Epidemiological Features and Forecast Model Analysis for the Morbidity of Influenza in Ningbo, China, 2006–2014

International Journal of Environmental Research and Public Health

卷:14

Wei Feng¹  Chunli Wang¹  Kui Liu²  Suli Jiao³  Guozhang Xu³  Yongdong Li³  Shu Zhang³  Fengjiao Hu³  Hongxia Ni³  Xuying Lao³ 

[1] Department of Chronic Diseases and Community Health, Fenghua Municipal Center for Disease Control and Prevention, Ningbo 315500, China;

[2] Department of Science Research and Information Management, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou 310051, China;

[3] Department of Virus Research, Ningbo Municipal Center for Disease Control and Prevention, Ningbo 315010, China;

关键词: ARIMA model;    influenza;    influenza-like illness;    prediction;   

DOI  :  10.3390/ijerph14060559

来源: DOAJ

【 摘 要 】

This study aimed to identify circulating influenza virus strains and vulnerable population groups and investigate the distribution and seasonality of influenza viruses in Ningbo, China. Then, an autoregressive integrated moving average (ARIMA) model for prediction was established. Influenza surveillance data for 2006–2014 were obtained for cases of influenza-like illness (ILI) (n = 129,528) from the municipal Centers for Disease Control and virus surveillance systems of Ningbo, China. The ARIMA model was proposed to predict the expected morbidity cases from January 2015 to December 2015. Of the 13,294 specimens, influenza virus was detected in 1148 (8.64%) samples, including 951 (82.84%) influenza type A and 197 (17.16%) influenza type B viruses; the influenza virus isolation rate was strongly correlated with the rate of ILI during the overall study period (r = 0.20, p < 0.05). The ARIMA (1, 1, 1) (1, 1, 0)12 model could be used to predict the ILI incidence in Ningbo. The seasonal pattern of influenza activity in Ningbo tended to peak during the rainy season and winter. Given those results, the model we established could effectively predict the trend of influenza-related morbidity, providing a methodological basis for future influenza monitoring and control strategies in the study area.

【 授权许可】

Unknown