-friendly interface helps simplify many of the computationally intense tasks inherent in network science.

  • GDS Calculator

    GDSCalc is a supplementary tool in the CINET suite, designed to help researchers study how various "contagions" spread through social networks, including fads, rumors, influence, disease, and political unrest.

  • Virus Tracker

    Virus Tracker is an educational program that simulates the spread of a virus, demonstrating the critical role of vaccinations in combating disease outbreaks. Designed to show how disease spreads, players can "infect" others with the “Zombie Virus" or score big points by vaccinating other players.

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Public Health

In the face of a public health crisis, knowledge is power. Our predictive modeling tools allow policy-makers to quickly identify effective response strategies and distribute their findings across the public sector. NDSSL technology has a proven track record, providing decision support to key government agencies through a number of recent epidemics: H1N1 in the US, cholera in Haiti and Ebola in western Africa.

News

Selected Publications

  1. Jiangzhuo Chen, Shuyu Chu, Youngyun Chungbaek, Maleq Khan, Christopher Kuhlman, Achla Marathe, Henning Mortveit, Anil Vullikanti, Dawen Xie. Effect of Modeling Slum Populations on Influenza Spread in Delhi. BMJ Open, 2016.
  2. Yi M, Marathe A. Fairness versus Efficiency of Vaccine Allocation Strategies. Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Resear. 2015;18(2):278–283.  http://www.sciencedirect.com/science/article/pii/S1098301514047718
  3. Caitlin M. Rivers, Eric T. Lofgren, Madhav V. Marathe, Stephen G. Eubank, Bryan L. Lewis (2014) Modeling the Impact of Interventions on an Epidemic of Ebola in Sierra Leone and Liberia. PLOS Currents Outbreaks.
  4. M. E Halloran, N. M. Ferguson, Stephen G. Eubank, I. M. Longini, D.A.T. Cummings, Bryan L. Lewis, S. Xu, C. Fraser, Vullikanti S. Anil Kumar, T. C. Germann, D. Wagener, Richard J. Beckman, K. Kadau, Christopher L. Barrett, C. A. Macken, D. S. Burke, P. Cooley (2008) Modeling targeted layered containment of an influenza pandemic in the United States. Proceedings of the National Academy of Sciences (PNAS), 105(12): 4639-4644.

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Tools

  • EpiCaster

    EpiCaster provides near-real-time forecasts of epidemics, combining publicly available disease surveillance data with high-resolution synthetic populations and epidemic simulations.

  • EpiViewer

    EpiViewer is a data repository for epidemiologists, allowing users to upload, share, and compare disease forecasts from a variety of sources and observe how they change over time.

  • SIBEL

    SIBEL allows researchers to model the effects of epidemics on a realistic synthetic population. This app supports effective public health decision-making by delivering sophisticated simulation capabilities directly into the hands of analysts.

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Systems Biology

The wealth of biological data circulating among medical providers, government agencies, and communications networks could transform how we promote health in our communities. Our team develops a range of user-friendly tools to help researchers make the most of this information: from a comprehensive database of drug-resistant pathogens to a simulated immune system that allows scientists to identify new treatments for chronic diseases.

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Selected Publications

  1. Md. Maksudul Alam, Xinwei Deng, Cassandra Philipson, Josep Bassaganya-Riera, Keith R. Bisset, Adria Carbo, Stephen G. Eubank, Raquel Hontecillas, Stefan Hoops, Yongguo Mei, Vida Abedi, Madhav V. Marathe (2015) Sensitivity Analysis of an ENteric Immunity SImulator (ENISI)-based Model of Immune Responses to Helicobacter pylori Infection. PLOS One, 10(9): e0136139.
  2. Wattam, A. R., Abraham, D., Dalay, O., Disz, T. L., Driscoll, T., Gabbard, J. L., Sobral, B. W. (2014). PATRIC, the bacterial bioinformatics database and analysis resource. Nucleic Acids Research, 42 (Database issue), D581–D591.  
  3. Mendes P, Hoops S, Sahle S, Gauges R, Dada J, Kummer U. Computational modeling of biochemical networks using COPASI. Methods Mol Biol. 2009;500:17–59.  

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Tools

  • PATRIC

    PATRIC is an information system designed to support the biomedical research community’s work on bacterial infectious diseases. This resource combines detailed information on bacterial genomes with a full suite of analysis tools.

  • COPASI

    COPASI allows researchers to model the dynamics of any biochemical network, providing a "virtual laboratory" to test new hypotheses or reproduce the results of real-life experiments. 

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Infrastructure, Security, and Resilience

By 2050, an estimated 70 percent of the world’s population will live in cities. Our lab is helping policy-makers keep pace with the rapid rate of urbanization, developing simulation systems that can identify ways to make massive infrastructure networks safer, more sustainable, and resilient to environmental stress. NDSSL researchers partner directly with government agencies to solve pressing challenges in telecommunications, transportation and energy systems.

News

Selected Publications

  1. Barrett C, Centeno V, Eubank S, Evrenosoglu C, Marathe A, Marathe M, Mishra C, Mortveit H, Pal A, Phadke A, Thorp J, Kumar VS Anil, Youssef M (2016) Impact of a surface nuclear blast on the transient stability of the power system. Critical Information Infrastructures Security. Springer International Publishing. ISBN: 10.1007/978-3-319-31664-2 16. 
  2. Beckman R, Channakeshava K, Huang F, et al. Integrated Multi-Network Modeling Environment for Spectrum Management. IEEE Journal on Selected Areas in Communication, special issue on Network Science. 2013;31(6):1158–1168.
  3. Pei G, Parthasarathy S, Srinivasan A, Kumar VS Anil. Approximation algorithms for throughput maximization in wireless networks with delay constraints. IEEE/ACM Transactions on Networking. 2013;21(6):1988–2000.
  4. Bo Han, Pan Hui, Vullikanti S. Anil Kumar, Madhav V. Marathe, Jianhua Shao, A. Srinivasan (2012) Mobile Data Offloading through Opportunistic Communications and Social Participation. IEEE Transactions on Mobile Computing, 11(5): 821-834.

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Social and Behavioral Modeling

When does civil unrest lead to public protest? Why does incarceration spread through our communities like an epidemic? To answer questions like these, simulation science needs to do more than crunch numbers. Our team develops computational models that can account for complex social phenomena, giving decision-makers a sophisticated toolset to predict how policy will influence human behavior.

News

Selected Publications

  1. Kristian Lum, Samarth Swarup, Stephen G. Eubank, James Hawdon (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).
  2. Ming Yi, Achla Marathe (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.
  3. Stephen G. Eubank, Vullikanti S. Anil Kumar, Madhav V. Marathe, A. Srinivasan, N. Wang (2006) Structure of social contact networks and their impact on epidemics. In AMS-DIMACS Special Volume on Epidemiology (pp. 181-213).

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Tools

  • My4Sight

    My4Sight uses crowdsourced data and human computation to enhance disease forecasting. Users can evaluate data provided by other disease experts, seamleslly integrating their feedback into future forecasting methods.

  • Dynamic Behavior Visualizer

    This tool allows analysts to observe probable patterns of public behavior during a natural or man-made disaster. DBV provides insight into how critical infrastructures can support populations throughout a period of crisis.

  • EDISON

    EDISON uses big data and data mining techniques to analyze how various social dynamics impact the behavior of large networks. These factors can range from disease epidemics to drug use to the emergence of political protests.

  • Eyes on the Ground

    This application allows individuals across the globe to quickly and easily report adverse road conditions in their area. These data can be used to help policymakers identify the most efficient routes for delivering supplies and personnel during an emergency.

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