LAUSR

ABSTRACT The novel coronavirus disease of 2019 (COVID-19) first appeared in Wuhan and subsequently spread rapidly in cities and provinces across the country and all over the world. In order to effectively control the spread of the epidemic in different areas, zonal management and endemic prevention and control policies should be implemented according to local epidemic situations. This study proposes a time-series clustering method to discover dynamic characteristics of the COVID-19 epidemic by categorizing the epidemic situations in China’s major cities into groups based on daily reported confirmed cases and analysing the driving factors of the city background conditions for each category. Our results show that according to the dynamic patterns of the COVID-19 epidemic there are eight types of epidemic situations, including extreme outbreak areas, large spread areas, potential resurged areas, middle spread areas, controlled outbreak areas, limited growth areas, delayed outbreak areas, and lag report areas. These dynamic patterns are mainly related to the city background conditions, such as population flow, local resident number, government emergency response capability, and medical resource conditions. Based on our results, different endemic prevention and control measures are recommended for containing the COVID-19 epidemic in cities with different types of epidemic situations.