  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.
