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Seasonal pattern of influenza activity in a subtropical city, China, 2010–2015

Influenza seasonality study is critical for policy-makers to choose an optimal time for influenza vaccination campaign, especially for subtropical regions where influenza seasonality and periodicity are unclear. In this study, we explored the seasonality and periodicity of influenza in Hefei, China...

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Autores principales: Liu, Xu-Xiang, Li, Yahong, Zhu, Yibing, Zhang, Juanjuan, Li, Xiaoru, Zhang, Junqing, Zhao, Kefu, Hu, Mingxia, Qin, Guoyou, Wang, Xi-Ling
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5727502/
https://www.ncbi.nlm.nih.gov/pubmed/29235535
http://dx.doi.org/10.1038/s41598-017-17806-z
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author Liu, Xu-Xiang
Li, Yahong
Zhu, Yibing
Zhang, Juanjuan
Li, Xiaoru
Zhang, Junqing
Zhao, Kefu
Hu, Mingxia
Qin, Guoyou
Wang, Xi-Ling
author_facet Liu, Xu-Xiang
Li, Yahong
Zhu, Yibing
Zhang, Juanjuan
Li, Xiaoru
Zhang, Junqing
Zhao, Kefu
Hu, Mingxia
Qin, Guoyou
Wang, Xi-Ling
author_sort Liu, Xu-Xiang
collection PubMed
description Influenza seasonality study is critical for policy-makers to choose an optimal time for influenza vaccination campaign, especially for subtropical regions where influenza seasonality and periodicity are unclear. In this study, we explored the seasonality and periodicity of influenza in Hefei, China during 2010 to 2015 using five proxies originated from three data sources of clinical surveillance of influenza-like illness (ILI), laboratory surveillance of influenza and death registration of pneumonia and influenza. We combined both wavelets analysis and de-linear-trend regression with Fourier harmonic terms to estimate seasonal characteristics of epidemic phase, peak time, amplitude, ratio of dominant seasonality. We found both annual cycle of influenza epidemics peaking in December-February and semi-annual cycle peaking in December-February and June-July in subtropical city Hefei, China. Compared to proxies developed by ILI and death registration data separately, influenza proxies incorporated laboratory surveillance data performed better seasonality and periodicity, especially in semi-annual periodicity in Hefei. Proxy of ILI consultation rate showed more timeliness peak than other proxies, and could be useful in developing the early warning model for influenza epidemics. Our study suggests to integrate clinical and laboratory surveillance of influenza for future influenza seasonality studies in subtropical regions.
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spelling pubmed-57275022017-12-18 Seasonal pattern of influenza activity in a subtropical city, China, 2010–2015 Liu, Xu-Xiang Li, Yahong Zhu, Yibing Zhang, Juanjuan Li, Xiaoru Zhang, Junqing Zhao, Kefu Hu, Mingxia Qin, Guoyou Wang, Xi-Ling Sci Rep Article Influenza seasonality study is critical for policy-makers to choose an optimal time for influenza vaccination campaign, especially for subtropical regions where influenza seasonality and periodicity are unclear. In this study, we explored the seasonality and periodicity of influenza in Hefei, China during 2010 to 2015 using five proxies originated from three data sources of clinical surveillance of influenza-like illness (ILI), laboratory surveillance of influenza and death registration of pneumonia and influenza. We combined both wavelets analysis and de-linear-trend regression with Fourier harmonic terms to estimate seasonal characteristics of epidemic phase, peak time, amplitude, ratio of dominant seasonality. We found both annual cycle of influenza epidemics peaking in December-February and semi-annual cycle peaking in December-February and June-July in subtropical city Hefei, China. Compared to proxies developed by ILI and death registration data separately, influenza proxies incorporated laboratory surveillance data performed better seasonality and periodicity, especially in semi-annual periodicity in Hefei. Proxy of ILI consultation rate showed more timeliness peak than other proxies, and could be useful in developing the early warning model for influenza epidemics. Our study suggests to integrate clinical and laboratory surveillance of influenza for future influenza seasonality studies in subtropical regions. Nature Publishing Group UK 2017-12-13 /pmc/articles/PMC5727502/ /pubmed/29235535 http://dx.doi.org/10.1038/s41598-017-17806-z Text en © The Author(s) 2017 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
Liu, Xu-Xiang
Li, Yahong
Zhu, Yibing
Zhang, Juanjuan
Li, Xiaoru
Zhang, Junqing
Zhao, Kefu
Hu, Mingxia
Qin, Guoyou
Wang, Xi-Ling
Seasonal pattern of influenza activity in a subtropical city, China, 2010–2015
title Seasonal pattern of influenza activity in a subtropical city, China, 2010–2015
title_full Seasonal pattern of influenza activity in a subtropical city, China, 2010–2015
title_fullStr Seasonal pattern of influenza activity in a subtropical city, China, 2010–2015
title_full_unstemmed Seasonal pattern of influenza activity in a subtropical city, China, 2010–2015
title_short Seasonal pattern of influenza activity in a subtropical city, China, 2010–2015
title_sort seasonal pattern of influenza activity in a subtropical city, china, 2010–2015
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5727502/
https://www.ncbi.nlm.nih.gov/pubmed/29235535
http://dx.doi.org/10.1038/s41598-017-17806-z
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