Multiple small- to middle-scale cities, mostly located in northern China, became epidemic hotspots during the second wave of the spread of COVID-19 in early 2021. Despite qualitative discussions of potential social-economic causes, it remains unclear how this pattern could be accounted for from a quantitative approach. Through the development of an urban epidemic hazard index (EpiRank), we came up with a mathematical explanation for this phenomenon. The index is constructed from epidemic simulations on a multi-layer transportation network model on top of local SEIR transmission dynamics, which characterizes intra- and inter-city compartment population flow with a detailed mathematical description. Essentially, we argue that these highlighted cities possess greater epidemic hazards due to the combined effect of large regional population and small inter-city transportation. The proposed index, dynamic and applicable to different epidemic settings, could be a useful indicator for the risk assessment and response planning of urban epidemic hazards in China; the model framework is modularized and can be adapted for other nations without much difficulty.