Redefining effective distance using multi-hop flows in urban mobility networks

Abstract Effective distance, a concept that redefines spatial connectivity by considering human mobility links rather than physical distance, has proven to be a powerful tool for predicting epidemic spread. Traditional effective distance models are constructed using shortest paths on mobility networks, however, in highly connected urban environments, such shortest paths typically reduce to direct one-hop flows, thereby neglecting the essential contribution of multi-hop mobility to infection risk. This study extends the effective distance framework by incorporating multi-hop flows, providing a comprehensive understanding of how infection risk propagates through urban networks. Using detailed mobility data from the Shanghai Omicron BA.2 outbreak, we demonstrate that our extended effective distance model improves the prediction of infection arrival times compared to original effective distance models at fine spatial scales, highlighting the importance of incorporating multi-hop mobility pathways in urban disease modeling.