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Transmission dynamics of cholera in Yemen, 2017: a real time forecasting

BACKGROUND: A large epidemic of cholera, caused by Vibrio cholerae, serotype Ogawa, has been ongoing in Yemen, 2017. To improve the situation awareness, the present study aimed to forecast the cholera epidemic, explicitly addressing the reporting delay and ascertainment bias. METHODS: Using weekly i...

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Autores principales: Nishiura, Hiroshi, Tsuzuki, Shinya, Yuan, Baoyin, Yamaguchi, Takayuki, Asai, Yusuke
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5527441/
https://www.ncbi.nlm.nih.gov/pubmed/28747188
http://dx.doi.org/10.1186/s12976-017-0061-x
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author Nishiura, Hiroshi
Tsuzuki, Shinya
Yuan, Baoyin
Yamaguchi, Takayuki
Asai, Yusuke
author_facet Nishiura, Hiroshi
Tsuzuki, Shinya
Yuan, Baoyin
Yamaguchi, Takayuki
Asai, Yusuke
author_sort Nishiura, Hiroshi
collection PubMed
description BACKGROUND: A large epidemic of cholera, caused by Vibrio cholerae, serotype Ogawa, has been ongoing in Yemen, 2017. To improve the situation awareness, the present study aimed to forecast the cholera epidemic, explicitly addressing the reporting delay and ascertainment bias. METHODS: Using weekly incidence of suspected cases, updated as a revised epidemic curve every week, the reporting delay was explicitly incorporated into the estimation model. Using the weekly case fatality risk as calculated by the World Health Organization, ascertainment bias was adjusted, enabling us to parameterize the family of logistic curves (i.e., logistic and generalized logistic models) for describing the unbiased incidence in 2017. RESULTS: The cumulative incidence at the end of the epidemic, was estimated at 790,778 (95% CI: 700,495, 914,442) cases and 767,029 (95% CI: 690,877, 871,671) cases, respectively, by using logistic and generalized logistic models. It was also estimated that we have just passed through the epidemic peak by week 26, 2017. From week 27 onwards, the weekly incidence was predicted to decrease. CONCLUSIONS: Cholera epidemic in Yemen, 2017 was predicted to soon start to decrease. If the weekly incidence is reported in the up-to-the-minute manner and updated in later weeks, not a single data point but the entire epidemic curve must be precisely updated.
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spelling pubmed-55274412017-08-02 Transmission dynamics of cholera in Yemen, 2017: a real time forecasting Nishiura, Hiroshi Tsuzuki, Shinya Yuan, Baoyin Yamaguchi, Takayuki Asai, Yusuke Theor Biol Med Model Research BACKGROUND: A large epidemic of cholera, caused by Vibrio cholerae, serotype Ogawa, has been ongoing in Yemen, 2017. To improve the situation awareness, the present study aimed to forecast the cholera epidemic, explicitly addressing the reporting delay and ascertainment bias. METHODS: Using weekly incidence of suspected cases, updated as a revised epidemic curve every week, the reporting delay was explicitly incorporated into the estimation model. Using the weekly case fatality risk as calculated by the World Health Organization, ascertainment bias was adjusted, enabling us to parameterize the family of logistic curves (i.e., logistic and generalized logistic models) for describing the unbiased incidence in 2017. RESULTS: The cumulative incidence at the end of the epidemic, was estimated at 790,778 (95% CI: 700,495, 914,442) cases and 767,029 (95% CI: 690,877, 871,671) cases, respectively, by using logistic and generalized logistic models. It was also estimated that we have just passed through the epidemic peak by week 26, 2017. From week 27 onwards, the weekly incidence was predicted to decrease. CONCLUSIONS: Cholera epidemic in Yemen, 2017 was predicted to soon start to decrease. If the weekly incidence is reported in the up-to-the-minute manner and updated in later weeks, not a single data point but the entire epidemic curve must be precisely updated. BioMed Central 2017-07-26 /pmc/articles/PMC5527441/ /pubmed/28747188 http://dx.doi.org/10.1186/s12976-017-0061-x Text en © The Author(s). 2017 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
Nishiura, Hiroshi
Tsuzuki, Shinya
Yuan, Baoyin
Yamaguchi, Takayuki
Asai, Yusuke
Transmission dynamics of cholera in Yemen, 2017: a real time forecasting
title Transmission dynamics of cholera in Yemen, 2017: a real time forecasting
title_full Transmission dynamics of cholera in Yemen, 2017: a real time forecasting
title_fullStr Transmission dynamics of cholera in Yemen, 2017: a real time forecasting
title_full_unstemmed Transmission dynamics of cholera in Yemen, 2017: a real time forecasting
title_short Transmission dynamics of cholera in Yemen, 2017: a real time forecasting
title_sort transmission dynamics of cholera in yemen, 2017: a real time forecasting
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5527441/
https://www.ncbi.nlm.nih.gov/pubmed/28747188
http://dx.doi.org/10.1186/s12976-017-0061-x
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