An Interactive, Web-Based High Performance Modeling Environment for Computational Epidemiology

We present an integrated interactive modeling environment to support public health epidemiology. The environment combines a high resolution individual-based model with a user-friendly Web-based interface that allows analysts to access the models and the analytics backend remotely from a desktop or a mobile device. The environment is based on a loosely coupled service-oriented-architecture that allows analysts to explore various counterfactual scenarios. As the modeling tools for public health epidemiology are getting more sophisticated, it is becoming increasingly difficult for noncomputational scientists to effectively use the systems that incorporate such models. Thus an important design consideration for an integrated modeling environment is to improve ease of use such that experimental simulations can be driven by the users. This is achieved by designing intuitive and user-friendly interfaces that allow users to design and analyze a computational experiment and steer the experiment based on the state of the system. A key feature of a system that supports this design goal is the ability to start, stop, pause, and roll back the disease propagation and intervention application process interactively. An analyst can access the state of the system at any point in time and formulate dynamic interventions based on additional information obtained through state assessment. In addition, the environment provides automated services for experiment set-up and management, thus reducing the overall time for conducting end-to-end experimental studies. We illustrate the applicability of the system by describing computational experiments based on realistic pandemic planning scenarios. The experiments are designed to demonstrate the system’s capability and enhanced user productivity.

[1]  Michele Catanzaro,et al.  Dynamical processes in complex networks , 2008 .

[2]  Madhav V. Marathe,et al.  ISIS: a networked-epidemiology based pervasive web app for infectious disease pandemic planning and response , 2014, KDD.

[3]  L. A. Rvachev,et al.  A mathematical model for the global spread of influenza , 1985 .

[4]  Benjamin J. Cowling,et al.  School Closure and Mitigation of Pandemic (H1N1) 2009, Hong Kong , 2010, Emerging infectious diseases.

[5]  Stephen Eubank,et al.  Scalable, efficient epidemiological simulation , 2002, SAC '02.

[6]  Mathieu Bastian,et al.  Gephi: An Open Source Software for Exploring and Manipulating Networks , 2009, ICWSM.

[7]  Shawn T. Brown,et al.  FRED (A Framework for Reconstructing Epidemic Dynamics): an open-source software system for modeling infectious diseases and control strategies using census-based populations , 2013, BMC Public Health.

[8]  M. Newman,et al.  Percolation and epidemics in a two-dimensional small world. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[9]  Jon Parker,et al.  A Distributed Platform for Global-Scale Agent-Based Models of Disease Transmission , 2011, TOMC.

[10]  Madhav V. Marathe,et al.  Indemics: an interactive data intensive framework for high performance epidemic simulation , 2010, ICS '10.

[11]  M. Keeling,et al.  Networks and epidemic models , 2005, Journal of The Royal Society Interface.

[12]  Aravind Srinivasan,et al.  Modelling disease outbreaks in realistic urban social networks , 2004, Nature.

[13]  Harry B. Hunt,et al.  Predecessor existence problems for finite discrete dynamical systems , 2007, Theor. Comput. Sci..

[14]  Nedialko B. Dimitrov,et al.  Mathematical Approaches to Infectious Disease Prediction and Control , 2010 .

[15]  Harry B. Hunt,et al.  Complexity of reachability problems for finite discrete dynamical systems , 2006, J. Comput. Syst. Sci..

[16]  N. Ferguson,et al.  Planning for smallpox outbreaks , 2003, Nature.

[17]  Aurélien Naldi,et al.  Diversity and Plasticity of Th Cell Types Predicted from Regulatory Network Modelling , 2010, PLoS Comput. Biol..

[18]  Madhav V. Marathe,et al.  Simfrastructure: A Flexible and Adaptable Middleware Platform for Modeling and Analysis of Socially Coupled Systems , 2013, 2013 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing.

[19]  Madhav V. Marathe,et al.  EpiFast: a fast algorithm for large scale realistic epidemic simulations on distributed memory systems , 2009, ICS.

[20]  Madhav V. Marathe,et al.  Indemics , 2014, ACM Trans. Model. Comput. Simul..

[21]  Bin Yu,et al.  Gryphon: A Hybrid Agent-Based Modeling and Simulation Platform for Infectious Diseases , 2010, SBP.

[22]  James Sexton,et al.  Enabling High-Performance Computing as a Service , 2012, Computer.

[23]  Madhav V. Marathe,et al.  EpiSimdemics: An efficient algorithm for simulating the spread of infectious disease over large realistic social networks , 2008, 2008 SC - International Conference for High Performance Computing, Networking, Storage and Analysis.

[24]  Alessandro Vespignani,et al.  Epidemics and immunization in scale‐free networks , 2002, cond-mat/0205260.

[25]  T. Geisel,et al.  Forecast and control of epidemics in a globalized world. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[26]  Dennis L. Chao,et al.  FluTE, a Publicly Available Stochastic Influenza Epidemic Simulation Model , 2010, PLoS Comput. Biol..

[27]  N. Ferguson,et al.  Closure of schools during an influenza pandemic , 2009, The Lancet Infectious Diseases.

[28]  C. Macken,et al.  Mitigation strategies for pandemic influenza in the United States. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[29]  D. Cummings,et al.  Strategies for mitigating an influenza pandemic , 2006, Nature.

[30]  Theresa-Marie Rhyne,et al.  Epinome: A Visual-Analytics Workbench for Epidemiology Data , 2012, IEEE Computer Graphics and Applications.

[31]  L. Meyers Contact network epidemiology: Bond percolation applied to infectious disease prediction and control , 2006 .

[32]  Alessandro Vespignani,et al.  The GLEaMviz computational tool, a publicly available software to explore realistic epidemic spreading scenarios at the global scale , 2011, BMC infectious diseases.

[33]  Madhav V. Marathe,et al.  Formal Specification and Experimental Analysis of an Interactive Epidemic Simulation Framework , 2011, 2011 IEEE International Conference on High Performance Computing and Communications.

[34]  Vladimir Batagelj,et al.  Pajek - Program for Large Network Analysis , 1999 .

[35]  Alessandro Vespignani,et al.  Dynamical Processes on Complex Networks , 2008 .

[36]  Reza Yaesoubi,et al.  Dynamic Health Policies for Controlling the Spread of Emerging Infections: Influenza as an Example , 2011, PloS one.