Zhirui He1, Hongbing Tao2. 1. Department of Health Management, School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, No. 13 Hongkong Road, Wuhan 430030, China; Wuhan Children's Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Huazhong University of Science & Technology, No. 100 Hongkong Road, Wuhan 430016, China. Electronic address: hzr@hust.edu.cn. 2. Department of Health Management, School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, No. 13 Hongkong Road, Wuhan 430030, China. Electronic address: hhbtao@hust.edu.cn.
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.
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 infectedpatients 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.
Authors: Rafael Bomfim; Sen Pei; Jeffrey Shaman; Teresa Yamana; Hernán A Makse; José S Andrade; Antonio S Lima Neto; Vasco Furtado Journal: J R Soc Interface Date: 2020-10-28 Impact factor: 4.293
Authors: Rui Zhang; Zhen Guo; Yujie Meng; Songwang Wang; Shaoqiong Li; Ran Niu; Yu Wang; Qing Guo; Yonghong Li Journal: Int J Environ Res Public Health Date: 2021-06-07 Impact factor: 3.390