Applying the moving epidemic method to determine influenza epidemic and intensity thresholds using influenza‐like illness surveillance data 2009‐2018 in Tunisia
Background Defining the start and assessing the intensity of influenza seasons are essential to ensure timely preventive and control measures and to contribute to the pandemic preparedness. The present study aimed to determine the epidemic and intensity thresholds of influenza season in Tunisia usin...
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Published in: | Influenza and other respiratory viruses Vol. 14; no. 5; pp. 507 - 514 |
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Abstract | Background
Defining the start and assessing the intensity of influenza seasons are essential to ensure timely preventive and control measures and to contribute to the pandemic preparedness. The present study aimed to determine the epidemic and intensity thresholds of influenza season in Tunisia using the moving epidemic method.
Methods
We applied the moving epidemic method (MEM) using the R Language implementation (package “mem”). We have calculated the epidemic and the different intensity thresholds from historical data of the past nine influenza seasons (2009‐2010 to 2017‐2018) and assessed the impact of the 2009‐2010 pandemic year. Data used were the weekly influenza‐like illness (ILI) proportions compared with all outpatient acute consultations. The goodness of the model was assessed using a cross validation procedure.
Results
The average duration of influenza epidemic during a typical season was 20 weeks and ranged from 11 weeks (2009‐2010 season) to 23 weeks (2015‐2016 season). The epidemic threshold with the exclusion of the pandemic season was 6.25%. It had a very high sensitivity of 85% and a high specificity of 69%. The different levels of intensity were established as follows: low, if ILI proportion is below 9.74%, medium below 12.05%; high below 13.27%; and very high above this last rate.
Conclusions
This is the first mathematically based study of seasonal threshold of influenza in Tunisia. As in other studies in different countries, the model has shown both good specificity and sensitivity, which allows timely and accurate detection of the start of influenza seasons. The findings will contribute to the development of more efficient measures for influenza prevention and control. |
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AbstractList | BACKGROUNDDefining the start and assessing the intensity of influenza seasons are essential to ensure timely preventive and control measures and to contribute to the pandemic preparedness. The present study aimed to determine the epidemic and intensity thresholds of influenza season in Tunisia using the moving epidemic method. METHODSWe applied the moving epidemic method (MEM) using the R Language implementation (package "mem"). We have calculated the epidemic and the different intensity thresholds from historical data of the past nine influenza seasons (2009-2010 to 2017-2018) and assessed the impact of the 2009-2010 pandemic year. Data used were the weekly influenza-like illness (ILI) proportions compared with all outpatient acute consultations. The goodness of the model was assessed using a cross validation procedure. RESULTSThe average duration of influenza epidemic during a typical season was 20 weeks and ranged from 11 weeks (2009-2010 season) to 23 weeks (2015-2016 season). The epidemic threshold with the exclusion of the pandemic season was 6.25%. It had a very high sensitivity of 85% and a high specificity of 69%. The different levels of intensity were established as follows: low, if ILI proportion is below 9.74%, medium below 12.05%; high below 13.27%; and very high above this last rate. CONCLUSIONSThis is the first mathematically based study of seasonal threshold of influenza in Tunisia. As in other studies in different countries, the model has shown both good specificity and sensitivity, which allows timely and accurate detection of the start of influenza seasons. The findings will contribute to the development of more efficient measures for influenza prevention and control. Background: Defining the start and assessing the intensity of influenza seasons are essential to ensure timely preventive and control measures and to contribute to the pandemic preparedness. The present study aimed to determine the epidemic and intensity thresholds of influenza season in Tunisia using the moving epidemic method. Methods: We applied the moving epidemic method (MEM) using the R Language implementation (package "mem"). We have calculated the epidemic and the different intensity thresholds from historical data of the past nine influenza seasons (2009-2010 to 2017-2018) and assessed the impact of the 2009-2010 pandemic year. Data used were the weekly influenza-like illness (ILI) proportions compared with all outpatient acute consultations. The goodness of the model was assessed using a cross validation procedure. Results: The average duration of influenza epidemic during a typical season was 20 weeks and ranged from 11 weeks (2009-2010 season) to 23 weeks (2015-2016 season). The epidemic threshold with the exclusion of the pandemic season was 6.25%. It had a very high sensitivity of 85% and a high specificity of 69%. The different levels of intensity were established as follows: low, if ILI proportion is below 9.74%, medium below 12.05%; high below 13.27%; and very high above this last rate. Conclusions: This is the first mathematically based study of seasonal threshold of influenza in Tunisia. As in other studies in different countries, the model has shown both good specificity and sensitivity, which allows timely and accurate detection of the start of influenza seasons. The findings will contribute to the development of more efficient measures for influenza prevention and control. Background Defining the start and assessing the intensity of influenza seasons are essential to ensure timely preventive and control measures and to contribute to the pandemic preparedness. The present study aimed to determine the epidemic and intensity thresholds of influenza season in Tunisia using the moving epidemic method. Methods We applied the moving epidemic method (MEM) using the R Language implementation (package “mem”). We have calculated the epidemic and the different intensity thresholds from historical data of the past nine influenza seasons (2009‐2010 to 2017‐2018) and assessed the impact of the 2009‐2010 pandemic year. Data used were the weekly influenza‐like illness (ILI) proportions compared with all outpatient acute consultations. The goodness of the model was assessed using a cross validation procedure. Results The average duration of influenza epidemic during a typical season was 20 weeks and ranged from 11 weeks (2009‐2010 season) to 23 weeks (2015‐2016 season). The epidemic threshold with the exclusion of the pandemic season was 6.25%. It had a very high sensitivity of 85% and a high specificity of 69%. The different levels of intensity were established as follows: low, if ILI proportion is below 9.74%, medium below 12.05%; high below 13.27%; and very high above this last rate. Conclusions This is the first mathematically based study of seasonal threshold of influenza in Tunisia. As in other studies in different countries, the model has shown both good specificity and sensitivity, which allows timely and accurate detection of the start of influenza seasons. The findings will contribute to the development of more efficient measures for influenza prevention and control. Defining the start and assessing the intensity of influenza seasons are essential to ensure timely preventive and control measures and to contribute to the pandemic preparedness. The present study aimed to determine the epidemic and intensity thresholds of influenza season in Tunisia using the moving epidemic method. We applied the moving epidemic method (MEM) using the R Language implementation (package "mem"). We have calculated the epidemic and the different intensity thresholds from historical data of the past nine influenza seasons (2009-2010 to 2017-2018) and assessed the impact of the 2009-2010 pandemic year. Data used were the weekly influenza-like illness (ILI) proportions compared with all outpatient acute consultations. The goodness of the model was assessed using a cross validation procedure. The average duration of influenza epidemic during a typical season was 20 weeks and ranged from 11 weeks (2009-2010 season) to 23 weeks (2015-2016 season). The epidemic threshold with the exclusion of the pandemic season was 6.25%. It had a very high sensitivity of 85% and a high specificity of 69%. The different levels of intensity were established as follows: low, if ILI proportion is below 9.74%, medium below 12.05%; high below 13.27%; and very high above this last rate. This is the first mathematically based study of seasonal threshold of influenza in Tunisia. As in other studies in different countries, the model has shown both good specificity and sensitivity, which allows timely and accurate detection of the start of influenza seasons. The findings will contribute to the development of more efficient measures for influenza prevention and control. Defining the start and assessing the intensity of influenza seasons are essential to ensure timely preventive and control measures and to contribute to the pandemic preparedness. The present study aimed to determine the epidemic and intensity thresholds of influenza season in Tunisia using the moving epidemic method. We applied the moving epidemic method (MEM) using the R Language implementation (package "mem"). We have calculated the epidemic and the different intensity thresholds from historical data of the past nine influenza seasons (2009-2010 to 2017-2018) and assessed the impact of the 2009-2010 pandemic year. Data used were the weekly influenza-like illness (ILI) proportions compared with all outpatient acute consultations. The goodness of the model was assessed using a cross validation procedure. The average duration of influenza epidemic during a typical season was 20 weeks and ranged from 11 weeks (2009-2010 season) to 23 weeks (2015-2016 season). The epidemic threshold with the exclusion of the pandemic season was 6.25%. It had a very high sensitivity of 85% and a high specificity of 69%. The different levels of intensity were established as follows: low, if ILI proportion is below 9.74%, medium below 12.05%; high below 13.27%; and very high above this last rate. This is the first mathematically based study of seasonal threshold of influenza in Tunisia. As in other studies in different countries, the model has shown both good specificity and sensitivity, which allows timely and accurate detection of the start of influenza seasons. The findings will contribute to the development of more efficient measures for influenza prevention and control. |
Audience | Academic |
Author | Abid, Salma Bettaieb, Jihene Abdeddaiem, Naima Bouabid, Leila Bouguerra, Hind Yazidi, Rihab Bouafif Ben Alaya, Nissaf Cherif, Amal Kouni Chahed, Mohamed Maazaoui, Latifa Zorraga, Mokhtar Bougatef, Souha Amine, Slim Ben Salah, Afif Boutouria, Elyes ElMoussi, Awatef |
AuthorAffiliation | 5 Faculté de Médecine de Tunis Université de Tunis El Manar Tunis Tunisia 4 Microbiology Laboratory Virology Unit Charles Nicolle Hospital Tunis Tunisia 6 Faculté de Médecine de Tunis LR01ES04 Epidémiologie et Prévention des Maladies Cardiovasculaires en Tunisie Université de Tunis El Manar Tunis Tunisia 2 Direction of Primary Health Care Tunis Tunisia 3 Pasteur Institute of Tunis Tunis Tunisia 1 National Observatory of New and Emerging Diseases Ministry of Health Tunis Tunisia |
AuthorAffiliation_xml | – name: 5 Faculté de Médecine de Tunis Université de Tunis El Manar Tunis Tunisia – name: 1 National Observatory of New and Emerging Diseases Ministry of Health Tunis Tunisia – name: 3 Pasteur Institute of Tunis Tunis Tunisia – name: 2 Direction of Primary Health Care Tunis Tunisia – name: 6 Faculté de Médecine de Tunis LR01ES04 Epidémiologie et Prévention des Maladies Cardiovasculaires en Tunisie Université de Tunis El Manar Tunis Tunisia – name: 4 Microbiology Laboratory Virology Unit Charles Nicolle Hospital Tunis Tunisia |
Author_xml | – sequence: 1 givenname: Hind surname: Bouguerra fullname: Bouguerra, Hind organization: Ministry of Health – sequence: 2 givenname: Elyes surname: Boutouria fullname: Boutouria, Elyes organization: Ministry of Health – sequence: 3 givenname: Mokhtar surname: Zorraga fullname: Zorraga, Mokhtar organization: Direction of Primary Health Care – sequence: 4 givenname: Amal surname: Cherif fullname: Cherif, Amal organization: Ministry of Health – sequence: 5 givenname: Rihab surname: Yazidi fullname: Yazidi, Rihab organization: Pasteur Institute of Tunis – sequence: 6 givenname: Naima surname: Abdeddaiem fullname: Abdeddaiem, Naima organization: Direction of Primary Health Care – sequence: 7 givenname: Latifa surname: Maazaoui fullname: Maazaoui, Latifa organization: Direction of Primary Health Care – sequence: 8 givenname: Awatef surname: ElMoussi fullname: ElMoussi, Awatef organization: Charles Nicolle Hospital – sequence: 9 givenname: Salma surname: Abid fullname: Abid, Salma organization: Charles Nicolle Hospital – sequence: 10 givenname: Slim surname: Amine fullname: Amine, Slim organization: Charles Nicolle Hospital – sequence: 11 givenname: Leila surname: Bouabid fullname: Bouabid, Leila organization: Ministry of Health – sequence: 12 givenname: Souha surname: Bougatef fullname: Bougatef, Souha organization: Ministry of Health – sequence: 13 givenname: Mohamed surname: Kouni Chahed fullname: Kouni Chahed, Mohamed organization: Université de Tunis El Manar – sequence: 14 givenname: Afif surname: Ben Salah fullname: Ben Salah, Afif organization: Pasteur Institute of Tunis – sequence: 15 givenname: Jihene surname: Bettaieb fullname: Bettaieb, Jihene organization: Pasteur Institute of Tunis – sequence: 16 givenname: Nissaf orcidid: 0000-0002-3326-9861 surname: Bouafif Ben Alaya fullname: Bouafif Ben Alaya, Nissaf email: nissafba@yahoo.fr organization: Université de Tunis El Manar |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32390333$$D View this record in MEDLINE/PubMed |
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Cites_doi | 10.1186/s12879-016-2087-9 10.2807/1560-7917.ES.2019.24.12.1800042 10.1111/irv.12330 10.1186/1471-2334-7-141 10.1093/ije/dyl162 10.1093/aje/kwx334 10.1016/j.jiph.2019.05.005 10.2807/1560-7917.ES.2017.22.35.30606 10.2471/BLT.18.211508 10.1371/journal.pone.0074064 10.1016/j.ics.2004.02.121 10.1093/pubmed/fdm068 10.1111/j.1750-2659.2012.00422.x 10.26719/2016.22.7.459 10.1371/journal.pone.0077244 10.1111/irv.12544 10.5365/wpsar.2017.8.1.002 |
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Copyright | 2020 The Authors. Published by John Wiley & Sons Ltd. 2020 The Authors. Influenza and Other Respiratory Viruses Published by John Wiley & Sons Ltd. COPYRIGHT 2020 John Wiley & Sons, Inc. 2020. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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Defining the start and assessing the intensity of influenza seasons are essential to ensure timely preventive and control measures and to contribute... Defining the start and assessing the intensity of influenza seasons are essential to ensure timely preventive and control measures and to contribute to the... Background: Defining the start and assessing the intensity of influenza seasons are essential to ensure timely preventive and control measures and to... BackgroundDefining the start and assessing the intensity of influenza seasons are essential to ensure timely preventive and control measures and to contribute... BACKGROUNDDefining the start and assessing the intensity of influenza seasons are essential to ensure timely preventive and control measures and to contribute... |
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SubjectTerms | Analysis Confidence intervals epidemic threshold Epidemics Historical account ILI surveillance Illnesses Influenza Methods moving epidemic method Original Pandemics Prevention Primary care Respiratory diseases Seasons Sensitivity Thresholds Viruses |
Title | Applying the moving epidemic method to determine influenza epidemic and intensity thresholds using influenza‐like illness surveillance data 2009‐2018 in Tunisia |
URI | https://onlinelibrary.wiley.com/doi/abs/10.1111%2Firv.12748 https://www.ncbi.nlm.nih.gov/pubmed/32390333 https://www.proquest.com/docview/2434630406 https://www.proquest.com/docview/2464990664 https://search.proquest.com/docview/2401121182 https://pubmed.ncbi.nlm.nih.gov/PMC7431642 |
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