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. 2018 Sep:74:61-70.
doi: 10.1016/j.ijid.2018.07.003. Epub 2018 Jul 7.

Epidemiology and ARIMA model of positive-rate of influenza viruses among children in Wuhan, China: A nine-year retrospective study

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Free article

Epidemiology and ARIMA model of positive-rate of influenza viruses among children in Wuhan, China: A nine-year retrospective study

Zhirui He et al. Int J Infect Dis. 2018 Sep.
Free article

Abstract

Objective: Influenza is a common childhood disease and protecting children by predicting the positive rate of influenza virus is important as vaccines are not routinely administered in China. Our study aims to describe the epidemiology of influenza viruses among children in Wuhan, China during the past nine influenza seasons (2007-2015) and to predict the positive rate of different types of influenza virus in the future.

Methods: During the last nine influenza seasons (2007-2015), a total of 10,232 nasopharyngeal swabs collected from pediatric outpatients (age <15years) with influenza-like illness (ILI) infections in two sentinel children's hospitals were examined for influenza A and B by real-time one step RT-PCR. An autoregressive integrated moving average (ARIMA) model was used to fit the time series and to predict the future (first half of 2016) positive rates of different types of influenza virus.

Results: A total of 1,341 specimens were positive for influenza A and 490 for influenza B. The majority of infected patients were 1-11 years old (87.7%). The ARIMA model could effectively predict the positive rate of influenza virus in a short time. ARIMA(0,0,11), SARIMA(1,0,0)(0,1,1)12, ARIMA(0,0,1) and SARIMA(0,0,1)(1,0,1)12 were suitable for B(Victoria), B(Yamagata), A(H1N1)pdm09, and A(H3N2), respectively.

Conclusion: Additional policies must be formulated to prevent and control influenza. The wide use of influenza vaccines, especially for influenza B, especially for influenza B(Yamagata) and B(Victoria), can potentially reduce the effects of influenza on children of China.

Keywords: ARIMA model; Children; Influenza virus; Time series analysis.

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