Time series clustering of COVID-19 pandemic-related data
Zhixue Luo, Lin Zhang, Na Liu, Ye Wu
Abstract
The COVID-19 pandemic continues to impact daily life worldwide. It would be helpful and valuable if we could obtain valid information from the COVID-19 pandemic sequential data itself for characterizing the pandemic. Here, we aim to demonstrate that it is feasible to analyze the patterns of the pandemic using a time-series clustering approach. In this work, we use dynamic time warping distance and hierarchical clustering to cluster time series of daily new cases and deaths from different countries into four patterns. It is found that geographic factors have a large but not decisive influence on the pattern of pandemic development. Moreover, the age structure of the population may also influence the formation of cluster patterns. Our proven valid method may provide a different but very useful perspective for other scholars and researchers.