How virus migration and meteorological elements shape the seasonality of influenza a/H3N2: A case study in China

IF 4.5 2区 医学 Q1 INFECTIOUS DISEASES One Health Pub Date : 2025-06-01 Epub Date: 2025-04-14 DOI:10.1016/j.onehlt.2025.101037
Yilan Liao , Tong Zhao , Wei Du , Dayan Wang , Zhibin Peng , Shan Xue , Jianxing Yu , An Zhang , Hongyan Ren , Zhoupeng Ren , Geoge Fu Gao , Jinfeng Wang , Nils Christian Stenseth
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Abstract

Recent global influenza resurgences, escalating to pandemics, emphasize the urgency for effective vaccinations. Despite their efficacy, vaccines offer limited protection against A/H3N2 variants. Thus, elucidating the spatial patterns and underlying drivers of A/H3N2 seasonality is critical for its management. However, the mechanisms governing this seasonality are not fully understood. The study conducted a collaborative and interdisciplinary analysis of influenza A/H3N2 epidemiology in China from 2012 to 2018, utilizing national influenza surveillance data, viral gene sequence data, and meteorological information. We initially examined the spatiotemporal distribution of influenza A/H3N2 across different temperate zones in China. Subsequently, we employed Bayesian “SkyGrid” reconstruction analysis to gain insights into the population dynamics of the influenza A/H3N2 virus within China's temperature zones. Additionally, we utilized generalized additive models (GAM) to assess the influence of meteorological factors on the seasonal prevalence of influenza A/H3N2. Our analysis of China's national influenza data revealed distinct seasonal patterns for A/H3N2: winter epidemics prevailed in temperate zones, while summer and autumn outbreaks occurred in subtropical and tropical areas. The seasonality of influenza A/H3N2 across China's diverse climatic zones is shaped by the interplay of virus migration and meteorological factors. Virus migration introduced new variant populations during seasonal epidemics of influenza A/H3N2 to different temperature zones in China, thereby seeding subsequent seasonal outbreaks. Our findings also indicate that meteorological elements trigger influenza A/H3N2 activity following virus migration. Moreover, the spatial variations in influenza A/H3N2 seasonality in China can be attributed to specific temperature thresholds, approximately 1 °C and 24 °C. These thresholds could serve as potential indicators for A/H3N2 prevalence. This insight is invaluable for tailoring region-specific prevention and control strategies in China and other regions with similar environmental conditions.

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病毒迁移和气象因素如何影响甲型/H3N2 流感的季节性:中国案例研究
最近全球流感卷土重来,并升级为大流行,这突显了有效接种疫苗的紧迫性。尽管疫苗有效,但对A/H3N2变异体的保护有限。因此,阐明A/H3N2季节性的空间格局和潜在驱动因素对其管理至关重要。然而,控制这种季节性的机制尚不完全清楚。本研究利用国家流感监测数据、病毒基因序列数据和气象信息,对2012 - 2018年中国a /H3N2流感流行病学进行了协作和跨学科分析。我们初步研究了A/H3N2流感在中国不同温带地区的时空分布。随后,我们采用贝叶斯“SkyGrid”重建分析来深入了解中国温带甲型流感/H3N2病毒的种群动态。此外,我们还利用广义加性模型(GAM)评估了气象因素对甲型H3N2流感季节性流行的影响。我们对中国国家流感数据的分析显示,A/H3N2具有明显的季节性特征:冬季流行于温带地区,而夏季和秋季流行于亚热带和热带地区。A/H3N2流感在中国不同气候带的季节性是由病毒迁移和气象因素的相互作用形成的。在甲型H3N2流感季节性流行期间,病毒迁移将新的变异种群引入中国不同的温度区,从而播种了随后的季节性疫情。我们的研究结果还表明,气象因素引发了病毒迁移后的A/H3N2流感活动。此外,中国甲型流感/H3N2季节性的空间差异可归因于特定的温度阈值,约为1°C和24°C。这些阈值可作为A/H3N2流行率的潜在指标。这一见解对于在中国和其他具有类似环境条件的地区制定针对特定区域的防控战略具有宝贵价值。
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来源期刊
One Health
One Health Medicine-Infectious Diseases
CiteScore
8.10
自引率
4.00%
发文量
95
审稿时长
18 weeks
期刊介绍: One Health - a Gold Open Access journal. The mission of One Health is to provide a platform for rapid communication of high quality scientific knowledge on inter- and intra-species pathogen transmission, bringing together leading experts in virology, bacteriology, parasitology, mycology, vectors and vector-borne diseases, tropical health, veterinary sciences, pathology, immunology, food safety, mathematical modelling, epidemiology, public health research and emergency preparedness. As a Gold Open Access journal, a fee is payable on acceptance of the paper. Please see the Guide for Authors for more information. Submissions to the following categories are welcome: Virology, Bacteriology, Parasitology, Mycology, Vectors and vector-borne diseases, Co-infections and co-morbidities, Disease spatial surveillance, Modelling, Tropical Health, Discovery, Ecosystem Health, Public Health.
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