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

Chunli Wang, Yongdong Li, Wei Feng, Kui Liu, Shu Zhang, Fengjiao Hu, Suli Jiao, Xuying Lao, Hongxia Ni and Guozhang Xu
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Chunli Wang: Department of Chronic Diseases and Community Health, Fenghua Municipal Center for Disease Control and Prevention, Ningbo 315500, China
Yongdong Li: Department of Virus Research, Ningbo Municipal Center for Disease Control and Prevention, Ningbo 315010, China
Wei Feng: Department of Chronic Diseases and Community Health, Fenghua Municipal Center for Disease Control and Prevention, Ningbo 315500, China
Kui Liu: Department of Science Research and Information Management, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou 310051, China
Shu Zhang: Department of Virus Research, Ningbo Municipal Center for Disease Control and Prevention, Ningbo 315010, China
Fengjiao Hu: Department of Virus Research, Ningbo Municipal Center for Disease Control and Prevention, Ningbo 315010, China
Suli Jiao: Department of Virus Research, Ningbo Municipal Center for Disease Control and Prevention, Ningbo 315010, China
Xuying Lao: Department of Virus Research, Ningbo Municipal Center for Disease Control and Prevention, Ningbo 315010, China
Hongxia Ni: Department of Virus Research, Ningbo Municipal Center for Disease Control and Prevention, Ningbo 315010, China
Guozhang Xu: Department of Virus Research, Ningbo Municipal Center for Disease Control and Prevention, Ningbo 315010, China

IJERPH, 2017, vol. 14, issue 6, 1-10

Abstract: 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.

Keywords: ARIMA model; influenza; influenza-like illness; prediction (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2017
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