Emergency and Disaster Management

Large-scale simulation capability can be utilized to study phenomenon outside the discipline of transportation such as public health and emergencies or disasters management. We build on recent work to develop a fully mechanistic, activity-based and highly spatio-temporally resolved epidemiological model which leverages person-trajectories obtained from an activity-based model calibrated for two full-scale prototype cities, consisting of representative synthetic populations and mobility networks for two contrasting auto-dependent city typologies. We simulate the propagation of the COVID-19 epidemic in both cities to analyze spreading patterns in urban networks across various activity types. Investigating the impact of the transit network, we find that its removal dampens disease propagation significantly, suggesting that travel restriction is more critical in post-peak disease spreading and where the mass transit system is extensively used. In latter stages of disease spread, we found that the greatest share of infections occur at work locations. We conduct a statistical analysis of the resulting activity-based contact networks and find evidence that transit contacts are scale-free, work contacts are Weibull distributed, and shopping or leisure contacts are exponentially distributed. We validate our simulation results against existing case and mortality data across multiple cities in their respective typologies. Our framework demonstrates the potential for tracking epidemic propagation in urban networks, analyzing socio-demographic impacts and assessing activity- and mobility-specific implications of spreading patterns and potential intervention strategies.

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