Cluster-based dual evolution for multivariate time series: Analyzing COVID-19.
Chaos
; 30(6): 061108, 2020 Jun.
Article
in English
| MEDLINE | ID: covidwho-622581
ABSTRACT
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 cases 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.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Pneumonia, Viral
/
Time and Motion Studies
/
Cluster Analysis
/
Coronavirus Infections
Type of study:
Experimental Studies
/
Observational study
/
Prognostic study
Limits:
Humans
Language:
English
Journal:
Chaos
Journal subject:
Science
Year:
2020
Document Type:
Article
Affiliation country:
5.0013156
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