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 ; Influenza, Human/epidemiology* ; Pandemics/prevention & control*

China ; Humans ; Influenza Vaccines ; administration & dosage ; Influenza, Human ; prevention & control

Disease Outbreaks ; prevention & control ; Humans ; Influenza A Virus, H5N1 Subtype ; Influenza Vaccines ; Influenza, Human ; prevention & control

Humans ; Influenza A virus ; Influenza Vaccines ; Influenza, Human ; epidemiology ; prevention & control ; virology

Animals ; Cytokines ; physiology ; Humans ; Influenza A virus ; immunology ; Influenza B virus ; immunology ; Influenza Vaccines ; Influenza, Human ; immunology ; prevention & control ; Vaccines, DNA

Animals ; Birds ; Humans ; Influenza A virus ; classification ; pathogenicity ; Influenza in Birds ; complications ; prevention & control ; virology ; Influenza, Human ; etiology ; prevention & control

Disease Outbreaks ; prevention & control ; Humans ; Influenza A Virus, H5N1 Subtype ; immunology ; Influenza Vaccines ; immunology ; Influenza, Human ; prevention & control ; Seasons ; Vaccination

Disease Outbreaks ; Humans ; Influenza, Human ; epidemiology ; prevention & control

Humans ; Influenza, Human ; prevention & control ; Information Storage and Retrieval ; Internet

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.