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.
Highlights
IntroductionStrongly infective respiratory disease caused by the influenza virus [1]
Influenza is an acute, strongly infective respiratory disease caused by the influenza virus [1].The epidemic and pandemic forms are major health threats to humans
Because influenza-like illness (ILI) is a clinical definition designed to detect potential influenza cases, influenza viruses are most likely to be identified when ILI is used to define cases, and ILI is strongly correlated with the influenza virus isolation rate [14]
Summary
Strongly infective respiratory disease caused by the influenza virus [1]. The epidemic and pandemic forms are major health threats to humans. Disease surveillance, including monitoring of influenza-like illness (ILI) and influenza virus infections, plays a significant role in controlling and preventing influenza epidemics and pandemics [2]. In many parts of the world, in developed regions, the etiologic agents associated with ILI have been well characterized [3]. The epidemiology and etiology of ILI are poorly understood in developing countries, creating challenges for governments when planning interventions and prevention strategies. The lack of data inhibits the modeling of pandemic influenza infections and the development of appropriate control strategies
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