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Seasonal Activity of Influenza in Iran: Application of Influenza-like Illness Data from Sentinel Sites of Healthcare Centers during 2010 to 2015
This study aimed to predict seasonal influenza activity and detection of influenza outbreaks. Data of all registered cases (n = 53,526) of influenza-like illnesses (ILIs) from sentinel sites of healthcare centers in Iran were obtained from the FluNet web-based tool, World Health Organization (WHO),...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Atlantis Press
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7325813/ https://www.ncbi.nlm.nih.gov/pubmed/30859784 http://dx.doi.org/10.2991/j.jegh.2018.08.100 |
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author | Hosseini, Seyedhadi Karami, Manoochehr Farhadian, Maryam Mohammadi, Younes |
author_facet | Hosseini, Seyedhadi Karami, Manoochehr Farhadian, Maryam Mohammadi, Younes |
author_sort | Hosseini, Seyedhadi |
collection | PubMed |
description | This study aimed to predict seasonal influenza activity and detection of influenza outbreaks. Data of all registered cases (n = 53,526) of influenza-like illnesses (ILIs) from sentinel sites of healthcare centers in Iran were obtained from the FluNet web-based tool, World Health Organization (WHO), from 2010 to 2015. The status of the ILI activity was obtained from the FluNet and considered as the gold standard of the seasonal activity of influenza during the study period. The cumulative sum (CUSUM) as an outbreak detection method was used to predict the seasonal activity of influenza. Also, time series similarity between the ILI trend and CUSUM was assessed using the cross-correlogram. Of 7684 (14%) positive cases of influenza, about 71% were type A virus and 28% were type B virus. The majority of the outbreaks occurred in winter and autumn. Results of the cross-correlogram showed that there was a considerable similarity between time series graphs of the ILI cases and CUSUM values. However, the CUSUM algorithm did not have a good performance in the timely detection of influenza activity. Despite a considerable similarity between time series of the ILI cases and CUSUM algorithm in weekly lag, the seasonal activity of influenza in Iran could not be predicted by the CUSUM algorithm. HIGHLIGHTS: • Activity of influenza in Iran could not be predicted by CUSUM algorithm. • The majority of the outbreaks occurred in winter and autumn. • CUSUM algorithm did not have a good performance in detection of influenza outbreaks. |
format | Online Article Text |
id | pubmed-7325813 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Atlantis Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-73258132020-07-28 Seasonal Activity of Influenza in Iran: Application of Influenza-like Illness Data from Sentinel Sites of Healthcare Centers during 2010 to 2015 Hosseini, Seyedhadi Karami, Manoochehr Farhadian, Maryam Mohammadi, Younes J Epidemiol Glob Health Original Article This study aimed to predict seasonal influenza activity and detection of influenza outbreaks. Data of all registered cases (n = 53,526) of influenza-like illnesses (ILIs) from sentinel sites of healthcare centers in Iran were obtained from the FluNet web-based tool, World Health Organization (WHO), from 2010 to 2015. The status of the ILI activity was obtained from the FluNet and considered as the gold standard of the seasonal activity of influenza during the study period. The cumulative sum (CUSUM) as an outbreak detection method was used to predict the seasonal activity of influenza. Also, time series similarity between the ILI trend and CUSUM was assessed using the cross-correlogram. Of 7684 (14%) positive cases of influenza, about 71% were type A virus and 28% were type B virus. The majority of the outbreaks occurred in winter and autumn. Results of the cross-correlogram showed that there was a considerable similarity between time series graphs of the ILI cases and CUSUM values. However, the CUSUM algorithm did not have a good performance in the timely detection of influenza activity. Despite a considerable similarity between time series of the ILI cases and CUSUM algorithm in weekly lag, the seasonal activity of influenza in Iran could not be predicted by the CUSUM algorithm. HIGHLIGHTS: • Activity of influenza in Iran could not be predicted by CUSUM algorithm. • The majority of the outbreaks occurred in winter and autumn. • CUSUM algorithm did not have a good performance in detection of influenza outbreaks. Atlantis Press 2018-12 2018-12 /pmc/articles/PMC7325813/ /pubmed/30859784 http://dx.doi.org/10.2991/j.jegh.2018.08.100 Text en © 2018 Atlantis Press International B.V. This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/). |
spellingShingle | Original Article Hosseini, Seyedhadi Karami, Manoochehr Farhadian, Maryam Mohammadi, Younes Seasonal Activity of Influenza in Iran: Application of Influenza-like Illness Data from Sentinel Sites of Healthcare Centers during 2010 to 2015 |
title | Seasonal Activity of Influenza in Iran: Application of Influenza-like Illness Data from Sentinel Sites of Healthcare Centers during 2010 to 2015 |
title_full | Seasonal Activity of Influenza in Iran: Application of Influenza-like Illness Data from Sentinel Sites of Healthcare Centers during 2010 to 2015 |
title_fullStr | Seasonal Activity of Influenza in Iran: Application of Influenza-like Illness Data from Sentinel Sites of Healthcare Centers during 2010 to 2015 |
title_full_unstemmed | Seasonal Activity of Influenza in Iran: Application of Influenza-like Illness Data from Sentinel Sites of Healthcare Centers during 2010 to 2015 |
title_short | Seasonal Activity of Influenza in Iran: Application of Influenza-like Illness Data from Sentinel Sites of Healthcare Centers during 2010 to 2015 |
title_sort | seasonal activity of influenza in iran: application of influenza-like illness data from sentinel sites of healthcare centers during 2010 to 2015 |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7325813/ https://www.ncbi.nlm.nih.gov/pubmed/30859784 http://dx.doi.org/10.2991/j.jegh.2018.08.100 |
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