Preparing for the Next Influenza Pandemic: Vaccine Progress, Challenges, and Prospects

Na Zhang¹; Dayan Wang^1,#

1. National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Disease, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.

^# Corresponding author: Dayan Wang, wangdayan@ivdc.chinacdc.cn.

Influenza pandemics arise when novel influenza virus subtypes emerge in populations with little or no pre-existing immunity. The recent expansion of H5N1 virus circulation in mammals — including documented spread in cattle and sporadic human infections — coupled with the emergence of mutations associated with enhanced pandemic potential, underscores the persistent threat of novel influenza strains. Pandemic preparedness critically depends on developing effective vaccines capable of providing broad protection across diverse viral strains. While vaccination remains the most effective strategy for preventing influenza and its complications, pandemic vaccine development faces substantial challenges. These include the rapid mutation rates characteristic of influenza viruses, driven by error-prone RNA replication, broad host range, environmental selection pressures, and frequent genetic recombination. Such factors complicate predictions of which strain will trigger the next pandemic and hinder efforts to create universal vaccines. Recent advances in vaccine production platforms, bioinformatics, and artificial intelligence have accelerated pandemic vaccine development capabilities. Continued research is essential to enhance vaccine technology, expedite production timelines, and broaden vaccine efficacy against the full spectrum of influenza virus strains.

应对下一次流感大流行：疫苗的进展、挑战与前景

张娜¹；王大燕^1,#

1. 传染病溯源预警与智能决策全国重点实验室，病毒病预防控制所，中国疾病预防控制中心，北京，中国。

^# 通信作者: 王大燕：wangdayan@ivdc.chinacdc.cn。

当新的流感病毒亚型在缺乏预先免疫的人群中出现持续人际传播时，便可能引发流感大流行。近期，H5N1病毒在哺乳动物中的传播范围扩大，凸显了新型流感病毒的持续威胁。疫苗接种是预防流感及其并发症的最有效手段，但大流行疫苗的研制仍面临重大挑战，其中之一便是流感病毒的快速变异。该特性主要归因于其易出错的RNA复制机制、广泛的宿主范围、环境选择压力以及频繁的基因重组等多种因素，不仅增加了预测下一次大流行毒株的难度，也阻碍了通用疫苗的研发进程。新型疫苗生产平台、生物信息学与人工智能等领域的进展，为大流行疫苗的研发提供了助力，后续研究需持续推进，以优化疫苗体系、缩短生产周期，并增强疫苗的广谱保护力。

For more information: https://weekly.chinacdc.cn/en/article/doi/10.46234/ccdcw2025.231

Epidemiological Assessment and Optimization of School-Based Influenza Vaccination — Shenzhen City, Guangdong Province, China, 2023–2024

Shuqi Wang^1,2,3,&; Zhigao Chen^4,&; Qi Tan⁵; Zengyang Shao^1,3; Yushuang Chen^1,3; Fang Huang⁴; Yanpeng Cheng⁴; Jianxing Yu⁶; Ting Zhang⁶; Xin Wang⁴; Xiujuan Tang^4,#; Zhen Zhang⁴; Chao Gao^7,#; Zhongjie Li^6,#; Zhanwei Du^8,9

1 WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong Special Administrative Region, China;

2 School of Cybersecurity, Northwestern Polytechnical University, Xi’an City, Shaanxi Province, China;

3 Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China;

4 Shenzhen Center for Disease Control and Prevention, Shenzhen City, Guangdong Province, China;

5 College of Computer and Information Engineering (College of Artificial Intelligence), Nanjing Tech University, Nanjing City, Jiangsu Province, China;

6 School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China;

7 School of Artificial Intelligence, Optics, and Electronics (iOPEN), Northwestern Polytechnical University, Xi’an City, Shaanxi Province, China;

8 School of Public Health and Emergency Management, Southern University of Science and Technology, Shenzhen City, Guangdong Province, China;

9 School of Medicine, Yunnan University, Kunming City, Yunnan Province, China.

^& Joint first authors.

^# Corresponding author: Xiujuan Tang, txj43@126.com; Chao Gao, cgao@nwpu.edu.cn; Zhongjie Li, lizhongjie@sph.pumc.edu.cn.

School-aged children are primary vectors for influenza transmission through their frequent close contact in educational settings and developing immune awareness. Since 2019, the Shenzhen municipal government has implemented annual, free, influenza vaccination programs targeting eligible primary and secondary school students. However, evidence-based optimization strategies specifically tailored to this demographic remain insufficient. This study analyzed weekly influenza-like illness (ILI) surveillance data and laboratory-confirmed positivity rates from Shenzhen during the 2023–2024 season. It developed an age-stratified SEYARHDV compartmental model integrated with the Ensemble Adjustment Kalman Filter (EAKF) algorithm to estimate historical transmission parameters and quantify vaccination impact. The Upper Confidence Bound applied to Trees (UCT) algorithm was used to optimize vaccination schedule and evaluated multiple strategic scenarios comparatively. Compared to a no-vaccination scenario, the current government strategy prevented approximately 1,285,925 [95% confidence interval (CI): 1,240,671–1,331,180] symptomatic infections and 56,956 (95% CI: 55,118–58,793) hospitalizations. Under identical vaccine supply, the optimized strategy recommends vaccinating 30%, 25%, and 5% of school-aged children in November, December, and January, respectively. This optimized approach would avert approximately 1,469,368 (95% CI: 1,392,734–1,546,002) symptomatic infections and 64,442 (95% CI: 61,269–67,615) hospitalizations—representing 14.3% and 13.1% improvements over the government strategy, respectively. Additionally, a generic strategy developed using 2017–2019 data performed well during 2023–2024, demonstrating cross-seasonal adaptability. Concentrating influenza vaccination efforts among school-enrolled children during November and December significantly reduces disease burden and represents a critical strategy for controlling influenza transmission.

中小学生流感疫苗接种的流行病学评估与优化 — 深圳市, 广东省, 中国, 2023–2024年

王淑祺^1,2,3,&, 陈志高^4,&, 谭棋⁵, 邵增洋^1,3, 陈玉爽^1,3, 黄芳⁴, 程雁鹏⁴, 余建兴⁶, 张婷⁶, 王昕⁴, 唐秀娟^4,#, 张振⁴, 高超^7,#, 李中杰^6,#, 杜占玮^8,9

1. 世界卫生组织传染病流行病学及控制合作中心，公共卫生学院，李嘉诚医学院，香港大学，香港特别行政区，中国

2. 网络空间安全学院，西北工业大学，西安市，陕西省，中国

3. 医卫深析大数据实验室，香港科技园，香港特别行政区，中国

4. 深圳市疾病预防控制中心，深圳市，广东省，中国

5. 计算机与信息工程学院（人工智能学院），南京工业大学，南京市，江苏省，中国

6. 群医学与公共卫生学院，中国医学科学院北京协和医学院，北京，中国

7. 光电与智能研究院（iOPEN），西北工业大学，西安市，陕西省，中国

8. 公共卫生及应急管理学院，南方科技大学院，深圳市，广东省，中国

9. 医学院，云南大学，昆明市，云南省，中国。

^& 共同第一作者。

^# 通信作者：唐秀娟，txj43@126.com；高超，cgao@nwpu.edu.cn；李中杰，lizhongjie@sph.pumc.edu.cn。

在校中小学生因其在教育场所中的密集接触和免疫认知尚在发展阶段，成为流感传播的关键载体。自2019年起，深圳市政府每年为适龄中小学生提供免费的流感疫苗集中接种服务。然而，专门针对该人群的实证优化策略仍较有限。本研究分析了深圳市2023–2024年每周流感样病例（ILI）监测数据及实验室阳性率。研究开发了结合集合调整卡尔曼滤波（EAKF）算法的分年龄组的SEYARHDV传播动力学模型，以估计历史传播率参数并量化疫苗接种的影响。研究采用树搜索上限置信区间算法对疫苗接种计划进行优化，并对多种策略场景进行对比评估。与无疫苗接种场景相比，当前政府实施的策略预计可减少约1,285,925 (95% CI: 1,240,671, 1,331,180)例有症状感染和56,956 (95% CI: 55,118, 58,793)例住院。在疫苗供应不变的情况下，优化策略建议在11月至次年1月分别接种30%、25%和5%的中小学生。该优化方案预计可减少约1,469,368 (95% CI: 1,392,734, 1,546,002)例有症状感染和64,442例住院，较政府策略可分别提高14.3%和13.1%。此外，研究还基于2017–2019年数据构建了通用接种策略，在2023–2024年亦表现良好，具有跨年度适应性。将针对在校中小学生的流感疫苗接种工作集中于11月至12月，可显著减轻疾病疾病负担,是遏制流感传播的关键策略。

For more information: https://weekly.chinacdc.cn/en/article/doi/10.46234/ccdcw2025.232

Phylogenetic and Molecular Characteristics of An H3N8 Avian Influenza Virus Detected in Wild Birds — Beijing, China, September 2024

Jiachen Zhao^1,2,3, Lipeng Liu⁴, Lili Li⁴, Dan Wu^1,2,3, Chunna Ma^1,2,3, Yimeng Liu^1,2,3, Weixian Shi^1,2,3, Xiaomin Peng^1,2,3, Shujuan Cui^1,2,3, Daitao Zhang^1,2,3, Guilan Lu^1,2,3,#

1. Beijing Center for Disease Prevention and Control, Beijing, China;

2. Beijing Key Laboratory of Surveillance, Early Warning and Pathogen Research on Emerging Infectious Diseases Beijing Research Center for Respiratory Infectious Diseases, Beijing, China;

3. Beijing Research Center for Respiratory Infectious Diseases, Beijing, China;

4. Fangshan District Center for Disease Control and Prevention, Beijing, China.

^# Corresponding author: Guilan Lu, luguilan@bjcdc.org.

The H3N8 avian influenza virus (AIV) is recognized for its capacity for interspecies transmission and has been detected in multiple mammalian hosts. Between 2022 and 2023, three human infections with H3N8 were documented in China, raising significant concerns about its zoonotic spillover potential. In this study, we characterized an H3N8 isolate from Niukouyu Wetland Park in Beijing Municipality to elucidate the genetic variability and evolutionary dynamics of this AIV subtype. The virus underwent whole-genome sequencing followed by comprehensive molecular and phylogenetic characterization. We identified a genetically reassorted, low-pathogenicity H3N8 AIV, marking the first detection of this subtype in a wild environment in Beijing. Throat swabs from park staff tested negative for influenza viruses. Phylogenetic analyses demonstrated that the viral hemagglutinin and neuraminidase genes originated from Eurasian and North American lineages, respectively. Nucleotide sequence comparisons revealed 97.57%–99.06% similarity between the eight gene segments of this virus and those of reference strains. Multiple internal gene mutations were identified, including PB2-K318R and PB1-F2-N66S, which are associated with enhanced polymerase activity, increased virulence, and improved mammalian adaptation. The molecular characteristics of this H3N8 virus indicate a potential risk for cross-species transmission to humans, emphasizing the critical need to strengthen influenza surveillance networks and expand monitoring efforts targeting H3 subtype AIVs.

一株野鸟源H3N8禽流感病毒的系统发育及分子特征 — 北京，中国，2024年9月

赵佳琛^1,2,3，刘利鹏⁴，李丽丽⁴，吴丹^1,2,3，马春娜^1,2,3，刘医萌^1,2,3，石伟先^1,2,3，彭晓旻^1,2,3，崔淑娟^1,2,3，张代涛^1,2,3，卢桂兰^1,2,3,#

1. 北京市疾病预防控制中心，北京，中国；

2. 北京重大呼吸道传染病研究中心和北京市重点实验，北京，中国；

3. 北京呼吸疾病研究中心，北京，中国；

3. 房山区疾病预防控制中心，北京，中国。

^# 通信作者: 卢桂兰，luguilan@bjcdc.org。

H3N8 禽流感病毒因其具有跨物种传播能力而为人所知，该病毒已在多种哺乳动物中被发现。在 2022–2023 年期间，中国发现了三例人类感染 H3N8 病例，这引发了对其潜在溢出风险的担忧。本研究对北京牛口峪湿地公园采集的 H3N8 样本进行了全基因组测序，并对其分子和系统发育特征进行了分析。对该病毒进行了全基因组测序以及分子和系统发育特征的鉴定。本研究检测出基因重配的低致病性 H3N8 禽流感病毒，这是该亚型在北京市野生环境中的首次发现。值得注意的是，在公园工作人员的咽拭子检测中未发现流感病毒。系统发育分析表明，病毒的血凝素和神经氨酸酶基因分别源自欧亚谱系和北美谱系。BLAST 结果显示，该病毒的 8 个基因片段与其他参考菌株的相应基因片段的相似性为97.57%– 99.06%。发现了若干内部基因突变（例如 PB2-K318R、PB1-F2-N66S 等），这些突变与增强的聚合酶活性、致病性以及对哺乳动物的适应性有关。因此，这种 H3N8 病毒的分子特征表明其存在跨物种传染给人类的潜在风险，这凸显了加强流感监测网络以及扩大针对 H3 亚型禽流感病毒监测工作的必要性。

For more information: https://weekly.chinacdc.cn/en/article/doi/10.46234/ccdcw2025.233

Developing Machine Learning Prediction Model for Daily Influenza Reported Cases Using Multichannel Surveillance Data — A City, Hubei Province, China, 2023–2025

Xinyue Zhang¹, Xinyi Sang¹, Beibei Liu¹, Quanyu Wang¹, Xiuran Zuo², Sheng Wei^1,3,#, Qi Wang^1,#

1. Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan City, Hubei Province, China;

2. Health Information Center of Wuhan, Wuhan City, Hubei Province, China;

3. School of Public Health and Emergency Management, Southern University of Science and Technology, Shenzhen City, Guangdong Province, China.

^# Corresponding authors: Qi Wang, wangqi_tj@hust.edu.cn; Sheng Wei, weis@sustech.edu.cn.

Public health surveillance is crucial for decision-making. Given the significant threat of influenza to public health, developing predictive models using multichannel surveillance systems is imperative. Data were collected from multichannel surveillance systems, including hospitals, search engines, and climatological and air pollutant surveillance systems, in a southern Chinese city from January 2023 to January 2025. Spearman's correlation analysis assessed the relationships between variables and reported influenza cases. Several machine learning models were used to predict trends in reported cases. Correlation analysis showed all four surveillance systems related to influenza, with 27 variables correlated with daily reported cases. The Long Short-Term Memory model, established based on variables with highest lagged correlations (5-day to 7-day lag) through combined surveillance systems, outperformed other models for 5-day forecasts (R²=0.92; mean absolute error=156.92; mean absolute percentage Error=79.95%; root Mean Squared Error=292.33). Data from various surveillance systems effectively track influenza epidemics. The model shows potential for infectious disease surveillance for epidemic preparedness.

利用多源监测数据构建每日流感病例的机器学习预测模型及其应用 — 某市，湖北省，中国，2023–2025年

张馨月¹, 桑昕怡¹, 刘贝贝¹, 王全钰¹, 左秀然², 魏晟^1,3,#, 王齐^1,#

1. 公共卫生学院流行病与卫生统计学系，同济医学院，华中科技大学，武汉市，湖北省；

2. 武汉市卫生健康信息中心（武汉市人口信息监测站），武汉市，湖北省；

3. 南方科技大学公共卫生及应急管理学院，深圳市，广东省。

^# 通信作者：王齐, wangqi_tj@hust.edu.cn; 魏晟, weis@sustech.edu.cn。

公共卫生监测对于决策至关重要。鉴于流感对公共健康的重大威胁，开发基于多渠道监测系统的预测模型迫在眉睫。研究收集了2023年1月至2025年1月中国南方某城市多渠道监测系统（包括医院、搜索引擎以及气象和空气污染监测系统）的数据。采用Spearman秩相关性分析评估多监测变量与流感日报告病例之间的关系，并运用多种机器学习模型预测流感报告病例趋势。相关性分析显示，四个监测系统数据均与流感日报告病例相关，其中27个变量与每日报告病例相关。基于所有监测系统数据中滞后相关性最高的变量（5 - 7天滞后）建立的长短期记忆网络模型，在5天预测中表现优于其他模型（R²=0.92；平均绝对误差 = 156.92；平均绝对百分比误差 = 79.95%；均方根误差 = 292.33）。多渠道监测系统的数据能有效监测流感流行趋势。本研究强调了多渠道监测数据对于提升呼吸道传染病预测准确性的价值，并证明了其在传染病监测中的应用潜力。

For more information: https://weekly.chinacdc.cn/en/article/doi/10.46234/ccdcw2025.234

Epidemiological and Genetic Characterization of Three H9N2 Viruses Causing Human Infections — Changsha City, Hunan Province, China,  April 2025

Chaoyang Huang^1,2,&, Yi Liu^1,&, Zheng Huang³, Shuilian Chen³, Zhifei Zhan^1,2, Qianlai Sun¹, Ruchun Liu³, Liang Cai^1,2,#, Kaiwei Luo^1,#

1. Hunan Provincial Center for Disease Control and Prevention, Hunan Provincial Academy of Preventive Medicine, Changsha City, Hunan Province, China;

2. Hunan Provincial Key Laboratory of Microbial Molecular Biology, Changsha City, Hunan Province, China;

3. Changsha Center for Disease Control and Prevention, Changsha City, Hunan Province, China.

^& Joint first authors.

^# Corresponding authors: Kaiwei Luo, cfk@hncdc.com; Liang Cai, cailiang@hncdc.com.

In April 2025, three suspected human cases of avian influenza were identified in Changsha, China. Laboratory testing confirmed three cases of H9N2 AIV infection. This report summarizes the epidemiological findings from cases and contact investigations, along with genetic characterization of the isolated H9N2 strains. Comprehensive epidemiological assessments were conducted for each confirmed case. Virus isolation and culture were performed using throat swab specimens obtained from the cases. Isolated H9N2 strains were sequenced using next-generation sequencing (NGS). HA and NA gene sequences were analyzed for homology; evolutionary trees were constructed; and key antigenic sites were examined to identify genetic features. All three cases were sporadic. No influenza-like illness was observed among close contacts or live poultry store employees during the 10-day medical monitoring period. Phylogenetic analysis indicated that the HA gene of all three H9N2 strains belonged to the A/Duck/Hong Kong/Y280/97 (Y280-like) clade within the Eurasian lineage. HA gene sequence homology was 99.7–99.8%, and NA gene homology was 98.4–99.8%. The HA protein cleavage site was identified as PSRSSR↓GLF. Several HA protein site mutations were detected — H191N, A198T/V, Q226L, and Q234L — that had been previously associated with increased binding to receptors. HA-232H, 234L, and 236G support a binding preference for the human-type sialic acid-α-2,6-galactose receptor. All three H9N2 avian influenza cases were mild and involved reported exposure to poultry or related environments. Genetic analysis revealed high homology of HA and NA among the isolated viruses. No epidemiological links were identified between cases, and no evidence was found of sustained human-to-human transmission. Continued avian influenza surveillance and public health education are warranted.

3株人H9N2禽流感病毒的流行病学和遗传特征分析 — 长沙市，湖南省，中国，2025年4月

黄超洋^1,2,&, 刘意^1,&, 黄政³, 陈水连³, 湛志飞^1,2, 孙倩莱¹, 刘如春³, 蔡亮^1,2,#, 罗垲炜^1,#

1. 湖南省疾病预防控制中心，湖南省预防医学科学院，长沙市，湖南省，中国；

2. 湖南省微生物分子生物学重点实验室，长沙市，湖南省，中国；

3. 长沙市疾病预防控制中心，长沙市，湖南省，中国。

^& 共同第一作者。

^# 通信作者：罗垲炜, cfk@hncdc.com; 蔡亮, cailiang@hncdc.com。

2025年4月，长沙市连续发现并报告了3例疑似人禽流感病例。实验室检测确证为H9N2亚型禽流感病毒感染，随后进行病例及密接人员的流行病学调查和H9N2禽流感毒株的遗传特征分析。每个病例进行了流行病学调查。病例的咽拭子标本用于病毒分离和培养。随后分离到的H9N2流感毒株进行NGS测序，分析HA和NA基因序列的同源性，构建进化树和分析抗原的关键位点用于遗传特征分析。3例病例为散发病例，密接人员和活禽门店的从业人员在10天医学观察内未出现发热、咳嗽等流感样症状。对测序数据进行分析表明，3株H9N2禽流感病毒株HA属于欧亚分支的A/Duck/HongKong/Y280/97(H9N2) (Y280) 样进化分支；HA基因同源性为99.7%–99.8%，NA基因同源性为98.4%–99.8%；HA蛋白的裂解位点为PSRSSR↓GLF，H191N、A198T/V、Q226L和Q234L等关键的氨基酸位点突变能够增强病毒与受体的结合能力，而232H， 234L和236G表明病毒和人唾液酸‐α2,6‐半乳糖受体结合。3例H9N2禽流感病例为轻症病例，都存在禽类或禽类环境暴露史。分离得到的3株H9N2病毒的HA和NA基因测序同源性接近。3例病例未发现流行病学关联，也未发现H9N2病毒存在持续人传人的能力，但仍需持续做好禽流感监测和公众健康宣教。

For more information: https://weekly.chinacdc.cn/en/article/doi/10.46234/ccdcw2025.235