Cargando…

A maximum curvature method for estimating epidemic onset of seasonal influenza in Japan

BACKGROUND: Detecting the onset of influenza epidemic is important for epidemiological surveillance and for investigating the factors driving spatiotemporal transmission patterns. Most approaches define the epidemic onset based on thresholds, which use subjective criteria and are specific to individ...

Descripción completa

Detalles Bibliográficos
Autores principales: Cai, Jun, Zhang, Bing, Xu, Bo, Chan, Karen Kie Yan, Chowell, Gerardo, Tian, Huaiyu, Xu, Bing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6383251/
https://www.ncbi.nlm.nih.gov/pubmed/30786869
http://dx.doi.org/10.1186/s12879-019-3777-x
_version_ 1783396806712885248
author Cai, Jun
Zhang, Bing
Xu, Bo
Chan, Karen Kie Yan
Chowell, Gerardo
Tian, Huaiyu
Xu, Bing
author_facet Cai, Jun
Zhang, Bing
Xu, Bo
Chan, Karen Kie Yan
Chowell, Gerardo
Tian, Huaiyu
Xu, Bing
author_sort Cai, Jun
collection PubMed
description BACKGROUND: Detecting the onset of influenza epidemic is important for epidemiological surveillance and for investigating the factors driving spatiotemporal transmission patterns. Most approaches define the epidemic onset based on thresholds, which use subjective criteria and are specific to individual surveillance systems. METHODS: We applied the empirical threshold method (ETM), together with two non-thresholding methods, including the maximum curvature method (MCM) that we proposed and the segmented regression method (SRM), to determine onsets of influenza epidemics in each prefecture of Japan, using sentinel surveillance data of influenza-like illness (ILI) from 2012/2013 through 2017/2018. Performance of the MCM and SRM was evaluated, in terms of epidemic onset, end, and duration, with those derived from the ETM using the nationwide epidemic onset indicator of 1.0 ILI case per sentinel per week. RESULTS: The MCM and SRM yielded complete estimates for each of Japan’s 47 prefectures. In contrast, ETM estimates for Kagoshima during 2012/2013 and for Okinawa during all six influenza seasons, except 2013/2014, were invalid. The MCM showed better agreement in all estimates with the ETM than the SRM (R(2) = 0.82, p < 0.001 vs. R(2) = 0.34, p < 0.001 for epidemic onset; R(2) = 0.18, p < 0.001 vs. R(2) = 0.05, p < 0.001 for epidemic end; R(2) = 0.28, p < 0.001 vs. R(2) < 0.01, p = 0.35 for epidemic duration). Prefecture-specific thresholds for epidemic onset and end were established using the MCM. CONCLUSIONS: The Japanese national epidemic onset threshold is not applicable to all prefectures, particularly Okinawa. The MCM could be used to establish prefecture-specific epidemic thresholds that faithfully characterize influenza activity, serving as useful complements to the influenza surveillance system in Japan. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12879-019-3777-x) contains supplementary material, which is available to authorized users.
format Online
Article
Text
id pubmed-6383251
institution National Center for Biotechnology Information
language English
publishDate 2019
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-63832512019-03-01 A maximum curvature method for estimating epidemic onset of seasonal influenza in Japan Cai, Jun Zhang, Bing Xu, Bo Chan, Karen Kie Yan Chowell, Gerardo Tian, Huaiyu Xu, Bing BMC Infect Dis Research Article BACKGROUND: Detecting the onset of influenza epidemic is important for epidemiological surveillance and for investigating the factors driving spatiotemporal transmission patterns. Most approaches define the epidemic onset based on thresholds, which use subjective criteria and are specific to individual surveillance systems. METHODS: We applied the empirical threshold method (ETM), together with two non-thresholding methods, including the maximum curvature method (MCM) that we proposed and the segmented regression method (SRM), to determine onsets of influenza epidemics in each prefecture of Japan, using sentinel surveillance data of influenza-like illness (ILI) from 2012/2013 through 2017/2018. Performance of the MCM and SRM was evaluated, in terms of epidemic onset, end, and duration, with those derived from the ETM using the nationwide epidemic onset indicator of 1.0 ILI case per sentinel per week. RESULTS: The MCM and SRM yielded complete estimates for each of Japan’s 47 prefectures. In contrast, ETM estimates for Kagoshima during 2012/2013 and for Okinawa during all six influenza seasons, except 2013/2014, were invalid. The MCM showed better agreement in all estimates with the ETM than the SRM (R(2) = 0.82, p < 0.001 vs. R(2) = 0.34, p < 0.001 for epidemic onset; R(2) = 0.18, p < 0.001 vs. R(2) = 0.05, p < 0.001 for epidemic end; R(2) = 0.28, p < 0.001 vs. R(2) < 0.01, p = 0.35 for epidemic duration). Prefecture-specific thresholds for epidemic onset and end were established using the MCM. CONCLUSIONS: The Japanese national epidemic onset threshold is not applicable to all prefectures, particularly Okinawa. The MCM could be used to establish prefecture-specific epidemic thresholds that faithfully characterize influenza activity, serving as useful complements to the influenza surveillance system in Japan. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12879-019-3777-x) contains supplementary material, which is available to authorized users. BioMed Central 2019-02-20 /pmc/articles/PMC6383251/ /pubmed/30786869 http://dx.doi.org/10.1186/s12879-019-3777-x Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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 Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Cai, Jun
Zhang, Bing
Xu, Bo
Chan, Karen Kie Yan
Chowell, Gerardo
Tian, Huaiyu
Xu, Bing
A maximum curvature method for estimating epidemic onset of seasonal influenza in Japan
title A maximum curvature method for estimating epidemic onset of seasonal influenza in Japan
title_full A maximum curvature method for estimating epidemic onset of seasonal influenza in Japan
title_fullStr A maximum curvature method for estimating epidemic onset of seasonal influenza in Japan
title_full_unstemmed A maximum curvature method for estimating epidemic onset of seasonal influenza in Japan
title_short A maximum curvature method for estimating epidemic onset of seasonal influenza in Japan
title_sort maximum curvature method for estimating epidemic onset of seasonal influenza in japan
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6383251/
https://www.ncbi.nlm.nih.gov/pubmed/30786869
http://dx.doi.org/10.1186/s12879-019-3777-x
work_keys_str_mv AT caijun amaximumcurvaturemethodforestimatingepidemiconsetofseasonalinfluenzainjapan
AT zhangbing amaximumcurvaturemethodforestimatingepidemiconsetofseasonalinfluenzainjapan
AT xubo amaximumcurvaturemethodforestimatingepidemiconsetofseasonalinfluenzainjapan
AT chankarenkieyan amaximumcurvaturemethodforestimatingepidemiconsetofseasonalinfluenzainjapan
AT chowellgerardo amaximumcurvaturemethodforestimatingepidemiconsetofseasonalinfluenzainjapan
AT tianhuaiyu amaximumcurvaturemethodforestimatingepidemiconsetofseasonalinfluenzainjapan
AT xubing amaximumcurvaturemethodforestimatingepidemiconsetofseasonalinfluenzainjapan
AT caijun maximumcurvaturemethodforestimatingepidemiconsetofseasonalinfluenzainjapan
AT zhangbing maximumcurvaturemethodforestimatingepidemiconsetofseasonalinfluenzainjapan
AT xubo maximumcurvaturemethodforestimatingepidemiconsetofseasonalinfluenzainjapan
AT chankarenkieyan maximumcurvaturemethodforestimatingepidemiconsetofseasonalinfluenzainjapan
AT chowellgerardo maximumcurvaturemethodforestimatingepidemiconsetofseasonalinfluenzainjapan
AT tianhuaiyu maximumcurvaturemethodforestimatingepidemiconsetofseasonalinfluenzainjapan
AT xubing maximumcurvaturemethodforestimatingepidemiconsetofseasonalinfluenzainjapan