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Seesaw Effect Between COVID-19 and Influenza From 2020 to 2023 in World Health Organization Regions: Correlation Analysis

BACKGROUND: Seasonal influenza activity showed a sharp decline in activity at the beginning of the emergence of COVID-19. Whether there is an epidemiological correlation between the dynamic of these 2 respiratory infectious diseases and their future trends needs to be explored. OBJECTIVE: We aimed t...

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Autores principales: Wang, Qing, Jia, Mengmeng, Jiang, Mingyue, Liu, Wei, Yang, Jin, Dai, Peixi, Sun, Yanxia, Qian, Jie, Yang, Weizhong, Feng, Luzhao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10263104/
https://www.ncbi.nlm.nih.gov/pubmed/37191650
http://dx.doi.org/10.2196/44970
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author Wang, Qing
Jia, Mengmeng
Jiang, Mingyue
Liu, Wei
Yang, Jin
Dai, Peixi
Sun, Yanxia
Qian, Jie
Yang, Weizhong
Feng, Luzhao
author_facet Wang, Qing
Jia, Mengmeng
Jiang, Mingyue
Liu, Wei
Yang, Jin
Dai, Peixi
Sun, Yanxia
Qian, Jie
Yang, Weizhong
Feng, Luzhao
author_sort Wang, Qing
collection PubMed
description BACKGROUND: Seasonal influenza activity showed a sharp decline in activity at the beginning of the emergence of COVID-19. Whether there is an epidemiological correlation between the dynamic of these 2 respiratory infectious diseases and their future trends needs to be explored. OBJECTIVE: We aimed to assess the correlation between COVID-19 and influenza activity and estimate later epidemiological trends. METHODS: We retrospectively described the dynamics of COVID-19 and influenza in 6 World Health Organization (WHO) regions from January 2020 to March 2023 and used the long short-term memory machine learning model to learn potential patterns in previously observed activity and predict trends for the following 16 weeks. Finally, we used Spearman correlation coefficients to assess the past and future epidemiological correlation between these 2 respiratory infectious diseases. RESULTS: With the emergence of the original strain of SARS-CoV-2 and other variants, influenza activity stayed below 10% for more than 1 year in the 6 WHO regions. Subsequently, it gradually rose as Delta activity dropped, but still peaked below Delta. During the Omicron pandemic and the following period, the activity of each disease increased as the other decreased, alternating in dominance more than once, with each alternation lasting for 3 to 4 months. Correlation analysis showed that COVID-19 and influenza activity presented a predominantly negative correlation, with coefficients above –0.3 in WHO regions, especially during the Omicron pandemic and the following estimated period. The diseases had a transient positive correlation in the European region of the WHO and the Western Pacific region of the WHO when multiple dominant strains created a mixed pandemic. CONCLUSIONS: Influenza activity and past seasonal epidemiological patterns were shaken by the COVID-19 pandemic. The activity of these diseases was moderately or greater than moderately inversely correlated, and they suppressed and competed with each other, showing a seesaw effect. In the postpandemic era, this seesaw trend may be more prominent, suggesting the possibility of using one disease as an early warning signal for the other when making future estimates and conducting optimized annual vaccine campaigns.
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spelling pubmed-102631042023-06-15 Seesaw Effect Between COVID-19 and Influenza From 2020 to 2023 in World Health Organization Regions: Correlation Analysis Wang, Qing Jia, Mengmeng Jiang, Mingyue Liu, Wei Yang, Jin Dai, Peixi Sun, Yanxia Qian, Jie Yang, Weizhong Feng, Luzhao JMIR Public Health Surveill Original Paper BACKGROUND: Seasonal influenza activity showed a sharp decline in activity at the beginning of the emergence of COVID-19. Whether there is an epidemiological correlation between the dynamic of these 2 respiratory infectious diseases and their future trends needs to be explored. OBJECTIVE: We aimed to assess the correlation between COVID-19 and influenza activity and estimate later epidemiological trends. METHODS: We retrospectively described the dynamics of COVID-19 and influenza in 6 World Health Organization (WHO) regions from January 2020 to March 2023 and used the long short-term memory machine learning model to learn potential patterns in previously observed activity and predict trends for the following 16 weeks. Finally, we used Spearman correlation coefficients to assess the past and future epidemiological correlation between these 2 respiratory infectious diseases. RESULTS: With the emergence of the original strain of SARS-CoV-2 and other variants, influenza activity stayed below 10% for more than 1 year in the 6 WHO regions. Subsequently, it gradually rose as Delta activity dropped, but still peaked below Delta. During the Omicron pandemic and the following period, the activity of each disease increased as the other decreased, alternating in dominance more than once, with each alternation lasting for 3 to 4 months. Correlation analysis showed that COVID-19 and influenza activity presented a predominantly negative correlation, with coefficients above –0.3 in WHO regions, especially during the Omicron pandemic and the following estimated period. The diseases had a transient positive correlation in the European region of the WHO and the Western Pacific region of the WHO when multiple dominant strains created a mixed pandemic. CONCLUSIONS: Influenza activity and past seasonal epidemiological patterns were shaken by the COVID-19 pandemic. The activity of these diseases was moderately or greater than moderately inversely correlated, and they suppressed and competed with each other, showing a seesaw effect. In the postpandemic era, this seesaw trend may be more prominent, suggesting the possibility of using one disease as an early warning signal for the other when making future estimates and conducting optimized annual vaccine campaigns. JMIR Publications 2023-06-12 /pmc/articles/PMC10263104/ /pubmed/37191650 http://dx.doi.org/10.2196/44970 Text en ©Qing Wang, Mengmeng Jia, Mingyue Jiang, Wei Liu, Jin Yang, Peixi Dai, Yanxia Sun, Jie Qian, Weizhong Yang, Luzhao Feng. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 12.06.2023. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Public Health and Surveillance, is properly cited. The complete bibliographic information, a link to the original publication on https://publichealth.jmir.org, as well as this copyright and license information must be included.
spellingShingle Original Paper
Wang, Qing
Jia, Mengmeng
Jiang, Mingyue
Liu, Wei
Yang, Jin
Dai, Peixi
Sun, Yanxia
Qian, Jie
Yang, Weizhong
Feng, Luzhao
Seesaw Effect Between COVID-19 and Influenza From 2020 to 2023 in World Health Organization Regions: Correlation Analysis
title Seesaw Effect Between COVID-19 and Influenza From 2020 to 2023 in World Health Organization Regions: Correlation Analysis
title_full Seesaw Effect Between COVID-19 and Influenza From 2020 to 2023 in World Health Organization Regions: Correlation Analysis
title_fullStr Seesaw Effect Between COVID-19 and Influenza From 2020 to 2023 in World Health Organization Regions: Correlation Analysis
title_full_unstemmed Seesaw Effect Between COVID-19 and Influenza From 2020 to 2023 in World Health Organization Regions: Correlation Analysis
title_short Seesaw Effect Between COVID-19 and Influenza From 2020 to 2023 in World Health Organization Regions: Correlation Analysis
title_sort seesaw effect between covid-19 and influenza from 2020 to 2023 in world health organization regions: correlation analysis
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10263104/
https://www.ncbi.nlm.nih.gov/pubmed/37191650
http://dx.doi.org/10.2196/44970
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