The Generation and Application of Large Scale Synthetic Populations for Disease Outbreaks and Disasters

Authors: Andrew Crooks*, George Mason University, Annetta Burger, George Mason University, Xiaoyi Yuan, George Mason University, William G Kennedy, George Mason University
Topics: Geographic Information Science and Systems, Hazards, Risks, and Disasters, Urban Geography
Keywords: Agent-based modeling, Urban Systems, Population Synthesis, Social Networks
Session Type: Paper
Scheduler ID: THU-064-8:00 a.m.
Day: 4/12/2018
Start / End Time: 8:00 AM / 9:40 AM
Room: Bayside A, Sheraton, 4th Floor

Increasingly agent-based models are being used to study human behavior in response to disease outbreaks and natural disasters. Such models allow us to explore questions regarding the effects of school closure on disease outbreaks or shelter versus evacuation during a disaster. However, developing realistic control populations with realistic social networks of large-scale urban populations remains a key challenge for such research and experimentation. Modelers must balance the need for representative, heterogeneous populations with the computational costs of developing large population sets. These models must also include the social network relationships that influence social interactions and behavioral patterns in times of crisis. To address this challenge, we use a set of methods and empirical data to build a geographically explicit synthesized population with social networks embedded in an agent-based modeling environment for the New York megacity region. Furthermore, we compare and contrast our synthetic population with other population synthesis methods and demonstrate its utility through two example applications. The first is a disease outbreak and the second is an evacuation scenario during a disaster. These applications demonstrate how our computational framework provides a baseline laboratory for testing a variety of scenarios with respect to urban systems and the role of social networks on model outcomes and the movement of populations times of crisis.

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