Ayako Sumi

Exponential Characteristics of Power Spectral Densities Caused by Chaotic Phenomena

Power spectral densities (PSDs) were calculated by the maximum entropy method (MEM) for three types of chaotic time series numerically generated from the Lorenz, Rossler and Duffing models. Every PSD indicated exponential decay until it levels off at a limit determined by the accuracy of the present computation. This exponential chracteristics were universally found in all chaotic time series irrespective of the data length as well as the time range of data. The values of the coefficient of exponent were 3.26, 5.72 and 7.36 for 6000-point data of the Lorenz, Rossler and Duffing models, respectively, and they slightly increased as the data length shortened. The exponential form was also recognized in the MEM-PSD for the Lorentzian-type time series as in theory.

Effect of temperature, relative humidity and rainfall on rotavirus infections in Kolkata, India

Rotavirus is a common viral cause of severe diarrhoea. For the underlying cause of rotavirus seasonality, the meteorological factor has been suspected, whereas quantitative correlation between seasonality and meteorological factor has not been fully investigated. In this study, we investigated the correlation of temporal patterns of the isolation rate of rotavirus with meteorological condition (temperature, relative humidity, rainfall) in Kolkata, India. We used time-series analysis combined with spectral analysis and least squares method. A 1-year cycle explained underlying variations of rotavirus and meteorological data. The 1-year cycle for rotavirus data was correlated with an opposite phase to that for meteorological data. Relatively high temperature could be associated with a low value of isolation rate of rotavirus in the monsoon season. Quantifying a correlation of rotavirus infections with meteorological conditions might prove useful in predicting rotavirus epidemics and health services could plan accordingly.

Diversity of staphylocoagulase and identification of novel variants of staphylocoagulase gene in Staphylococcus aureus.

Staphylocoagulase (SC) is a major phenotypic determinant of Staphylococcus aureus. Serotype of SC (coagulase type) is used as an epidemiological marker and 10 types (I–X) have been discriminated so far. To clarify genetic diversity of SC within a single and among different serotype(s), we determined approximately 1500 bp‐nucleotide sequences of SC gene encoding D1, D2, and central regions (N‐terminal half and central regions of SC; SCNC) for a total of 33 S. aureus strains comprising two to three strains from individual coagulase types (I–VIII, X) and 10 strains which were not determined as previously known SC serotypes (ND‐strains). Amino acid sequence identities of SCNC among strains with a single coagulase type of II, III, IV, V, VI and X were extremely high (more than 99%), whereas lower identity (56–87%) was observed among different types. In contrast, within a single coagulase type of I, VII, or VIII, sequence divergence was found (lowest identity; 82%). SCNC sequences from the ND‐strains were discriminated into two genetic groups with an identity of 71% to each other (tentatively assigned to genotypes [XI] and [XII]), and exhibited less than 86% sequence identities to those of most known coagulase types. All the types [XI] and [XII] strains were methicillin susceptible and belonged to different sequence types from those of coagulase types I–X strains reported so far by multilocus sequence typing. These findings indicated genetic heterogeneity of SC in coagulase types I, VII, and VIII strains, and the presence of two novel SC genotypes related to antigenicity of SC serotypes.

Time Series Analysis of Incidence Data of Influenza in Japan

Background Much effort has been expended on interpreting the mechanism of influenza epidemics, so as to better predict them. In addition to the obvious annual cycle of influenza epidemics, longer-term incidence patterns are present. These so-called interepidemic periods have long been a focus of epidemiology. However, there has been less investigation of the interepidemic period of influenza epidemics. In the present study, we used spectral analysis of influenza morbidity records to indentify the interepidemic period of influenza epidemics in Japan. Methods We used time series data of the monthly incidence of influenza in Japan from January 1948 through December 1998. To evaluate the incidence data, we conducted maximum entropy method (MEM) spectral analysis, which is useful in investigating the periodicities of shorter time series, such as that of the incidence data used in the present study. We also conducted a segment time series analysis and obtained a 3-dimensional spectral array. Results Based on the results of power spectral density (PSD) obtained from MEM spectral analysis, we identified 3 periodic modes as the interepidemic periods of the incidence data. Segment time series analysis revealed that the amount of amplitude of the interepidemic periods increased during the occurrence of influenza pandemics and decreased when vaccine programs were introduced. Conclusions The findings suggest that the temporal behavior of the interepidemic periods of influenza epidemics is correlated with the magnitude of cross-reactive immune responses.

Relationship of cholera incidence to El Niño and solar activity elucidated by time-series analysis

Using time-series analysis, we investigated the monthly cholera incidence in Dhaka, Bangladesh during an 18-year period for its relationship to the sea surface temperature (SST) linked to El Niño, and to the sunspot number. Dominant periodic modes identified for cholera incidence were 11.0, 4.8, 3.5, 2.9, 1.6, 1.0 and 0.5 years. The majority of these modes, e.g. the 11.0-, 4.8-, 3.5-, 1.6- and 1.0-year modes, were essentially consistent with those obtained for the SST data (dominant modes: 5.1, 3.7, 2.5, 2.1, 1.5, 1.0 years) and the sunspot number data (dominant modes: 22.1, 11.1, 7.3, 4.8, 3.1 years). We confirmed that the variations of cholera incidence were synchronous with SSTs, and were inversely correlated to the sunspot numbers. These results suggest that the cholera incidence in Bangladesh may have been influenced by the occurrence of El Niño and also by the periodic change of solar activity.

Prediction Analysis for Measles Epidemics

A newly devised procedure of prediction analysis, which is a linearized version of the nonlinear least squares method combined with the maximum entropy spectral analysis method, was proposed. This method was applied to time series data of measles case notification in several communities in the UK, USA and Denmark. The dominant spectral lines observed in each power spectral density (PSD) can be safely assigned as fundamental periods. The optimum least squares fitting (LSF) curve calculated using these fundamental periods can essentially reproduce the underlying variation of the measles data. An extension of the LSF curve can be used to predict measles case notification quantitatively. Some discussions including a predictability of chaotic time series are presented.

Spectral Study of Measles Epidemics: The Dependence of Spectral Gradient on the Population Size of the Community.

We have carried out spectral analysis of measles notifications in several communities in Denmark, UK and USA. The results confirm that each power spectral density (PSD) shows exponential characteristics, which are universally observed in the PSD for time series generated from nonlinear dynamical system. The exponential gradient increases with the population size. For almost all communities, many spectral lines observed in each PSD can be fully assigned to linear combinations of several fundamental periods, suggesting that the measles data are substantially noise-free. The optimum least squares fitting curve calculated using these fundamental periods essentially reproduces an underlying variation of the measles data, and an extension of the curve can be used to predict measles epidemics. For the communities with large population sizes, some PSD patterns obtained from segment time series analysis show a close resemblance to the PSD patterns at the initial stages of a period-doubling bifurcation process for the so-called susceptible/exposed/infectious/recovered (SEIR) model with seasonal forcing. The meaning of the relationship between the exponential gradient and the population size is discussed.

Emerging non-PCV13 serotypes of noninvasive Streptococcus pneumoniae with macrolide resistance genes in northern Japan

In Japan, the 7-valent pneumococcal conjugate vaccine (PCV7) was introduced to the nations routine immunization program in April 2013 and was replaced by the 13-valent pneumococcal conjugate vaccine (PCV13) in November 2013. Distribution of serotypes and macrolide resistance genotypes was investigated for a total of 1097 (975 children, 122 adults) and 960 (873 children, 87 adults) clinical isolates of Streptococcus pneumoniae from noninvasive infections in Hokkaido (northern main island of Japan) in the routine immunization periods for PCV7 and PCV13 (April–October 2013 and November 2013–November 2014, respectively). Serotype was determined by sequential multiplex PCR and additional genetic analyses. Macrolide resistance genes erm(B) and mef(A/E) were detected by multiplex PCR. Although the most prevalent serotypes in children were 23A and 6C in the PCV7 period, after replacement with PCV13, 19A became the most common, followed by 6C, 15A and 23A. Among adults, serotype 3 was consistently the most frequent throughout the study periods. Compared with values from the pre-PCV7 routine immunization period, PCV7 serotypes decreased from 48.3 to 3.3% in the PCV13 period among children, while the rates of non-PCV13 serotypes (particularly 15A, 23A, 11A, 10A and 35B) increased from 39.7 to 75.1% (p < 0.001). In the PCV13 period, erm(B), mef(A/E) and both of these genes were detected in 75.8, 31.6 and 11.3% of all isolates, respectively. Serotype 19A accounted for 76.9% of the isolates with both the macrolide resistance genes, and emerging non-PCV13 serotypes 15A, 15C and 23A mostly harboured erm(B).

Time-series analysis of hepatitis A, B, C and E infections in a large Chinese city: application to prediction analysis.

Viral hepatitis is recognized as one of the most frequently reported diseases, and especially in China, acute and chronic liver disease due to viral hepatitis has been a major public health problem. The present study aimed to analyse and predict surveillance data of infections of hepatitis A, B, C and E in Wuhan, China, by the method of time-series analysis (MemCalc, Suwa-Trast, Japan). On the basis of spectral analysis, fundamental modes explaining the underlying variation of the data for the years 2004-2008 were assigned. The model was calculated using the fundamental modes and the underlying variation of the data reproduced well. An extension of the model to the year 2009 could predict the data quantitatively. Our study suggests that the present method will allow us to model the temporal pattern of epidemics of viral hepatitis much more effectively than using the artificial neural network, which has been used previously.

A Detailed Study of Power Spectral Density for Rossler System

Power spectral densities (PSDs) calculated by the maximum entropy method (MEM) for Rossler system indicated exponential decay with a large number of well-defined spectral lines. The spectral lines were confirmed to indicate a complete bifurcation up to the fifth-order period-doubling. An extremely anomalous behavior was recognized in the region of c =4.18–4.21 which is considered to be a transition region. The contribution of the power of the fundamental mode to the total power was overwhelmingly large: it becomes larger than 90%. A prediction of time series including chaotic ones was performed and the satisfactory results obtained. It will be discussed that the fluctuations due to amplitude instability of time series in the periodic solutions generate, resulting in the continuous component of PSD structured through the subharmonic cascade process, and this continuous region is restructured in the inverse cascade process (the socalled broad continuum in the chaotic mixing).