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

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 surveill...

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Autores principales: Wang, Chunli, Li, Yongdong, Feng, Wei, Liu, Kui, Zhang, Shu, Hu, Fengjiao, Jiao, Suli, Lao, Xuying, Ni, Hongxia, Xu, Guozhang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5486245/
https://www.ncbi.nlm.nih.gov/pubmed/28587073
http://dx.doi.org/10.3390/ijerph14060559
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author Wang, Chunli
Li, Yongdong
Feng, Wei
Liu, Kui
Zhang, Shu
Hu, Fengjiao
Jiao, Suli
Lao, Xuying
Ni, Hongxia
Xu, Guozhang
author_facet Wang, Chunli
Li, Yongdong
Feng, Wei
Liu, Kui
Zhang, Shu
Hu, Fengjiao
Jiao, Suli
Lao, Xuying
Ni, Hongxia
Xu, Guozhang
author_sort Wang, Chunli
collection PubMed
description 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.
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spelling pubmed-54862452017-06-30 Epidemiological Features and Forecast Model Analysis for the Morbidity of Influenza in Ningbo, China, 2006–2014 Wang, Chunli Li, Yongdong Feng, Wei Liu, Kui Zhang, Shu Hu, Fengjiao Jiao, Suli Lao, Xuying Ni, Hongxia Xu, Guozhang Int J Environ Res Public Health Article 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. MDPI 2017-05-25 2017-06 /pmc/articles/PMC5486245/ /pubmed/28587073 http://dx.doi.org/10.3390/ijerph14060559 Text en © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Wang, Chunli
Li, Yongdong
Feng, Wei
Liu, Kui
Zhang, Shu
Hu, Fengjiao
Jiao, Suli
Lao, Xuying
Ni, Hongxia
Xu, Guozhang
Epidemiological Features and Forecast Model Analysis for the Morbidity of Influenza in Ningbo, China, 2006–2014
title Epidemiological Features and Forecast Model Analysis for the Morbidity of Influenza in Ningbo, China, 2006–2014
title_full Epidemiological Features and Forecast Model Analysis for the Morbidity of Influenza in Ningbo, China, 2006–2014
title_fullStr Epidemiological Features and Forecast Model Analysis for the Morbidity of Influenza in Ningbo, China, 2006–2014
title_full_unstemmed Epidemiological Features and Forecast Model Analysis for the Morbidity of Influenza in Ningbo, China, 2006–2014
title_short Epidemiological Features and Forecast Model Analysis for the Morbidity of Influenza in Ningbo, China, 2006–2014
title_sort epidemiological features and forecast model analysis for the morbidity of influenza in ningbo, china, 2006–2014
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5486245/
https://www.ncbi.nlm.nih.gov/pubmed/28587073
http://dx.doi.org/10.3390/ijerph14060559
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