Urban Epidemic Hazard Index for Chinese Cities: Why Did Small Cities Become Epidemic Hotspots?

TL;DR

This paper introduces the EpiRank index, a quantitative tool combining transportation network modeling and epidemic dynamics to explain why small Chinese cities became COVID-19 hotspots, aiding risk assessment.

Contribution

The paper develops a novel, adaptable epidemic hazard index based on multi-layer transportation and SEIR models to quantitatively analyze urban epidemic risks.

Findings

Small cities with high population and low inter-city transport are more epidemic-prone.

The EpiRank index effectively identifies epidemic hotspots in Chinese cities.

The model framework can be adapted for different countries and epidemic scenarios.

Abstract

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.