Modeling emergent border-crossing behaviors during pandemics

Eunice E. Santos; Eugene Santos Jr.; John Korah; Jeremy E. Thompson; Qi Gu; Keum Joo Kim; Deqing Li; Jacob Russell; Suresh Subramanian; Yuxi Zhang; Yan Zhao

doi:10.1117/12.2018201

6 June 2013 Modeling emergent border-crossing behaviors during pandemics

Eunice E. Santos, Eugene Santos Jr., John Korah, Jeremy E. Thompson, Qi Gu, Keum Joo Kim, Deqing Li, Jacob Russell, Suresh Subramanian, Yuxi Zhang, Yan Zhao

Author Affiliations +

Proceedings Volume 8711, Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense XII; 87110Z (2013) https://doi.org/10.1117/12.2018201
Event: SPIE Defense, Security, and Sensing, 2013, Baltimore, Maryland, United States

Abstract

Modeling real-world scenarios is a challenge for traditional social science researchers, as it is often hard to capture the intricacies and dynamisms of real-world situations without making simplistic assumptions. This imposes severe limitations on the capabilities of such models and frameworks. Complex population dynamics during natural disasters such as pandemics is an area where computational social science can provide useful insights and explanations. In this paper, we employ a novel intent-driven modeling paradigm for such real-world scenarios by causally mapping beliefs, goals, and actions of individuals and groups to overall behavior using a probabilistic representation called Bayesian Knowledge Bases (BKBs). To validate our framework we examine emergent behavior occurring near a national border during pandemics, specifically the 2009 H1N1 pandemic in Mexico. The novelty of the work in this paper lies in representing the dynamism at multiple scales by including both coarse-grained (events at the national level) and finegrained (events at two separate border locations) information. This is especially useful for analysts in disaster management and first responder organizations who need to be able to understand both macro-level behavior and changes in the immediate vicinity, to help with planning, prevention, and mitigation. We demonstrate the capabilities of our framework in uncovering previously hidden connections and explanations by comparing independent models of the border locations with their fused model to identify emergent behaviors not found in either independent location models nor in a simple linear combination of those models.

Citation Download Citation

Eunice E. Santos, Eugene Santos Jr., John Korah, Jeremy E. Thompson, Qi Gu, Keum Joo Kim, Deqing Li, Jacob Russell, Suresh Subramanian, Yuxi Zhang, and Yan Zhao "Modeling emergent border-crossing behaviors during pandemics", Proc. SPIE 8711, Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense XII, 87110Z (6 June 2013); https://doi.org/10.1117/12.2018201

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