This paper proposes a cluster-based method to analyse multivariate systems that change over time. At each point in time, we partition data points into an appropriate number of clusters and thereby track both the total number of clusters and the individual constituents' cluster memberships changing over time. We can also compare and contrast several multivariate systems and determine patterns and anomalies between different data. We apply this method to study the evolution of COVID-19 cases and deaths over time. We detect significant similarities in the evolution of cases and deaths up to a suitable offset in time. We compute this offset and use it to identify anomalous countries in the progression of cases to deaths.
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.
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At each point in time, we partition
We apply this method to analyse the evolution of COVID-19 cases and deaths, partitioning
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and thereby
on each day to
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the individual constituents' cluster memberships changing
their changing constituency
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We can also compare and contrast several multivariate systems and determine patterns and anomalies between different data. We apply this method to study the evolution of COVID-19
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
R
over time. We detect significant similarities in
independently reveals a close relationship between
R
cases and deaths up to a suitable offset in time. We compute this offset and use it
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