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

Abstract

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.

References

1. Abdelbaky, M., Parashar, M., Kim, H., Jordan, K. E., Sachdeva, V., Sexton, J., Jamjoom, H., Shae, Z.-Y., Pencheva, G., Tavakoli, R., and Wheeler, M. F. 2012. Enabling high-performance computing as a service. Comput. 45, 72--80.
2. Barrat, A., Barthelemy, M., and Vespignani, A. 2008. Dynamical Processes in Complex Networks. Cambridge University Press.
3. Barrett, C., Hunt III, H. B., Marathe, M. V., Ravi, S. S., Rosenkrantz, D. J., Stearns, R. E., and Thakur, M. 2007. Predecessor existence problems for finite discrete dynamical systems. Theor. Comput. Sci. 386, 1--2, 3--37.
4. Barrett, C. L., Bisset, K. R., Eubank, S. G., Feng, X., and Marathe, M. V. 2008. EpiSimdemics: An efficient algorithm for simulating the spread of infectious disease over large realistic social networks. In Proceedings of the ACM/IEEE Conference on Supercomputing.
5. Barrett, C. L., Hunt III, H. B., Marathe, M. V., Ravi, S., Rosenkrantz, D. J., and Stearns, R. E. 2006. Complexity of reachability problems for finite discrete dynamical systems. J. Comput. Syst. Sci. 72, 8, 1317--1345.
6. Bastian, M., Heymann, S., and Jacomy, M. 2009. Gephi: An open source software for exploring and manipulating networks. In Proceedings of the AAAI Conference.
7. Batagelj, V. and Mrvar, A. 1998. Pajek-program for large network analysis. Connections 21, 2, 47--57.
8. Beckman, R., Bisset, K. R., Chen, J., Lewis, B., Marathe, M., and Stretz, P. 2014. ISIS: A networked-epidemiology based pervasive Web app for infectious disease pandemic planning and response. In Proceedings of the 20th ACM SKDD Conference on Knowledge Discovery and Data Mining.
9. Bisset, K. R., Chen, J., Deodhar, S., Feng, X., Ma, Y., and Marathe, M. V. 2014. Indemics: An interactive high-performance computing framework for data-intensive epidemic modeling. ACM Trans. Model. Comput. Simul. 24, 1, 1--32.
10. Bisset, K. R., Chen, J., Feng, X., Kumar, V. S. A., and Marathe, M. V. 2009. EpiFast: A fast algorithm for large-scale realistic epidemic simulations on distributed memory systems. In Proceedings of the 23rd International Conference on Supercomputing. 430--439.
11. Bisset, K. R., Chen, J., Feng, X., Ma, Y., and Marathe, M. V. 2010. Indemics: An interactive data intensive framework for high performance epidemic simulation. In Proceedings of the 24rd International Conference on Conference on Supercomputing. 233--242.
12. Bisset, K. R., Deodhar, S., Makkapati, H., Marathe, M. V., Stretz, P., and Barrett, C. L. 2013. Simfrastructure: A flexible and adaptable middleware platform for modeling and analysis of socially coupled systems. In Proceedings of the IEEE International Symposium on Cluster Computing and the Grid. 506--513.
13. Broeck, W., Gioannini, C., Goncalves, B., Quaggiotto, M., Colizza, V., and Vespignani, A. 2011. The gleamviz computational tool, a publicly available software to explore realistic epidemic spreading scenarios at the global scale. BMC Infectious Diseases 11, 1, 37.
14. Cauchemez, S., Ferguson, N. M., Wachtel, C., Tegnell, A., Saour, G., Duncan, B., and Nicoll, A. 2009. Closure of schools during an influenza pandemic. Lancet Infectious Diseases 9, 8, 473--481.
15. Chai, D. L., Halloran, M. E., Obenchain, V. J., and Longini Jr., I. M. 2010. Flute, a publicly available stochastic influenza epidemic simulation model. PLoS Comput. Biol. 6, 1.
16. CNN. 2009. 31 New York schools closed as flu spreads. http://www.cnn.com/2009/HEALTH/05/21/ny.flu.schools/.
17. Edlund, S. and Kaufman, J. 2012. STEM: Spatio-temporal epidemiological modeler. http://www.eclipse.org/stem/.
18. Eubank, S. G. 2002. Scalable, efficient epidemiological simulation. In Proceedings of ACM Symposium on Applied Computing. 139--145.
19. Eubank, S. G., Guclu, H., Kumar, V. S. A., Marathe, M. V., Srinivasan, A., Toroczkai, Z., And Wang, N. 2004. Modelling disease outbreaks in realistic urban social networks. Nature 4, 180--184.
20. Ferguson, N. M., Cummings, D. A. T., Fraser, C., Cajka, J. C., Cooley, P. C., and Burke, D. S. 2006. Strategies for mitigating an influenza pandemic. Nature 442, 448--452.
21. Ferguson, N. M., Keeling, M. J., Edmunds, W. J., Gani, R., Grenfell, B. T., Anderson, R. M., and Leach, S. 2003. Planning for smallpox outbreaks. Nature 425, 681--685.
22. Germann, T. C., Kadau, K., Longini, I. M., and Macken, C. A. 2006. Mitigation strategies for pandemic influenza in the United States. Proc. Nat. Acad. Sci. 103, 15, 5935--5940.
23. Grefenstette, J. J., Brown, S. T., Rosenfeld, R. et al. 2013. FRED (A Framework for Reconstructing Epidemic Dynamics): An open-source software system for modeling infectious diseases and control strategies using census-based populations. BMC Public Health 13, 1, 940.
24. Hufnagel, L., Brockmann, D., and Geisel, T. 2004. Forecast and control of epidemics in a globalized world. Proc. Nat. Acad. Sci. 101, 15124--15129.
25. Keeling, M. J. and Eames, K. T. D. 2005. Networks and epidemic models. J. R. Soc. Interface 2, 295.
26. Livnat, Y., Rhyne, T., and Samore, M. 2012. Epinome: A visual-analytics workbench for epidemiology data. IEEE Comput. Graph. Appl. 32, 2, 89--95.
27. Ma, Y., Bisset, K. R., Chen, J., Deodhar, S., and Marathe, M. V. 2011. Formal specification and experimental analysis of an interactive epidemic simulation framework. In Proceedings of HPCC. 790--795.
28. Meyers, L. A. 2007. Contact network epidemiology: Bond percolation applied to infectious disease prediction and control. Bull. Amer. Math. Soc. 44, 63--86.
29. Meyers, L. A. and Dimitrov, N. 2010. Mathematical approaches to infectious disease prediction and control. INFORMS, Tutor. Oper. Res.
30. Newman, M., Jensen, I., and Ziff, R. 2002. Percolation and epidemics in a two-dimensional small world. Phys. Rev. E 65, 021904.
31. Parker, J. and Epstein, J. M. 2012. A distributed platform for global-scale agent-based models of disease transmission. ACM Trans. Model. Comput. Simul. 22, 1.
32. Pastor-Satorras, R. and Vespignani, A. 2002. Epidemics and immunization in scale-free networks. In Handbook of Graphs and Networks, S. Bornholdt and H. G. Schuster Eds., Wiley-VCH, Berlin.
33. Rvachev, L. A. and Longini, I. M. 1985. A mathematical model for the global spread of influenza. Math. Biosciences 17, 3--22.
34. TIME. 2009. CDC says H1N1 outbreak shouldn’t close schools. http://www.time.com/time/health/article/0,8599,1915244,00.html.
35. Wu, J. T., Cowling, B. J., Lau, E. H. Y. et al. 2010. School closure and mitigation of pandemic (H1N1) 2009, Hong Kong. Emerg. Infectious Disease 16, 3, 538--541.
36. Yaesoubi, R. and Cohen, T. 2011. Dynamic health policies for controlling the spread of emerging infections: Influenza as an example. PLoS ONE 6, 9, e24043.
37. Yu, B., Wang, J., McGowan, M., Vaidyanathan, G., and Younger, K. 2010. Gryphon: A hybrid agent-based modeling and simulation platform for infectious diseases. Advances Social Comput., 199--207.

Index Terms

• Computing methodologies

  • Modeling and simulation

• Software and its engineering