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.

Sheikh Taslim Ali, Yiu Chung Lau, Songwei Shan, Sukhyun Ryu, Zhanwei Du, Lin Wang, Xiao-Ke Xu, Dongxuan Chen, Jiaming Xiong, Jungyeon Tae, Tim K Tsang, Peng Wu, Eric H Y Lau, Benjamin J Cowling
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引用次数: 33

Abstract

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.

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COVID-19大流行期间放松公共卫生和社会措施后即将到来的流感季节全球感染负担的预测:一项建模研究
背景:自2020年初以来,全球为缓解COVID-19大流行而实施的公共卫生和社会措施(PHSMs)影响了流感的传播动态。我们的目的是评估COVID-19 phsm对流感病毒传播的影响,并预测即将到来的流感流行。方法:在这项建模研究中,我们使用了2017-22年11个不同地点和国家的流感病毒活动监测数据。我们实施了一个数据驱动的机制预测建模框架,以COVID-19前的动态和COVID-19大流行期间phsm的影响为基础预测未来的流感季节。我们模拟了即将到来的流感流行的潜在超额负担,与covid -19前的水平相比,峰值幅度和流行规模增加了两倍。我们还研究了积极的流感疫苗接种计划如何减轻这种影响。结果:我们估计2019- 2020年流感季节,COVID-19 PHSMs将流感传播率降低了17.3% (95% CI 13.3 - 21.4)至40.6%(35.2 - 45.9),发病率降低了5.1%(1.5 - 7.2)至24.8%(20.8 - 27.5)。我们估计,人口对流感的易感性将增加10-60%,这可能导致在即将到来的2022-23年流感季节,各地的峰值幅度最多增加1-5倍,流行病规模最多增加1-4倍,其中新加坡和台湾的增幅要高得多。通过增加主动的一次性流感疫苗接种计划,可减轻感染负担。解释:我们的研究结果表明,在即将到来的全球流感季节,感染负担可能会大幅增加。加强流感疫苗接种计划是减少流感病毒感染对社区影响的最佳预防措施。资助:香港卫生及医学研究基金。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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