The COVID-19 pandemic continues to impact daily life worldwide.It would be helpful and valuable if we could obtain valid information from the COVID-19 pandemic sequential data itself for characterizing the pandemic.He...The COVID-19 pandemic continues to impact daily life worldwide.It would be helpful and valuable if we could obtain valid information from the COVID-19 pandemic sequential data itself for characterizing the pandemic.Here,we aim to demonstrate that it is feasible to analyze the patterns of the pandemic using a time-series clustering approach.In this work,we use dynamic time warping distance and hierarchical clustering to cluster time series of daily new cases and deaths from different countries into four patterns.It is found that geographic factors have a large but not decisive influence on the pattern of pandemic development.Moreover,the age structure of the population may also influence the formation of cluster patterns.Our proven valid method may provide a different but very useful perspective for other scholars and researchers.展开更多
In this paper, we investigate optimal policies for an age-dependent n-dimensional competition system, which is controlled by fertility. By using Dubovitskii-Milyutin＇s general theory, the maximum principles are obtai...In this paper, we investigate optimal policies for an age-dependent n-dimensional competition system, which is controlled by fertility. By using Dubovitskii-Milyutin＇s general theory, the maximum principles are obtained for the problems with free terminal states, infinite horizon, and target sets, respectively.展开更多
基金jointly supported by the National Natural Science Foundation of China(Grant No.:11971074.61671005).
文摘The COVID-19 pandemic continues to impact daily life worldwide.It would be helpful and valuable if we could obtain valid information from the COVID-19 pandemic sequential data itself for characterizing the pandemic.Here,we aim to demonstrate that it is feasible to analyze the patterns of the pandemic using a time-series clustering approach.In this work,we use dynamic time warping distance and hierarchical clustering to cluster time series of daily new cases and deaths from different countries into four patterns.It is found that geographic factors have a large but not decisive influence on the pattern of pandemic development.Moreover,the age structure of the population may also influence the formation of cluster patterns.Our proven valid method may provide a different but very useful perspective for other scholars and researchers.
基金The work is supported by‘Qing Lan’Talent Engineering Funds(QL-05-1SA) by Lanzhou Jiaotong Universitythe National Natural Science Foundation of China under Grant No.604730304.
文摘In this paper, we investigate optimal policies for an age-dependent n-dimensional competition system, which is controlled by fertility. By using Dubovitskii-Milyutin＇s general theory, the maximum principles are obtained for the problems with free terminal states, infinite horizon, and target sets, respectively.
