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

    Users can view Ebola (or Flu) activity for the past four weeks and view forecast predictions for the next two weeks. They can also view forecast trends and compare them to surveillance data. EpiCaster allows users to see what impact various strategies, such as vaccines and social distancing, have on disease spread.

  • SIBEL

    SIBEL allows bioinformatics researchers to design experiments and create analysis for epidemiological disease studies based on realistic social network simulations. It enables improved readiness, planning, and decision making in the domains of public safety and national security by delivering sophisticated modeling and simulation capabilities directly into the hands of the analyst.

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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.

News

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 several levels of resolution - from national to individual. It supports our 2009 version of U.S. data and all international countries NDSSL has constructed, total population of 800+ million.

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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.

News

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

    Dynamics on networked populations are useful in understanding social processes on networked populations. Contagion dynamics can take various forms such as epidemics, social protest, smoking and drug use, use of social media, etc. EDISON utilizes big data and data mining to perform social dynamics on networks.

  • Granite

    This tool contains network analysis libraries to compute structural characteristics of networks. The libraries are GaLib, NetworkX, and SNAP.

  • GDS Calculator

    Agent-based simulations are used to understand disease transmission, the spread of social unrest, and the propagation a host of other contagions such as fads, rumors, and influence. Contagions may be spread, for example, by face-to-face interaction and/or electronic means (e.g., social media). Simulation is an effective way to study these dynamics of contagion spread.

  • Virus Tracker

    Virus Tracker simulates the spread of a virus and demonstrates the critical role of vaccinations in combating a disease outbreak. Designed to show how disease spreads, players can "infect" others with the “Zombie Virus" and can then return to a ‘Human’ state by getting vaccinated from 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.

Video




Tools

  • EpiCaster

    Users can view Ebola (or Flu) activity for the past four weeks and view forecast predictions for the next two weeks. They can also view forecast trends and compare them to surveillance data. EpiCaster allows users to see what impact various strategies, such as vaccines and social distancing, have on disease spread.

  • EpiViewer

    EpiViewer is a data repository for epidemiologists. Users can upload and compare Ebola forecasts and surveillance data from a variety of sources and see how forecasts change over time. Users can also load and share their own forecasting predictions.

  • EpiViz

    EpiViz is a highly dynamic system that provides a platform to track and study subjects’ decision making and information search strategies, under controlled and repeatable conditions using simulated disease outbreaks. Data visualization supports a multiple views environment and parallel simulation runs.

  • SIBEL

    SIBEL allows bioinformatics researchers to design experiments and create analysis for epidemiological disease studies based on realistic social network simulations. It enables improved readiness, planning, and decision making in the domains of public safety and national security by delivering sophisticated modeling and simulation capabilities directly into the hands of the analyst.

Projects

Contact

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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.

News

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 the Bacterial Bioinformatics Resource Center, an information system designed to support the biomedical research community’s work on bacterial infectious diseases via integration of vital pathogen information with rich data and analysis tools.

  • COPASI

    COPASI enables researchers to construct biochemical models that help them understand how a system works. Model parameters can be adjusted as necessary to reproduce experimental results. 

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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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Tools

  • TranSims

    The TRansportation ANalysis SIMulation System is a transportation planning and decision support tool capable of simulating second-by-second movements of every person and every vehicle through the transportation network of a large urban environment. TRANSIMS was the first successful example of high performance computational social sciences and policy informatics.

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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.

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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).

Video



Tools

  • My4Sight

    My4Sight uses human computation to enhance disease forecasting. Similar to games like Foldit, this web application allows users to assist computational models by performing tasks that humans are uniquely good at, in this case pattern matching.

  • Dynamic Behavior Visualizer

    Dynamic Behavior Visualizer (DBV) is an interactive visualization of people, a group of people, or a family used to help understand behaviors and movements over time during a natural or man-made disaster. DBV is used to study the resilience of critical infrastructures such as transportation, communication, and public health.

  • EDISON

    Dynamics on networked populations are useful in understanding social processes on networked populations. Contagion dynamics can take various forms such as epidemics, social protest, smoking and drug use, use of social media, etc. EDISON utilizes big data and data mining to perform social dynamics on networks.

  • Eyes on the Ground

    Road conditions can be variable in some of the rural areas in Western Africa. Eyes on the Ground allows people in affected areas to report their road conditions. Other travelers can then view these reports and plan their trips accordingly. This is especially useful when planning the delivery of patients and supplies between cities.

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