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Comparing the similarity and difference of three influenza surveillance systems in China

Three main surveillance systems (laboratory-confirmed, influenza-like illness (ILI) and nationwide Notifiable Infectious Diseases Reporting Information System (NIDRIS)) have been used for influenza surveillance in China. However, it is unclear which surveillance system is more reliable in developing...

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Autores principales: Yang, Xiaoting, Liu, Dongpeng, Wei, Kongfu, Liu, Xinfeng, Meng, Lei, Yu, Deshan, Li, Hongyu, Li, Baodi, He, Jian, Hu, Wenbiao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5809380/
https://www.ncbi.nlm.nih.gov/pubmed/29434230
http://dx.doi.org/10.1038/s41598-018-21059-9
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author Yang, Xiaoting
Liu, Dongpeng
Wei, Kongfu
Liu, Xinfeng
Meng, Lei
Yu, Deshan
Li, Hongyu
Li, Baodi
He, Jian
Hu, Wenbiao
author_facet Yang, Xiaoting
Liu, Dongpeng
Wei, Kongfu
Liu, Xinfeng
Meng, Lei
Yu, Deshan
Li, Hongyu
Li, Baodi
He, Jian
Hu, Wenbiao
author_sort Yang, Xiaoting
collection PubMed
description Three main surveillance systems (laboratory-confirmed, influenza-like illness (ILI) and nationwide Notifiable Infectious Diseases Reporting Information System (NIDRIS)) have been used for influenza surveillance in China. However, it is unclear which surveillance system is more reliable in developing influenza early warning system based on surveillance data. This study aims to evaluate the similarity and difference of the three surveillance systems and provide practical knowledge for improving the effectiveness of influenza surveillance. Weekly influenza data for the three systems were obtained from March 2010 to February 2015. Spearman correlation and time series seasonal decomposition were used to assess the relationship between the three surveillance systems and to explore seasonal patterns and characteristics of influenza epidemics in Gansu, China. Our results showed influenza epidemics appeared a single-peak around January in all three surveillance systems. Time series seasonal decomposition analysis demonstrated a similar seasonal pattern in the three systems, while long-term trends were observed to be different. Our research suggested that a combination of the NIDRIS together with ILI and laboratory-confirmed surveillance is an informative, comprehensive way to monitor influenza transmission in Gansu, China. These results will provide a useful information for developing influenza early warning systems based on influenza surveillance data.
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spelling pubmed-58093802018-02-15 Comparing the similarity and difference of three influenza surveillance systems in China Yang, Xiaoting Liu, Dongpeng Wei, Kongfu Liu, Xinfeng Meng, Lei Yu, Deshan Li, Hongyu Li, Baodi He, Jian Hu, Wenbiao Sci Rep Article Three main surveillance systems (laboratory-confirmed, influenza-like illness (ILI) and nationwide Notifiable Infectious Diseases Reporting Information System (NIDRIS)) have been used for influenza surveillance in China. However, it is unclear which surveillance system is more reliable in developing influenza early warning system based on surveillance data. This study aims to evaluate the similarity and difference of the three surveillance systems and provide practical knowledge for improving the effectiveness of influenza surveillance. Weekly influenza data for the three systems were obtained from March 2010 to February 2015. Spearman correlation and time series seasonal decomposition were used to assess the relationship between the three surveillance systems and to explore seasonal patterns and characteristics of influenza epidemics in Gansu, China. Our results showed influenza epidemics appeared a single-peak around January in all three surveillance systems. Time series seasonal decomposition analysis demonstrated a similar seasonal pattern in the three systems, while long-term trends were observed to be different. Our research suggested that a combination of the NIDRIS together with ILI and laboratory-confirmed surveillance is an informative, comprehensive way to monitor influenza transmission in Gansu, China. These results will provide a useful information for developing influenza early warning systems based on influenza surveillance data. Nature Publishing Group UK 2018-02-12 /pmc/articles/PMC5809380/ /pubmed/29434230 http://dx.doi.org/10.1038/s41598-018-21059-9 Text en © The Author(s) 2018 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Yang, Xiaoting
Liu, Dongpeng
Wei, Kongfu
Liu, Xinfeng
Meng, Lei
Yu, Deshan
Li, Hongyu
Li, Baodi
He, Jian
Hu, Wenbiao
Comparing the similarity and difference of three influenza surveillance systems in China
title Comparing the similarity and difference of three influenza surveillance systems in China
title_full Comparing the similarity and difference of three influenza surveillance systems in China
title_fullStr Comparing the similarity and difference of three influenza surveillance systems in China
title_full_unstemmed Comparing the similarity and difference of three influenza surveillance systems in China
title_short Comparing the similarity and difference of three influenza surveillance systems in China
title_sort comparing the similarity and difference of three influenza surveillance systems in china
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5809380/
https://www.ncbi.nlm.nih.gov/pubmed/29434230
http://dx.doi.org/10.1038/s41598-018-21059-9
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