These are the people who would show up first for treatment if indeed a chemical or biological attack had occurred. They also would serve as the subpopulation to seed our epidemiological simulations. Simfrastructure calls data mining tools and Simdemics to achieve these tasks.
Biases in their demographics compared to a random sample of the population will induce persistent biases in the set of people infected at any time. We estimated the demand at hospitals, assuming that people would arrive at a hospital near their home or current location. We also estimated the demographics of casualties under an alternative scenario during only a heat wave. Historically, the most likely casualties of a heat wave are elderly people living alone with few activities outside the home.
This information, combined with demographic and household structure data, allowed us to estimate demand for health services created by the heat wave by demographic and location. For situation assessment, we noted the obvious differences between these two demand patterns. In an actual event, comparison with admissions surveillance data would allow quick disambiguation between the two situations. We estimated the likely spread of disease for several different pathogens by demographic and location. Furthermore, we implemented several suggested mitigating responses, such as closing schools and/or workplaces, or quarantining households with symptomatic people. Knowledge of the household structure permits an exceptionally realistic representation of the consequences of these actions. For example, if schools are closed, a care-giver will also need to stay home in many households.
We described our work in progress that aims to build a scalable CI to study large socio-technical networked systems. The goal of the CI is to provide seamless access to HPC-based modeling and analysis capability for routine analytical efforts. It consists of (i) high-resolution models, tools for decision making, and consequence analysis, (ii) service-oriented architecture and delivery mechanism for facilitating the use of these models by domain experts, (iii) distributed coordinating architecture for information fusion, model execution and data processing, and (iv) scalable methods for visual and data analytics to support analysts.
Due to space considerations, we have not discussed peta-scale computing and data grids that will serve as the underlying technology. Much remains to be done to develop the CI. Researchers across the world are developing new tools in web services, tools and CI for various problem domains 9 10 11. We hope to build on these advances.
2Barrett, C., Eubank, S., Anil Kumar, V., Marathe, M. “Understanding Large Scale Social and Infrastructure Networks: A Simulation Based Approach,” SIAM news, March 2004, Appears as part of Math Awareness Month on The Mathematics of Networks.
3“Capturing Complexity Through Agent-Based Modelling,” Special Issue of the Proceedings of the National Academy of Sciences, Vol. 99 (suppl. 3), 2002.
4US-Canada Power System Outage Task Force. Final Report on the August 14, 2003 Blackout in the United States and Canada: Causes and Recommendations April 2004. www.ksg.harvard.edu/hepg/Blackout.htm
5Barrett, C. L., Bisset, K., Eubank, S., Kumar, V. S. A., Marathe, M. V., Mortveit, H. S. “Modeling and Simulation of Large Biological, Information and Soci-Technical Systems: An Interaction-Based Approach,” Proc. Short Course on Modeling and Simulation of Biological Networks, AMS Lecture Notes, Series: PSAPM, 2007.
6Barrett, C., Eubuank, S., Marathe, M. “Modeling and Simulation of Large Biological, Information and Socio-Technical Systems: An Interaction Based Approach,” in Interactive Computation: The New Paradigm, D. Goldin, S. Smolka and P. Wegner Eds. Springer Verlag, (2005).
7 Eubank, S., Guclu, H., Anil Kumar, V. S., Marathe, M., Srinivasan, A., Toroczkai, Z., Wang, N. “Modeling Disease Outbreaks in Realistic Urban Social Networks,” Nature, 429, pp. 180-184 May (2004).
8Barrett, C., Mortveit, H., Reidys, C. "Elements of a Theory of Computer Simulation: III," Applied Mathematics and Computation, pp. 325–340, (2001).
9Fox, G. “Grids Challenged by a Web 2.0 and Multicore Sandwich,” CCGrid 2007, Keynote Talk. Rio de Janeiro, Brazil, May 15, 2007.
10Hayden, L., Fox, G., Gogineni, P. “Cyberinfrastructure for Remote Sensing of Ice Sheets,” Proceedings of TeraGrid 2007 Conference, Madison, Wisconsin, June 4-8 2007.
11Grid Computing: Making the Global Infrastructure a Reality, edited by Fran Berman, Geoffrey Fox and Tony Hey. Wiley Publishers, March 2003.
12Atkins, K., Barrett, C., Beckman, R., Bisset, K., Chen, J., Eubank, S., Anil Kumar, V. S., Lewis, B., Macauley, M., Marathe, A., Marathe, M., Mortveit, H., Stretz, P. “Simulated Pandemic Influenza Outbreaks in Chicago,” NIH DHHS Study Final report, 2006.
13Barrett, C., Smith, J. P., Eubank, S. “Modern Epidemiology Modeling,” Scientific American, March (2005).
14Barrett, C., Beckman, R., Berkbigler, K., Bisset, K., Bush, B., Campbell, K., Eubank, S., Henson, K., Hurford, J., Kubicek, D., Marathe, M., Romero, P., Smith, J., Smith, L., Speckman, P., Stretz, P., Thayer, G., Eeckhout, E., Williams, M. D. “TRANSIMS: Transportation Analysis Simulation System,” Technical Report LA-UR-00-1725, Los Alamos National Laboratory Unclassified Report, 2001. An earlier version appears as a 7 part technical report series LA-UR- 99-1658 and LA-UR-99-2574 to LA-UR-99-2580.
15Bailey-Kellog, C., Ramakrishnan, N., Marathe, M. "Spatial data mining to support pandemic preparedness," SIGKDD Explorations 8: 80-82, 2006.
16Barrett, C., Bisset, K., Eubank, S., Fox, E., Ma, Y., Marathe, M., Zhang, X. "A Scalable Data Management Tool to Support Epidemiological Modeling of Large Urban Regions," European Conference on Digital Libraries (ECDL), pp. 546-548, 2007.
17Gelernter, D., Carriero, N. “Coordination Languages and their significance,” CACM, 1992, 35(2).
18Gelernter, D., Carriero, N. “Linda in Context,” CACM, 1989, 32(4).
19Atkins, K., Barrett, C., Beckman, R., Bisset, K., Chen, J., Eubank, S., Anil Kumar, V. S., Lewis, B., Marathe, A., Marathe, M., Mortveit, H., Stretz, P. “An analysis of layered public health interventions at Ft. Lewis and Ft. Hood during a pandemic influenza event," TR-NDSSL-07-019, 2007.
