Multilayer urban commuting networks reveal inequality in epidemic dynamics in cities

Multiple transportation modes and commuting patterns in cities make urban epidemics extremely complicated. While previous studies have explored impacts of mobility on epidemics, the combined effects of multilayer structures and commuting behaviors remain less systematically examined. Using urban statistical data, we construct multilayer urban commuting networks stratified by three transportation modes and incorporating both random and regular commuting patterns. Quantitative analyses show that commuting infections on subway layer significantly impact urban epidemics, resulting in an optimal subsequent infection size in communities. The regular commuting pattern has a dual effect on outbreak size and enhances the spatio-temporal complexity of urban epidemics, i.e., the large number of regular commuters in megacities leads to the asymmetric prevalence evolution and reinforces spreading inequality across districts. Our results provide theoretical support for offering structural insights to inform mobility-based prevention and control strategies for healthier and more resilient cities.