Prediction Analysis for Measles Epidemics

Sumi, Ayako; Ohtomo, Norio; Tanaka, Yukio; Sawamura, Sadashi; Olsen, Lars Folke; Kobayashi, Nobumichi

doi:10.1143/jjap.42.7611

Cited by 18 publications

(26 citation statements)

References 8 publications

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“…The cross-correlation functions between the symmetrical peaks, even if the width is different, have no time lag [see (1,2) and (2, 1)]. If the shapes of the peaks are different and if at least one of them is deformed, the crosscorrelation functions have the maxima when τ ≠ 0 [see (1,3), (1,4), (2,3), (2,4), (3,1), (3,2), (3,4), (4,1), (4,2) and (4, 3)]. …”

Section: (T) and B(t) Such That If B(t) Lags Behind A(t) R(τ)mentioning

confidence: 99%

“…[1][2][3][4][5][6][7][8][9][10][11][12][13] The focus in the dimension of the propagation is on time and space with much regard for humans, culture, society, environment, geography, etc. The stochastic properties such as auto-correlation, power spectral density, 1/f fluctuation and chaos have been extracted from the time series of disease processes.…”

Section: Introductionmentioning

confidence: 99%

“…The stochastic properties such as auto-correlation, power spectral density, 1/f fluctuation and chaos have been extracted from the time series of disease processes. [1][2][3][4][5][6][7][8] For the spatial understanding of the processes, choropleth maps and geographic information systems have been developed in epidemiology. [9][10][11][12][13] The information necessary for the above study is basically the number of patients diagnosed by doctors and can be obtained through the nation-wide surveillance, notification in communities, etc.…”

Section: Introductionmentioning

confidence: 99%

“…[9][10][11][12][13] The information necessary for the above study is basically the number of patients diagnosed by doctors and can be obtained through the nation-wide surveillance, notification in communities, etc. [1][2][3][4][5][9][10][11][12] Recently, a new information source was proposed for the same purposes 9,10) : the daily variations in formulations dispensed at pharmacies and drug stores. These data were analyzed with a method of spectral analysis (cross-correlation function).…”

Section: Introductionmentioning

confidence: 99%

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Stochastic Study on the Sales Pattern of Influenza Anti-Viral Agents at Pharmacies

Takahashi

Ijuin²,

Iwaki

et al. 2006

JOURNAL OF HEALTH SCIENCE

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This paper focuses on the stochastic analysis of the daily variations in formulations dispensed at pharmacies. A recent study showed that the cross-correlation functions between the time variations can be used for estimating the route and speed of influenza propagation process in society. In reality, however, the peakshaped signals of the time variations and cross-correlation function are so complicated and noisy that it is almost impossible to know the original shape of the signals. The aim of this paper is to give an answer to this problem by considering the mathematical relationship between the time variations and cross-correlation function. Influenza anti-viral agents for adults and children are taken as an example.

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Section: (T) and B(t) Such That If B(t) Lags Behind A(t) R(τ)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Stochastic Study on the Sales Pattern of Influenza Anti-Viral Agents at Pharmacies

Takahashi

Ijuin²,

Iwaki

et al. 2006

JOURNAL OF HEALTH SCIENCE

View full text Add to dashboard Cite

show abstract

“…[1][2][3][4][5][6] The techniques of spectral analysis has also been used for common purposes, e.g., power spectrum, auto-correlation function and cross-correlation function. [7][8][9][10][11][12][13][14] The cross-correlation functions are concerned with the time lag of similar or correlated phenomena which occur in distant places or under different situations.…”

Section: Introductionmentioning

confidence: 99%

Smoothing of Correlation Functions in the Estimation of the Order of Influenza Infection between Adults and Children

Takahashi

Kobari²,

Ijuin³

et al. 2006

JOURNAL OF HEALTH SCIENCE

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The cross-correlation functions between the time series of drug sales at community pharmacies have been used for the epidemiological understanding of influenza propagation process. The aim of this paper is to examine the methods of smoothing for determining the maximum position (time lag) of the cross-correlation function. The moving average method with a window of seven days is taken to eliminate the hebdomadal frequencies originating from the life style of people around the pharmacies, or rather, purchasers. Based on the smoothed data of influenza anti-viral agents for adults and children at pharmacies in Tokyo and its vicinity, this paper tries to give an answer to the question of which are earlier infected with influenza, adults or children, in the limited areas.

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MEM spectral analysis for predicting influenza epidemics in Japan

Sumi

Kamo

2011

Environ Health Prev Med

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Objectives The prediction of influenza epidemics has long been the focus of attention in epidemiology and mathematical biology. In this study, we tested whether time series analysis was useful for predicting the incidence of influenza in Japan. Methods The method of time series analysis we used consists of spectral analysis based on the maximum entropy method (MEM) in the frequency domain and the nonlinear least squares method in the time domain. Using this time series analysis, we analyzed the incidence data of influenza in Japan from January 1948 to December 1998; these data are unique in that they covered the periods of pandemics in Japan in 1957, 1968, and 1977. Results On the basis of the MEM spectral analysis, we identified the periodic modes explaining the underlying variations of the incidence data. The optimum least squares fitting (LSF) curve calculated with the periodic modes reproduced the underlying variation of the incidence data. An extension of the LSF curve could be used to predict the incidence of influenza quantitatively. Conclusions Our study suggested that MEM spectral analysis would allow us to model temporal variations of influenza epidemics with multiple periodic modes much more effectively than by using the method of conventional time series analysis, which has been used previously to investigate the behavior of temporal variations in influenza data.

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Prediction Analysis for Measles Epidemics

Cited by 18 publications

References 8 publications

Stochastic Study on the Sales Pattern of Influenza Anti-Viral Agents at Pharmacies

Stochastic Study on the Sales Pattern of Influenza Anti-Viral Agents at Pharmacies

Smoothing of Correlation Functions in the Estimation of the Order of Influenza Infection between Adults and Children

MEM spectral analysis for predicting influenza epidemics in Japan

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