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How to select a proper early warning threshold to detect infectious disease outbreaks based on the China infectious disease automated alert and response system (CIDARS)

BACKGROUND: China Centre for Diseases Control and Prevention (CDC) developed the China Infectious Disease Automated Alert and Response System (CIDARS) in 2005. The CIDARS was used to strengthen infectious disease surveillance and aid in the early warning of outbreak. The CIDARS has been integrated i...

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Autores principales: Wang, Ruiping, Jiang, Yonggen, Michael, Engelgau, Zhao, Genming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5468940/
https://www.ncbi.nlm.nih.gov/pubmed/28606078
http://dx.doi.org/10.1186/s12889-017-4488-0
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author Wang, Ruiping
Jiang, Yonggen
Michael, Engelgau
Zhao, Genming
author_facet Wang, Ruiping
Jiang, Yonggen
Michael, Engelgau
Zhao, Genming
author_sort Wang, Ruiping
collection PubMed
description BACKGROUND: China Centre for Diseases Control and Prevention (CDC) developed the China Infectious Disease Automated Alert and Response System (CIDARS) in 2005. The CIDARS was used to strengthen infectious disease surveillance and aid in the early warning of outbreak. The CIDARS has been integrated into the routine outbreak monitoring efforts of the CDC at all levels in China. Early warning threshold is crucial for outbreak detection in the CIDARS, but CDCs at all level are currently using thresholds recommended by the China CDC, and these recommended thresholds have recognized limitations. Our study therefore seeks to explore an operational method to select the proper early warning threshold according to the epidemic features of local infectious diseases. METHODS: The data used in this study were extracted from the web-based Nationwide Notifiable Infectious Diseases Reporting Information System (NIDRIS), and data for infectious disease cases were organized by calendar week (1–52) and year (2009–2015) in Excel format; Px was calculated using a percentile-based moving window (moving window [5 week*5 year], x), where x represents one of 12 centiles (0.40, 0.45, 0.50….0.95). Outbreak signals for the 12 Px were calculated using the moving percentile method (MPM) based on data from the CIDARS. When the outbreak signals generated by the ‘mean + 2SD’ gold standard were in line with a Px generated outbreak signal for each week during the year of 2014, this Px was then defined as the proper threshold for the infectious disease. Finally, the performance of new selected thresholds for each infectious disease was evaluated by simulated outbreak signals based on 2015 data. RESULTS: Six infectious diseases were selected in this study (chickenpox, mumps, hand foot and mouth diseases (HFMD), scarlet fever, influenza and rubella). Proper thresholds for chickenpox (P75), mumps (P80), influenza (P75), rubella (P45), HFMD (P75), and scarlet fever (P80) were identified. The selected proper thresholds for these 6 infectious diseases could detect almost all simulated outbreaks within a shorter time period compared to thresholds recommended by the China CDC. CONCLUSIONS: It is beneficial to select the proper early warning threshold to detect infectious disease aberrations based on characteristics and epidemic features of local diseases in the CIDARS.
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spelling pubmed-54689402017-06-14 How to select a proper early warning threshold to detect infectious disease outbreaks based on the China infectious disease automated alert and response system (CIDARS) Wang, Ruiping Jiang, Yonggen Michael, Engelgau Zhao, Genming BMC Public Health Research Article BACKGROUND: China Centre for Diseases Control and Prevention (CDC) developed the China Infectious Disease Automated Alert and Response System (CIDARS) in 2005. The CIDARS was used to strengthen infectious disease surveillance and aid in the early warning of outbreak. The CIDARS has been integrated into the routine outbreak monitoring efforts of the CDC at all levels in China. Early warning threshold is crucial for outbreak detection in the CIDARS, but CDCs at all level are currently using thresholds recommended by the China CDC, and these recommended thresholds have recognized limitations. Our study therefore seeks to explore an operational method to select the proper early warning threshold according to the epidemic features of local infectious diseases. METHODS: The data used in this study were extracted from the web-based Nationwide Notifiable Infectious Diseases Reporting Information System (NIDRIS), and data for infectious disease cases were organized by calendar week (1–52) and year (2009–2015) in Excel format; Px was calculated using a percentile-based moving window (moving window [5 week*5 year], x), where x represents one of 12 centiles (0.40, 0.45, 0.50….0.95). Outbreak signals for the 12 Px were calculated using the moving percentile method (MPM) based on data from the CIDARS. When the outbreak signals generated by the ‘mean + 2SD’ gold standard were in line with a Px generated outbreak signal for each week during the year of 2014, this Px was then defined as the proper threshold for the infectious disease. Finally, the performance of new selected thresholds for each infectious disease was evaluated by simulated outbreak signals based on 2015 data. RESULTS: Six infectious diseases were selected in this study (chickenpox, mumps, hand foot and mouth diseases (HFMD), scarlet fever, influenza and rubella). Proper thresholds for chickenpox (P75), mumps (P80), influenza (P75), rubella (P45), HFMD (P75), and scarlet fever (P80) were identified. The selected proper thresholds for these 6 infectious diseases could detect almost all simulated outbreaks within a shorter time period compared to thresholds recommended by the China CDC. CONCLUSIONS: It is beneficial to select the proper early warning threshold to detect infectious disease aberrations based on characteristics and epidemic features of local diseases in the CIDARS. BioMed Central 2017-06-12 /pmc/articles/PMC5468940/ /pubmed/28606078 http://dx.doi.org/10.1186/s12889-017-4488-0 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 Article
Wang, Ruiping
Jiang, Yonggen
Michael, Engelgau
Zhao, Genming
How to select a proper early warning threshold to detect infectious disease outbreaks based on the China infectious disease automated alert and response system (CIDARS)
title How to select a proper early warning threshold to detect infectious disease outbreaks based on the China infectious disease automated alert and response system (CIDARS)
title_full How to select a proper early warning threshold to detect infectious disease outbreaks based on the China infectious disease automated alert and response system (CIDARS)
title_fullStr How to select a proper early warning threshold to detect infectious disease outbreaks based on the China infectious disease automated alert and response system (CIDARS)
title_full_unstemmed How to select a proper early warning threshold to detect infectious disease outbreaks based on the China infectious disease automated alert and response system (CIDARS)
title_short How to select a proper early warning threshold to detect infectious disease outbreaks based on the China infectious disease automated alert and response system (CIDARS)
title_sort how to select a proper early warning threshold to detect infectious disease outbreaks based on the china infectious disease automated alert and response system (cidars)
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5468940/
https://www.ncbi.nlm.nih.gov/pubmed/28606078
http://dx.doi.org/10.1186/s12889-017-4488-0
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