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Prediction of upcoming global infection burden of influenza seasons after relaxation of public health and social measures during the COVID-19 pandemic: a modelling study
BACKGROUND: The transmission dynamics of influenza were affected by public health and social measures (PHSMs) implemented globally since early 2020 to mitigate the COVID-19 pandemic. We aimed to assess the effect of COVID-19 PHSMs on the transmissibility of influenza viruses and to predict upcoming...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Ltd
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9573849/ https://www.ncbi.nlm.nih.gov/pubmed/36240828 http://dx.doi.org/10.1016/S2214-109X(22)00358-8 |
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author | Ali, Sheikh Taslim Lau, Yiu Chung Shan, Songwei Ryu, Sukhyun Du, Zhanwei Wang, Lin Xu, Xiao-Ke Chen, Dongxuan Xiong, Jiaming Tae, Jungyeon Tsang, Tim K Wu, Peng Lau, Eric H Y Cowling, Benjamin J |
author_facet | Ali, Sheikh Taslim Lau, Yiu Chung Shan, Songwei Ryu, Sukhyun Du, Zhanwei Wang, Lin Xu, Xiao-Ke Chen, Dongxuan Xiong, Jiaming Tae, Jungyeon Tsang, Tim K Wu, Peng Lau, Eric H Y Cowling, Benjamin J |
author_sort | Ali, Sheikh Taslim |
collection | PubMed |
description | BACKGROUND: The transmission dynamics of influenza were affected by public health and social measures (PHSMs) implemented globally since early 2020 to mitigate the COVID-19 pandemic. We aimed to assess the effect of COVID-19 PHSMs on the transmissibility of influenza viruses and to predict upcoming influenza epidemics. METHODS: For this modelling study, we used surveillance data on influenza virus activity for 11 different locations and countries in 2017–22. We implemented a data-driven mechanistic predictive modelling framework to predict future influenza seasons on the basis of pre-COVID-19 dynamics and the effect of PHSMs during the COVID-19 pandemic. We simulated the potential excess burden of upcoming influenza epidemics in terms of fold rise in peak magnitude and epidemic size compared with pre-COVID-19 levels. We also examined how a proactive influenza vaccination programme could mitigate this effect. FINDINGS: We estimated that COVID-19 PHSMs reduced influenza transmissibility by a maximum of 17·3% (95% CI 13·3–21·4) to 40·6% (35·2–45·9) and attack rate by 5·1% (1·5–7·2) to 24·8% (20·8–27·5) in the 2019–20 influenza season. We estimated a 10–60% increase in the population susceptibility for influenza, which might lead to a maximum of 1–5-fold rise in peak magnitude and 1–4-fold rise in epidemic size for the upcoming 2022–23 influenza season across locations, with a significantly higher fold rise in Singapore and Taiwan. The infection burden could be mitigated by additional proactive one-off influenza vaccination programmes. INTERPRETATION: Our results suggest the potential for substantial increases in infection burden in upcoming influenza seasons across the globe. Strengthening influenza vaccination programmes is the best preventive measure to reduce the effect of influenza virus infections in the community. FUNDING: Health and Medical Research Fund, Hong Kong. |
format | Online Article Text |
id | pubmed-9573849 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-95738492022-10-17 Prediction of upcoming global infection burden of influenza seasons after relaxation of public health and social measures during the COVID-19 pandemic: a modelling study Ali, Sheikh Taslim Lau, Yiu Chung Shan, Songwei Ryu, Sukhyun Du, Zhanwei Wang, Lin Xu, Xiao-Ke Chen, Dongxuan Xiong, Jiaming Tae, Jungyeon Tsang, Tim K Wu, Peng Lau, Eric H Y Cowling, Benjamin J Lancet Glob Health Articles BACKGROUND: The transmission dynamics of influenza were affected by public health and social measures (PHSMs) implemented globally since early 2020 to mitigate the COVID-19 pandemic. We aimed to assess the effect of COVID-19 PHSMs on the transmissibility of influenza viruses and to predict upcoming influenza epidemics. METHODS: For this modelling study, we used surveillance data on influenza virus activity for 11 different locations and countries in 2017–22. We implemented a data-driven mechanistic predictive modelling framework to predict future influenza seasons on the basis of pre-COVID-19 dynamics and the effect of PHSMs during the COVID-19 pandemic. We simulated the potential excess burden of upcoming influenza epidemics in terms of fold rise in peak magnitude and epidemic size compared with pre-COVID-19 levels. We also examined how a proactive influenza vaccination programme could mitigate this effect. FINDINGS: We estimated that COVID-19 PHSMs reduced influenza transmissibility by a maximum of 17·3% (95% CI 13·3–21·4) to 40·6% (35·2–45·9) and attack rate by 5·1% (1·5–7·2) to 24·8% (20·8–27·5) in the 2019–20 influenza season. We estimated a 10–60% increase in the population susceptibility for influenza, which might lead to a maximum of 1–5-fold rise in peak magnitude and 1–4-fold rise in epidemic size for the upcoming 2022–23 influenza season across locations, with a significantly higher fold rise in Singapore and Taiwan. The infection burden could be mitigated by additional proactive one-off influenza vaccination programmes. INTERPRETATION: Our results suggest the potential for substantial increases in infection burden in upcoming influenza seasons across the globe. Strengthening influenza vaccination programmes is the best preventive measure to reduce the effect of influenza virus infections in the community. FUNDING: Health and Medical Research Fund, Hong Kong. Elsevier Ltd 2022-10-11 /pmc/articles/PMC9573849/ /pubmed/36240828 http://dx.doi.org/10.1016/S2214-109X(22)00358-8 Text en © 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Articles Ali, Sheikh Taslim Lau, Yiu Chung Shan, Songwei Ryu, Sukhyun Du, Zhanwei Wang, Lin Xu, Xiao-Ke Chen, Dongxuan Xiong, Jiaming Tae, Jungyeon Tsang, Tim K Wu, Peng Lau, Eric H Y Cowling, Benjamin J Prediction of upcoming global infection burden of influenza seasons after relaxation of public health and social measures during the COVID-19 pandemic: a modelling study |
title | Prediction of upcoming global infection burden of influenza seasons after relaxation of public health and social measures during the COVID-19 pandemic: a modelling study |
title_full | Prediction of upcoming global infection burden of influenza seasons after relaxation of public health and social measures during the COVID-19 pandemic: a modelling study |
title_fullStr | Prediction of upcoming global infection burden of influenza seasons after relaxation of public health and social measures during the COVID-19 pandemic: a modelling study |
title_full_unstemmed | Prediction of upcoming global infection burden of influenza seasons after relaxation of public health and social measures during the COVID-19 pandemic: a modelling study |
title_short | Prediction of upcoming global infection burden of influenza seasons after relaxation of public health and social measures during the COVID-19 pandemic: a modelling study |
title_sort | prediction of upcoming global infection burden of influenza seasons after relaxation of public health and social measures during the covid-19 pandemic: a modelling study |
topic | Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9573849/ https://www.ncbi.nlm.nih.gov/pubmed/36240828 http://dx.doi.org/10.1016/S2214-109X(22)00358-8 |
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