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Planning and Response in the Aftermath of a Large Crisis: An Agent-Based Informatics Framework

Planning and Response in the Aftermath of a Large Crisis: An Agent-Based Informatics Framework.

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Planning and Response in the Aftermath of a Large Crisis: An Agent-Based Informatics Framework

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  1. Planning and Response in the Aftermath of a Large Crisis: An Agent-Based Informatics Framework Chris Barrett, Keith Bisset, Shridhar Chandan, Jiangzhuo Chen, Youngyun Chungbaek, Stephen Eubank, Yaman Evrenosoglu, Bryan Lewis, Kristian Lum, Achla Marathe, Madhav Marathe, Henning Mortveit, Nidhi Parikh, Arun Phadke, Jeffrey Reed, Caitlin Rivers, Sudip Saha, Paula Stretz, Samarth Swarup, James Thorp, Anil Vullikanti, Dawen Xie Winter Simulation Conference December 10th, 2013

  2. Presentation outline • Introduction • Large scale human initiated crisis • Our contributions • Agent based informatics framework • Synthetic information system • Computational architecture • Studies and results • Major insights • Study: effects of communication availability and behavior on health outcomes • Conclusion

  3. Presentation outline • Introduction • Large scale human initiated crisis • Our contributions • Agent based informatics framework • Synthetic information system • Computational architecture • Studies and results • Major insights • Study: effects of communication availability and behavior on health outcomes • Conclusion

  4. Large scale crises • Natural disasters • Freezing rain in metro DC on Sunday (Dec. 8th, 2013) • This hotel lost Internet access for the whole Monday! • How did it affect your behavior? • Larger scale crises: hurricane, earthquake, tsunami, … • Human initiated crises: our focus in this work • E.g. major terrorist attacks • We have limited understanding of them • Important to be prepared for them • Our focus in this work; but our framework also applies to natural crises • We rely on computer simulations in studying them and developing response plans for them

  5. National Planning Scenario 1 (NPS1) • Detonation of a 10 kt improvised nuclear device (IND) • Location: • -16th and K street • - Washington DC • Time: 11:15 EDT • Date: May 15, 2006 • Published by Department of Homeland Security

  6. NPS1: Damages • Roadways are filled with rubble. • Cell towers within 0.6 miles of GZ are destroyed. • A large area around GZ suffers a long term blackout. • Most buildings within 1000 meters of GZ are severely damaged. • EMP destroys communication networks within ∼3 miles of GZ. • Intense heat causes numerous fires. • (Immediate) 279K deaths; 93K injured. • Federal Emergency Management Agency, 2010. “Planning Guidance for response to a Nuclear Detonation”. • Buddemeier et al., 2011, “National Capital Region: Key Response Planning Factors for the Aftermath of Nuclear Terrorism”. Technical Report LLNL-TR-512111, Lawrence Livermore National Lab. • Wein et al.. 2010. “Analyzing Evacuation Versus Shelter-in-Place Strategies After a Terrorist Nuclear Detonation”. Risk Analysis 30 (9): 1315–1327.

  7. Damage and fallout Red = Complete damage Gray Background = power outage area Yellow = No damage Yellow Swath = Plume

  8. Detailed study area (DSA) .01 Gray fallout contour at 60 minutes thermal radiation contour at 2.1 calories/cm2

  9. Our contributions • We have developed a synthetic information and modeling environment for representing and studying large scale crises • We have applied our informatics framework to study a hypothetical scenario and derived many important insights

  10. Presentation outline • Introduction • Large scale human initiated crisis • Our contributions • Agent based informatics framework • Synthetic information system • Computational architecture • Studies and results • Major insights • Study: effects of communication availability and behavior on health outcomes • Conclusion

  11. Synthetic information system • Our approach is to combine many sources of data (including procedural information) to synthesize a dataset sufficient for describing the problem. • Synthetic populations: demographics, family structure, home locations, daily activity schedules, activity locations, and interaction network for every individual in the region. • Synthetic infrastructures: e.g., cell phone base stations and coverage areas, hospital locations and capacities, power substations and capacities, road network, etc. • The synthetic information methodology and platform can be applied to many problems.

  12. Synthetic information system: Data generation

  13. Synthetic information system: Data sources • Event-specific data: • Prompt radiation • Blast overpressure • Thermal fluence • Building damage • Rubble • Fallout plume

  14. Synthetic information system: Behavior modeling

  15. Interactions between behavior and infrastructures

  16. Synthetic information system: Data flow

  17. Computational architecture and scale

  18. Presentation outline • Introduction • Large scale human initiated crisis • Our contributions • Agent based informatics framework • Synthetic information system • Computational architecture • Studies and results • Major insights • Study: effects of communication availability and behavior on health outcomes • Conclusion

  19. Major insights from studies • Small improvement in communication networks has disproportionately large and positive impact on the overall behavior, leading to fewer deaths, better health outcomes and reduction in panic. . • The 2-3 day problem is different than 2-3 week problems which is different than .... This implies that the problem changes, leading to changes in the kinds of things we will need to represent in our behavioral models as well as policies. • Despite the huge physical event, human behaviors & their adaptations are important to represent. It allows us to drive the 2-3 week problem. • The power network suffers a huge loss. Large portions of the network will likely be inoperative for at least two years. • The economic consequences of the detonation are such that it is not a regional problem – it has direct effects on national economic planning and macroeconomics. • Large-scale spatio-temporal patterning of behavior emerges from interactions between individuals and between infrastructures and individuals.

  20. Example study: settings Study effects of the following on health outcomes: • Partial restoration of communication (mobile phone coverage) in regions close to ground zero • Initially 0% coverage within 1 mile; 100% otherwise • Restored to 50% capacity within 3 hours in 0.6~1 mile ring • Shelter-seeking behavior with emergency broadcast received (EBR)

  21. Example study: Difference on health outcomes b/w cells • Cells 3 vs 1: no much difference • Cells 2 vs 1: communication restoration helps • Cells 4 vs 3: communication helps much more with better shelter-seeking behavior Lower is better

  22. Example study:Behavior changes due to communication restoration • Cells 4 vs 3: communication helps decreasing exposure and injury • During early hours, more people seek shelter; • less people panic or search for family members Cell 4 – Cell 3

  23. Example study:Behavior changes due to communication restoration • Cells 2 vs 1: communication helps decreasing exposure and injury even with low shelter seeking preference • During early hours, less people panic or search for family members; • increase in all other behaviors; aid&assist helps the most. Cell 2 – Cell 1

  24. Conclusion • Synthetic information framework and computing architecture • HPC based; scalable for large scale crises • Co-evolving individual and population behaviors, and their complex relationship with civil infrastructures • Study of a hypothetical scenario as a demonstration • Detonation of an improvised nuclear device in DC • Spatial, temporal, and individual level details • Counter-factual experiments show that targeted interventions can significantly improve outcomes in terms of human health

  25. Acknowledgments • NDSSL: • Staff: Abhijin Adiga, Chris Barrett, Keith Bisset, Jiangzhuo Chen, Youngyun Chungbaek, Stephen Eubank, Annette Feng, Kevin Hall, Kathy Laskowski, Bryan Lewis, Kristian Lum, Achla Marathe, Madhav Marathe, Bill Marmagas, Henning Mortveit, Paula Stretz, Samarth Swarup, Anil Vullikanti, Dawen Xie, Mina Youssef • Students: Shridhar Chandan, José Jiménez, Junwhan Kim, Akshay Maloo, Nidhi Parikh, Guanhong Pei, Caitlin Rivers, Sudip Saha, Balaaji Sunapanasubbiah Collaborators: - Mike Snow, Jian Lu, Mandy Wilson: VBI CCF- Ryan Quint, Yaman Evrenosoglu, Arun Phadke, James Thorp: Electrical Engineering, Virginia Tech. Expertise in Power Networks- Jeff Reed: Electrical Engineering, Virginia Tech. Expertise in Communication Networks- Nishith Tripathi, Award Solutions. Expert in Communication Networks- Dane Webster: School of Visual Arts, Virginia Tech. Expertise in Visualization and Graphics- Thomas Dickerson and Peter Sforza: CGIT, Virginia Tech. Expertise in GIS.

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