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10.1063/5.0013156 http://scihub22266oqcxt.onion/10.1063/5.0013156 32611104!7328914!32611104     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Chaos 2020 ; 30 (6): 061108 Nephropedia Template TP {{tp|p=32611104|t=2020. Cluster-based dual evolution for multivariate time series: Analyzing COVID-19 |pdf=|usr=}}{{32611104}} gab.com Text Cluster-based dual evolution for multivariate time series: Analyzing COVID-19 JO: Chaos http://www.kidney.de/pget.php?&tw=1&pmid=32611104 #FOAvip 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. Twit Text FOAVip Cluster-based dual evolution for multivariate time series: Analyzing COVID-19 ????Chaos http://www.kidney.de/pget.php?&tw=1&pmid=32611104 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32611104 #FOAvip English Wikipedia <ref>{{Cite pmid|32611104}}</ref> Cluster-based dual evolution for multivariate time series: Analyzing COVID-19 #MMPMID32611104 James N; Menzies M Chaos 2020[Jun]; 30 (6): 061108 PMID32611104show ga 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. |*Cluster Analysis[MESH] |*Time and Motion Studies[MESH] |Betacoronavirus[MESH] |COVID-19[MESH] |Coronavirus Infections/*epidemiology/*mortality[MESH] |Humans[MESH] |Mortality/trends[MESH] |Pandemics[MESH] |Pneumonia, Viral/*epidemiology/*mortality[MESH]Cluster-based dual evolution for multivariate time series: Analyzing C(epmc).pdf DeepDyve Pubget Overpricing