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This paper proposes a cluster-based method to analyze the evolution ofmultivariate time series and applies this to the COVID-19 pandemic. On eachday, we partition countries into clusters according to both their case anddeath counts. The total number of clusters and individual countries' clustermemberships are algorithmically determined. We study the change in bothquantities over time, demonstrating a close similarity in the evolution ofcases and deaths. The changing number of clusters of the case counts precedesthat of the death counts by 32 days. On the other hand, there is an optimaloffset of 16 days with respect to the greatest consistency between clustergroupings, determined by a new method of comparing affinity matrices. With thisoffset in mind, we identify anomalous countries in the progression fromCOVID-19 cases to deaths. This analysis can aid in highlighting the most andleast significant public policies in minimizing a country's COVID-19 mortalityrate.

Cluster-based dual evolution for multivariate time series: analyzing COVID-19