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

SUMMARY 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.

[1]  A. Sumi,et al.  MEM spectral analysis for predicting influenza epidemics in Japan , 2012, Environmental Health and Preventive Medicine.

[2]  Hideo Tanaka,et al.  Hepatitis B and C Virus Infection and Hepatocellular Carcinoma in China: A Review of Epidemiology and Control Measures , 2011, Journal of epidemiology.

[3]  A. Sumi,et al.  Study on the effect of measles control programmes on periodic structures of disease epidemics in a large Chinese city , 2010, Epidemiology and Infection.

[4]  Lan Zou,et al.  Modeling the transmission dynamics and control of hepatitis B virus in China. , 2010, Journal of theoretical biology.

[5]  Shengli Bi,et al.  General Epidemiological Parameters of Viral Hepatitis A, B, C, and E in Six Regions of China: A Cross-Sectional Study in 2007 , 2009, PloS one.

[6]  R. Aggarwal,et al.  Epidemiology of hepatitis E: Current status , 2009, Journal of gastroenterology and hepatology.

[7]  A. Sumi,et al.  Predicting the incidence of human campylobacteriosis in Finland with time series analysis , 2009, APMIS : acta pathologica, microbiologica, et immunologica Scandinavica.

[8]  A. Sumi,et al.  Relationship of cholera incidence to El Niño and solar activity elucidated by time-series analysis , 2009, Epidemiology and Infection.

[9]  Jonathan K. Mitchell,et al.  Autochthonous hepatitis E in Southwest England: natural history, complications and seasonal variation, and hepatitis E virus IgG seroprevalence in blood donors, the elderly and patients with chronic liver disease , 2008, European journal of gastroenterology & hepatology.

[10]  R S Morris,et al.  Temporal and longitudinal analysis of Danish Swine Salmonellosis Control Programme data: implications for surveillance , 2008, Epidemiology and Infection.

[11]  A. Sumi,et al.  Study of the Effect of Vaccination on Periodic Structures of Measles Epidemics in Japan , 2007, Microbiology and immunology.

[12]  E. Naumova,et al.  Seasonality in six enterically transmitted diseases and ambient temperature , 2006, Epidemiology and Infection.

[13]  K. Ekdahl,et al.  An evaluation and comparison of three commonly used statistical models for automatic detection of outbreaks in epidemiological data of communicable diseases , 2005, Epidemiology and Infection.

[14]  Fangjun Zhou,et al.  A mathematical model to estimate global hepatitis B disease burden and vaccination impact. , 2005, International journal of epidemiology.

[15]  H. Dalton,et al.  Non-travel-associated hepatitis E in England and Wales: demographic, clinical, and molecular epidemiological characteristics. , 2005, The Journal of infectious diseases.

[16]  P. Guan,et al.  Forecasting model for the incidence of hepatitis A based on artificial neural network. , 2004, World journal of gastroenterology.

[17]  Man-Fung Yuen,et al.  Viral hepatitis B , 2003, The Lancet.

[18]  Lars Folke Olsen,et al.  Prediction Analysis for Measles Epidemics , 2003 .

[19]  Marco V José,et al.  Scaling properties and symmetrical patterns in the epidemiology of rotavirus infection. , 2003, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[20]  S. L. Wong,et al.  Modelling death rates for carriers of hepatitis B , 2002, Epidemiology and Infection.

[21]  F F Nobre,et al.  Dynamic linear model and SARIMA: a comparison of their forecasting performance in epidemiology , 2001, Statistics in medicine.

[22]  Z Xu,et al.  A mathematical model of hepatitis B virus transmission and its application for vaccination strategy in china. , 2000, International journal of epidemiology.

[23]  R. Koff.  Seroepidemiology of hepatitis A in the United States. , 1995, The Journal of infectious diseases.

[24]  H. Zhuang,et al.  Epidemiology of hepatitis E in China , 1991, Gastroenterologia Japonica.

[25]  S. Hadler,et al.  A Community-wide epidemic of hepatitis A in Ohio. , 1986, American journal of epidemiology.

[26]  M. Koffler.  Epidemiology of hepatitis. , 1965, Lancet.

[27]  Weinian Zhang,et al.  Author ' s personal copy Modeling the transmission dynamics and control of hepatitis B virus in China $ , 2009 .

[28]  Wang Yong-hon.  Analysis on epidemiological features of Hepatitis A and Hepatitis E in Wuhan City from 2004 to 2009 , 2011 .

[29]  R. Beaglehole,et al.  Public health in China: history and contemporary challenges. , 2009 .

[30]  N. Beeching.  Viral Hepatitis in China: Problems and Control Strategies. Monographs in Virology, volume 19 , 1993 .

[31]  Y. Wen,et al.  Viral hepatitis in China : problems and control strategies , 1992 .

[32]  W P Havens,et al.  Viral hepatitis. , 1970, The Medical clinics of North America.

[33]  Athanasios Papoulis,et al.  Probability, Random Variables and Stochastic Processes , 1965 .