Dissimilarity Measures for Time Series and Trend Analysis: Application to COVID-19 Cases Series

In this paper we propose some dissimilarity measure functions for trends of nonstationary time series.If time series are stationary,the cross correlation function can be applied as a similarity measure.However,the validity of the cross correlation function is lost for nonstationary time series.Moreover,the cross correlation function cannot be calculated if one of trends is constant.The proposed functions can be applied even if trends are constant and their values are determined through the minimization.The clustering is considered as an application of the dissimilarity measure.Furthermore the problem of the common trend within multiple time series is considered and the estimation algorithm is proposed.Usability of the proposed method is demonstrated by applying to series of COVID-19 cases in Japan.

Identifying key bird species and geographical hotspots of avian influenza A(H7N9)virus in China

Background:In China since the first human infection of avian influenza A(H7N9)virus was identified in 2013,it has caused serious public health concerns due to its wide spread and high mortality rate.Evidence shows that bird migration plays an essential role in global spread of avian influenza viruses.Accordingly,in this paper,we aim to identify key bird species and geographical hotspots that are relevant to the transmission of avian influenza A(H7N9)virus in China.Methods:We first conducted phylogenetic analysis on 626 viral sequences of avian influenza A(H7N9)virus isolated in chicken,which were collected from the Global Initiative on Sharing All Influenza Data(GISAID),to reveal geographical spread and molecular evolution of the virus in China.Then,we adopted the cross correlation function(CCF)to explore the relationship between the identified influenza A(H7N9)cases and the spatiotemporal distribution of migratory birds.Here,the spatiotemporal distribution of bird species was generated based on bird observation data collected from China Bird Reports,which consists of 157272 observation records about 1145 bird species.Finally,we employed a kernel density estimator to identify geographical hotspots of bird habitat/stopover that are relevant to the influenza A(H7N9)infections.Results:Phylogenetic analysis reveals the evolutionary and geographical patterns of influenza A(H7N9)infections,where cases in the same or nearby municipality/provinces are clustered together with small evolutionary differences.Moreover,three epidemic waves in chicken along the East Asian-Australasian flyway in China are distinguished from the phylogenetic tree.The CCF analysis identifies possible migratory bird species that are relevant to the influenza A(H7N9)infections in Shanghai,Jiangsu,Zhejiang,Fujian,Jiangxi,and Guangdong in China,where the six municipality/provinces account for 91.2%of the total number of isolated H7N9 cases in chicken in GISAID.Based on the spatial distribution of identified bird species,geographical hotspots are further estimated and illustrated within these typical municipality/provinces.Conclusions:In this paper,we have identified key bird species and geographical hotspots that are relevant to the spread of influenza A(H7N9)virus.The results and findings could provide sentinel signal and evidence for active surveillance,as well as strategic control of influenza A(H7N9)transmission in China.

Experimental Analysis of Pressure Fluctuations behind a Bottom Aerator

Experimental observations show that the random process of two phase flow behind an aerator is an ergodic process and its amplitude distribution is similar to a normal distribution. The maximum pressure fluctuation is at the re attachment point where the jet trajectory flow over the aerator re attaches to bottom of the channel, and its amplitude is 2 3 times larger than when there is no aerator. There is a dominant frequency of 1 24 Hz in the model, but the coherence in the frequency domain is not obvious for other frequencies beside the dominant frequency. There is a large vortex at the re attachment point behind the aerator but correlation among the measurement points is not obvious in the time domain.