Analyzing the Impact of Geo-Spatial Organization of Real-World Communities on Epidemic Spreading Dynamics

摘要

Models for complex epidemic spreading are an essential tool for predicting both local and global effects of epidemic outbreaks. The ongoing development of the COVID-19 pandemic has shown that many classic compartmental models, like SIR, SIS, SEIR considering homogeneous mixing of the population may lead to over-simplified estimations of outbreak duration, amplitude and dynamics (e.g., waves). The issue addressed in this paper focuses on the importance of considering the social organization into geo-spatially organized communities (i.e., the size, position, and density of cities, towns, settlements) which have a profound impact on shaping the dynamics of epidemics. We introduce a novel geo-spatial population model (GPM) which can be tailored to reproduce a similar heterogeneous individuals' organization to that of real-world communities in chosen countries. We highlight the important differences between a homogeneous model and GPM in their capability to estimate epidemic outbreak dynamics (e.g., waves), duration and overall coverage using a dataset of the world's nations. Results show that community size and density play an important role in the predictability and controllability of epidemics. Specifically, small and dense community systems can either remain completely isolated, or show rapid bursts of epidemic dynamics; larger systems lengthen the epidemic size and duration proportionally with their number of communities.