This paper proposes a cluster-based method to analyse multivariate systems that change over time . We apply this method to analyse the evolution of COVID-19 cases and deaths, partitioning data points into an appropriate number of clusters on each day to track both the  total number of clusters and their changing constituency over time . This method can be used to track the trajectory of both the entire system as well as individual countries relative to the system. Applying our analysis to cases and deaths independently reveals a close relationship between the evolution of these two systems. With this  in mind, we also develop a method to analyse the similarity and anomalies between two related multivariate systems in conjunction, allowing us to identify anomalous countries in the progression of cases to deaths  .
This paper proposes a cluster-based method to analyze the evolution of multivariate time series and applies this to the COVID-19 pandemic. On each day, we partition countries into clusters according to both their case and death counts. The total number of clusters and individual countries' cluster memberships are algorithmically determined. We study the change in both quantities over time, demonstrating a close similarity in the evolution of cases and deaths. The changing number of clusters of the case counts precedes that of the death counts by 32 days. On the other hand, there is an optimal offset of 16 days with respect to the greatest consistency between cluster groupings, determined by a new method of comparing affinity matrices . With this offset in mind, we identify anomalous countries in the progression from COVID-19 cases to deaths . This analysis can aid in highlighting the most and least significant public policies in minimizing a country's COVID-19 mortality rate .
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analyse multivariate systems that change over time . We apply this method to analyse the evolution of COVID-19 cases and deaths, partitioning data points into an appropriate number of clusters on each day to track both
analyze the evolution of multivariate time series and applies this to
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COVID-19 pandemic. On each day, we partition countries into clusters according to both their case and death counts. The
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their changing constituency over time
individual countries' cluster memberships are algorithmically determined. We study the change in both quantities over time, demonstrating a close similarity in the evolution of cases and deaths. The changing number of clusters of the case counts precedes that of the death counts by 32 days. On the other hand, there is an optimal offset of 16 days with respect to the greatest consistency between cluster groupings, determined by a new method of comparing affinity matrices
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This method can be used to track the trajectory of both the entire system as well as individual countries relative to the system. Applying our analysis to cases and deaths independently reveals a close relationship between the evolution of these two systems.
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offset
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also develop a method to analyse the similarity and anomalies between two related multivariate systems in conjunction, allowing us to
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of
from COVID-19
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. This analysis can aid in highlighting the most and least significant public policies in minimizing a country's COVID-19 mortality rate