A Spatial Agent-Based Model for Studying the Effect of Human Mobility Patterns on Epidemic Outbreaks in Urban Areas

The epidemic outbreaks of the last two decades have led governments to rely more on computational tools for establishing protection policies. Computational approaches to modeling epidemics traditionally rely on compartmental models, network models, or agent-based models (ABMs); however, each approach has its limitations, ranging from reduced realism to lack of tractability. Furthermore, the recent literature emphasizes the importance of points of interest (POIs) as sources of population mixing and potential outbreak hotspots. In response, this study proposes a novel urban spatial ABM validated using our augmented SICARQD epidemic model. To replicate daily activities more accurately, the urban area is divided into a matrix of points of interest (POIs) with agents that have unique paths that only permit infectious transmission within POIs. Our results provide a qualitative assessment of how urban characteristics and individual mobility patterns impact the infected population during an outbreak. That is, we study how population density, the total number of POIs (where the population concentrates), the average number of POIs visited by an agent, the maximum travel distance from the home location, and the quarantine ratio impact the dynamics of an outbreak. Our ABM simulation framework offers a valuable tool for investigating and controlling infectious disease outbreaks in urban environments with direct applicability to global policy makers.