1.Research progress on early warning model of influenza.
Xing Xing ZHANG ; Lu Zhao FENG ; Sheng Jie LAI ; Li Bing MA ; Ting ZHANG ; Jin YANG ; Qing WANG ; Wei Zhong YANG
Chinese Journal of Preventive Medicine 2022;56(11):1576-1583
Influenza is an acute respiratory infectious disease caused by influenza virus. It usually exhibits seasonal transmission, but the novel influenza strain can lead to a pandemic with severe human health and socioeconomic consequences. Early warning of influenza epidemic is an important strategy and means for influenza prevention and control. On the basis of reviewing the main influenza surveillance and early warning systems, this study summarizes the principles, applications, advantages and disadvantages, and development prospects of common influenza early warning models, in order to provide reference for research and application of early warning technology for influenza and other acute respiratory infectious diseases.
Humans
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Influenza, Human/epidemiology*
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Pandemics/prevention & control*
2.Technical guidelines for the application of seasonal influenza vaccine in China (2014-2015).
Luzhao FENG ; Peng YANG ; Tao ZHANG ; Juan YANG ; Chuanxi FU ; Ying QIN ; Yi ZHANG ; Chunna MA ; Zhaoqiu LIU ; Quanyi WANG ; Genming ZHAO ; Hongjie YU ; null ; null
Chinese Journal of Epidemiology 2014;35(12):1295-1319
5.Influenza DNA vaccine: an update.
Chinese Medical Journal 2004;117(1):125-132
10.The application of time series analysis in predicting the influenza incidence and early warning.
Meng ZHU ; Rong-qiang ZU ; Xiang HUO ; Chang-jun BAO ; Yang ZHAO ; Zhi-hang PENG ; Rong-bin YU ; Hong-bing SHEN ; Feng CHEN
Chinese Journal of Preventive Medicine 2011;45(12):1108-1111
OBJECTIVEThis research aimed to explore the application of ARIMA model of time series analysis in predicting influenza incidence and early warning in Jiangsu province and to provide scientific evidence for the prevention and control of influenza epidemic.
METHODSThe database was created based on the data collected from monitoring sites in Jiangsu province from October 2005 to February 2010. The ARIMA model was constructed based on the number of weekly influenza-like illness (ILI) cases. Then the achieved ARIMA model was used to predict the number of influenza-like illness cases of March and April in 2010.
RESULTSThe ARIMA model of the influenza-like illness cases was (1 + 0.785B(2))(1-B) ln X(t) = (1 + 0.622B(2))ε(t). Here B stands for back shift operator, t stands for time, X(t) stands for the number of weekly ILI cases and ε(t) stands for random error. The residual error with 16 lags was white noise and the Ljung-Box test statistic for the model was 5.087, giving a P-value of 0.995. The model fitted the data well. True values of influenza-like illness cases from March 2010 to April 2010 were within 95%CI of predicted values obtained from present model.
CONCLUSIONThe ARIMA model fits the trend of influenza-like illness in Jiangsu province.
Humans ; Influenza, Human ; prevention & control ; Models, Statistical ; Time Factors