Article(id=1320442603410142073, tenantId=1146029695717560320, journalId=1319373219989389464, issueId=1320442558581424898, articleNumber=null, orderNo=null, doi=10.11847/zgggws1148334, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1750867200000, receivedDateStr=2025-06-26, revisedDate=1756137600000, revisedDateStr=2025-08-26, acceptedDate=1762704000000, acceptedDateStr=2025-11-10, onlineDate=1792747649289, onlineDateStr=2026-10-23, pubDate=1770652800000, pubDateStr=2026-02-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1792747649289, onlineIssueDateStr=2026-10-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1792747649289, creator=13701087609, updateTime=1792747649289, updator=13701087609, issue=Issue{id=1320442558581424898, tenantId=1146029695717560320, journalId=1319373219989389464, year='2026', volume='42', issue='2', pageStart='129', pageEnd='256', issueExtLink='null', onlineDate='null', pubDate='1770652800000', pubDateStr='2026-02-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1792747638602, creator='13701087609', updateTime=1792748118043, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1320444569574031427, tenantId=1146029695717560320, journalId=1319373219989389464, issueId=1320442558581424898, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1320444569578225732, tenantId=1146029695717560320, journalId=1319373219989389464, issueId=1320442558581424898, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=245, endPage=249, ext={EN=ArticleExt(id=1320442603611468666, articleId=1320442603410142073, tenantId=1146029695717560320, journalId=1319373219989389464, language=EN, title=Research advances in the methods for tracing the origins of dengue fever outbreaks:a review, columnId=1320427713148597230, journalTitle=Chinese Journal of Public Health, columnName=Review, runingTitle=null, highlight=null, articleAbstract=

This study explores and summarizes existing analytical methods for tracing the origins of dengue fever, including the techniques based on epidemiology, genomics, molecular phylogeny of the dengue virus, and Geographic Information Systems (GIS). These approaches reveal the transmission routes, mutations, and host adaptation of the dengue virus from different perspectives. This paper introduces the applicability of these methods across different geographical regions and temporal scales and analyzes the advantages and disadvantages of these methods in practical applications to provide insights for the development of novel tracing technologies in the future. The aim is to enhance the understanding about the transmission mechanisms of dengue fever and offer technical support for the tracing, prediction, and control of dengue fever outbreaks.

, authors=Hao WU1, Jiaxuan BIAN2, Shaofu DU2, Huiqun JIA2, Hui WANG2, Ligui WANG2, Xiaoying LI2, Hongbin SONG2, authorsList=Hao WU, Jiaxuan BIAN, Shaofu DU, Huiqun JIA, Hui WANG, Ligui WANG, Xiaoying LI, Hongbin SONG, authorCompany=null, correspAuthors=Xiaoying LI, Hongbin SONG, authorNote=null, correspAuthorsNote=
LI Xiaoying, E-mail:
SONG Hongbin, E-mail:
, copyrightStatement=Copyright © 2026 Chinese Journal of Public Health. All rights reserved., copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1320442603875709819, articleId=1320442603410142073, tenantId=1146029695717560320, journalId=1319373219989389464, language=CN, title=登革热追踪溯源分析技术研究进展, columnId=1320427713358312432, journalTitle=中国公共卫生, columnName=综 述, runingTitle=null, highlight=null, articleAbstract=

本综述探讨和总结了现有的登革热追踪溯源分析方法,包括流行病学、基因组学、登革病毒的分子进化与传播溯源和地理信息系统(GIS)技术等。这些方法从不同角度揭示了登革热病毒的传播路径、病毒变异和宿主适应性。本文介绍了这些方法在不同地理区域和时间尺度上的适用性,进一步分析这些方法在实际应用中的优缺点,为未来溯源新技术的研发提供思路和借鉴,提高对登革热传播机制的理解,为登革热疫情的溯源、预测和控制提供技术支撑。

, authors=吴昊1, 卞家轩2, 杜绍福2, 贾慧群2, 王辉2, 王立贵2, 李笑樱2, *, *, 宋宏彬2, *, *, authorsList=吴昊, 卞家轩, 杜绍福, 贾慧群, 王辉, 王立贵, 李笑樱, 宋宏彬, authorCompany=null, correspAuthors=李笑樱, 宋宏彬, authorNote=

吴昊(2000—),硕士在读,研究方向:传染病流行病学

, correspAuthorsNote=
李笑樱,Email:
宋宏彬,Email:
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吴昊 1 , 卞家轩 2 , 杜绍福 2 , 贾慧群 2 , 王辉 2 , 王立贵 2 , 李笑樱 2, *, * , 宋宏彬 2, *, *
中国公共卫生 | 综 述 2026,42(2): 245-249
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中国公共卫生 |综 述 2026 , 42 (2) : 245 -249
登革热追踪溯源分析技术研究进展
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吴昊1, 卞家轩2, 杜绍福2, 贾慧群2, 王辉2, 王立贵2, 李笑樱2, *, * , 宋宏彬2, *, *
作者信息
  • 1郑州大学公共卫生学院,郑州 450001
  • 2中国人民解放军疾病预防控制中心传染病防控科,北京 100071
通讯作者:
李笑樱,Email:
宋宏彬,Email:
作者简介:

吴昊(2000—),硕士在读,研究方向:传染病流行病学

Research advances in the methods for tracing the origins of dengue fever outbreaks:a review
Hao WU1, Jiaxuan BIAN2, Shaofu DU2, Huiqun JIA2, Hui WANG2, Ligui WANG2, Xiaoying LI2 , Hongbin SONG2
Affiliations
  • 1College of Public Health, Zhengzhou University, Zhengzhou 450001, China
  • 2Department of Infectious Disease Control and Prevention, Chinese People's Liberation Army Center for Disease Control and Prevention, Beijing 100071, China
  • Corresponding Author:
    LI Xiaoying, E-mail:
    SONG Hongbin, E-mail:
出版时间: 2026-02-10 doi: 10.11847/zgggws1148334
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本综述探讨和总结了现有的登革热追踪溯源分析方法,包括流行病学、基因组学、登革病毒的分子进化与传播溯源和地理信息系统(GIS)技术等。这些方法从不同角度揭示了登革热病毒的传播路径、病毒变异和宿主适应性。本文介绍了这些方法在不同地理区域和时间尺度上的适用性,进一步分析这些方法在实际应用中的优缺点,为未来溯源新技术的研发提供思路和借鉴,提高对登革热传播机制的理解,为登革热疫情的溯源、预测和控制提供技术支撑。

登革热  /  追踪溯源  /  监测  /  分析技术

This study explores and summarizes existing analytical methods for tracing the origins of dengue fever, including the techniques based on epidemiology, genomics, molecular phylogeny of the dengue virus, and Geographic Information Systems (GIS). These approaches reveal the transmission routes, mutations, and host adaptation of the dengue virus from different perspectives. This paper introduces the applicability of these methods across different geographical regions and temporal scales and analyzes the advantages and disadvantages of these methods in practical applications to provide insights for the development of novel tracing technologies in the future. The aim is to enhance the understanding about the transmission mechanisms of dengue fever and offer technical support for the tracing, prediction, and control of dengue fever outbreaks.

dengue fever  /  tracing the origin  /  surveillance  /  analytical method
吴昊, 卞家轩, 杜绍福, 贾慧群, 王辉, 王立贵, 李笑樱, 宋宏彬. 登革热追踪溯源分析技术研究进展. 中国公共卫生, 2026 , 42 (2) : 245 -249 . DOI: 10.11847/zgggws1148334
Hao WU, Jiaxuan BIAN, Shaofu DU, Huiqun JIA, Hui WANG, Ligui WANG, Xiaoying LI, Hongbin SONG. Research advances in the methods for tracing the origins of dengue fever outbreaks:a review[J]. Chinese Journal of Public Health, 2026 , 42 (2) : 245 -249 . DOI: 10.11847/zgggws1148334
登革热是由登革病毒(Dengue virus, DENV)引发的蚊媒传染病,主要流行于热带和亚热带地区。据世界卫生组织(WHO)数据,登革热的全球发病率从2000年约50.5万例增至2019年的420万例[1–2]。登革病毒(DENV)属于黄病毒科,存在4种抗原差异显著的血清型(DENV-1~DENV-4)[3]。初次感染某一血清型仅能获得对该型的终身免疫,对其他血清型的交叉保护作用有限。二次感染不同血清型时,可能因抗体依赖性增强作用(ADE)导致病毒更易侵入宿主细胞,显著增加重症风险[4]。近年来,伊蚊媒介的扩散及非传统流行区登革热发病率的增长成为公共卫生新的关注重点。相较于1779年首次在雅加达报道时[5],登革热的地理分布已扩展至更多温带气候地区,如南欧部分地区、美国某些区域及尼泊尔的高海拔地带等[6]。这种趋势被认为是由气候变化、城市化以及人类活动等因素驱动[7–8]。此外,有研究发现,过去部分地区由于临床怀疑不足和诊断测试缺乏,可能低估了登革热的流行情况[6]。登革病毒主要通过埃及伊蚊(Aedes aegypti)和白纹伊蚊(Aedes albopictus)的“人—蚊—人”循环传播。埃及伊蚊起源于非洲,随人类活动扩散至全球热带地区,偏好与人类共生的环境;白纹伊蚊起源于东南亚,凭借耐寒能力已入侵温带地区。这两种伊蚊主要在白天活动,尤其是在清晨和黄昏时分最为活跃,这增加了人们暴露于叮咬的风险,并给传统的病媒控制手段带来了挑战[9–10]。登革热的主要传播途径包括伊蚊叮咬、母婴传播、输血传播和器官移植传播[6–11]。通过追踪溯源研究能够尽早确定登革病毒的起源和传播途径,对有效制定登革热防控措施至关重要。本文就登革热的追踪溯源研究方法和优缺点及其面临的挑战与发展方向作一综述。
流行病学是一门研究疾病和健康状况在人群中的分布及其影响因素的科学,旨在通过系统的数据收集与分析揭示疾病的动态过程,并为预防策略提供科学依据。在登革热追踪溯源的研究中,流行病学调查方法起着至关重要的作用。这些方法不仅揭示了病毒传播的时间、地点及人群特征,还帮助识别出潜在的风险因素,从而为制定精准防控措施提供了坚实的科学基础[12]。
观察性流行病学方法是一类通过自然观察(而非人为干预)来研究疾病分布、影响因素及健康相关事件的研究方法。其通过横断面研究、回顾性分析及现场流行病学调查等方法构建多维溯源证据网络。如在中国贵州省道真县输入性疫情的现场调查研究中,通过追踪接触史、伊蚊密度监测等,成功溯源发现此次疫情为外出务工人员在马尔代夫工地食堂就餐时被蚊虫叮咬而感染,成功还原了“输入病例—本地蚊媒—续发感染”的完整传播链[13]。在一项2022年尼泊尔登革热疫情的研究中,研究人员采用了横断面研究设计,整合了分子流行病学方法,通过对538例疑似登革热患者进行系统的检测,确认了DENV-1、DENV-2和DENV-3在当地共同循环的流行格局[14]。此外,一项针对2014—2017年马来西亚登革热病例的回顾性研究设计,结合分子流行病学分析方法,确定了DENV-1在此期间为主要流行的血清型和基因型。这项研究通过分析不同血清型和基因型与临床表现之间的关联,为理解当地登革热的流行特征提供了宝贵信息[15]。
随着流行病学的发展和学科交叉融合的深化,新兴的流行病学分支也促进登革热溯源工作的开展,如空间流行病学、分子流行病学等。如一项中国广东省的登革热研究,通过整合Richards模型和空间流行病学方法,明确了2014年广东省登革热疫情暴发的地理起源及传播路径,为基于时空特征的病毒扩散溯源提供了方法学范例[16]。而在中国海南省的一项研究中采用分子流行病学技术和现场流行病学调查,明确了2019年海南省登革热疫情由广东省输入的DENV-1毒株引发,并揭示了登革热的跨区域传播的多源性特征[17]。
流行病学方法在登革热溯源追踪中起着重要作用,通过综合运用流行病学方法,可以为登革热溯源追踪提供科学依据,指导制定有效的防控策略,最终实现控制疫情、降低疾病负担的目标。
在DENV溯源研究中,传统聚合酶链式反应(polymerase chain reaction, PCR)和实时定量聚合酶链式反应(quantitative real-time PCR, qPCR)都发挥着不可或缺的作用。传统PCR技术通过特异性引物介导的靶基因序列扩增,为病毒核酸的定性检测提供基础支撑,其技术经济性优势使其在资源有限地区仍具有重要应用价值[18]。例如,在中国云南省登革病毒血清型和基因型的溯源研究中,通过传统PCR扩增DENV包膜蛋白基因序列,进行核苷酸和推导氨基酸序列同源性分析及系统进化分析,以阐明云南省DENV的血清型、基因型分布特征及其传播来源[19]。
相比之下,qPCR技术能够实时监测扩增过程中的荧光信号变化,提供高灵敏度和特异性的定量分析。它不仅适用于病毒载量测定、基因型分型和快速疫情响应,而且能够在早期检测到低浓度的病毒核酸,从而提高诊断速度和准确性。尽管qPCR设备和技术要求较高、成本也相对较昂贵,但其高效性和精确性为登革热病毒的溯源研究提供了重要支持,其已成为分子生物学研究中的关键工具[18–20]。如2014—2015年在中国广东省肇庆市登革病毒分子分型及初步溯源研究中,通过qPCR检测阳性标本进行病毒培养,分离株进行E基因扩增与序列测定,进行分子分型和系统进化树构建,以了解可能的输入来源[21]。由于登革热病毒具有4种不同的血清型和众多的基因型,已有流行病学研究表明,每间隔一段时间,新的登革热血清或者基因分型的出现往往导致重大疫情的暴发[22]。因此,需要进行持续的检测溯源来监测登革热的传播,进而制定合适的防控措施,在此过程中聚合酶链反应方法发挥着重要作用,其可以及时地判断登革热的血清型和基因型,并且和其他方法结合可以追溯登革热疫情暴发的传播路径,进而制定防控措施。聚合酶链反应的应用发展正朝着高通量、自动化方向演进,如开发单重实时RT-PCR检测方法可实现登革病毒的高灵敏度检测和精准定量[23],或通过改进引物设计和优化反应条件来增强检测灵敏度和特异性[24]。这些技术革新不仅加速了病毒变异株的识别效率,更为跨境传播链的精细解析提供了分子证据链支持。
Sanger链终止法(一代测序)作为基因组学奠基性技术,通过双脱氧核苷酸介导的链终止原理实现了DNA序列的精确解析[25]。在登革热溯源研究中,其高保真性使其成为病毒血清型鉴定和基因分型的金标准[26],Santiago G A等[27]在2019年利用Sanger测序开发一种标准化方法,成功从临床样本中直接测序并基因分型了4种DENV血清型。此外,传统监测系统至今仍依赖Sanger共识测序,
自首个高通量测序平台商业化以来,第二代测序(Next-generation sequencing, NGS)凭借高通量和快速检测能力,彻底改变了DENV分子流行病学研究范式,研究者通过对大规模病毒基因组数据的比较分析,能够追踪病毒传播路径、量化变异速率并重建系统发育关系[28]。近年来,Ko H Y等[26]和Copin R等[29]通过应用NGS技术,分析了登革热病毒的遗传变异,并探讨了这些变异如何影响病毒的传播和疾病的严重性。这些研究均强调了NGS技术在监测病毒适应性进化、传播潜力以及对临床结果严重性影响的重要性。
第三代测序技术的概念于21世纪初提出,其标志性的单分子实时(SMRT)测序和纳米孔测序技术均在2010年实现了商业化。能够直接测定单个DNA分子并具有更长的读长。这种技术使复杂基因组组装和结构变异检测更加高效准确,在登革热病毒研究中提供了关于病毒基因组结构、变异及其与宿主相互作用的深入见解。2020年,学者Adikari T N等[30]利用纳米孔测序成功对DENV近全长基因组进行了测序,不仅提高了测序覆盖度和准确性,还减少了PCR扩增过程中的偏差,有助于区分宿主内的病毒变体,理解病毒进化、传播模式及疾病严重程度的变异。
基因组测序为登革热溯源研究提供了一种强有力的工具,其高通量和高灵敏度的特点为理解病毒的传播和变异提供了新的视角。尽管该技术在成本和数据处理上存在一定的挑战,但其潜在的公共卫生应用价值不可忽视。科研人员通过将基因组测序与流行病学、临床医学等学科更好结合,能够更有效地对登革热等流行病进行溯源,从而更好地应对登革热等相关传染病的威胁。
通过对病毒基因组的分析,可以揭示病毒的进化历史和传播模式。系统进化分析是基于病毒的生物学特征推断同一谱系不同株病毒之间的进化关系,通过构建系统发育树来了解病毒的进化、演变与迁移。近年来,由于DENV基因组相对较小、高通量测序技术的进步、公共DENV基因库(例如GenBank)的迅速增长等,系统发育分析在登革热溯源追踪中变得愈发重要[31]。根据溯源研究,登革热至少有4种病毒血清型,且不同血清型病毒的氨基酸序列存在较大差异,一致性约为60%~70%。而相同的血清型病毒的氨基酸也有约3%的差异,核苷酸水平有6%的差异,这种差异导致产生不同的基因型和进化分支。血清型与进化分支之间的遗传变异决定不同病毒的适应性、毒力及流行潜能[32]。
系统发育分析是研究病毒进化关系的重要方法,主要通过比较不同病毒株的遗传序列,构建系统发育树,揭示病毒的进化历史和传播路径。构建系统进化树的方法主要包括最大似然法(ML)、贝叶斯推断(BI)、邻接法(NJ)和最大简约法(MP),这些方法通过不同的算法和模型,从遗传序列数据中提取病毒株之间的进化关系,为推断病毒溯源和系统发育提供科学依据[33–34]。
ML方法基于概率模型,具有高效性和较高的分辨率,并且可以为后续研究提供参考,验证结果一致性。在一项巴西登革热溯源研究中,通过ML和BI方法分析2011—2022年的177个DENV基因组序列,揭示了DENV-1和DENV-2血清型的共循环、多样的病毒谱系及追溯到了登革热的国际输入事件[35]。BI法整合先验信息,具有较高的灵活力和较强的解释力,并且其带有时间信息。在东非登革热的一项溯源研究中,通过BI方法进行时间动态与地理扩散分析,重建了登革热从东亚及东南亚到东非的传播路径[36]。NJ方法可以进行快速的筛查,并且辅助验证复杂模型,两项用于筛查印度和尼泊尔登革热流行病毒分型的研究均采用了NJ方法,印度研究发现长期流行的 DENV-2 基因型 IV主导传播,并发现罕见的 DENV-3 基因型 I 输入[22];而尼泊尔研究则首次证实2022年高海拔地区暴发由 DENV-1 和 DENV-3 新型共循环引发,凸显病毒适应性进化对公共卫生的威胁[14]。MP方法具有简约性,可以帮助识别关键突变位点,验证其他方法结果。在一项巴西的登革热溯源的研究中,通过MP方法,重建病毒株的系统发育关系,推断DENV-1病毒来自巴西北部地区及本地DENV-3的分支分化[37]。
系统发育分析在登革热溯源方面发挥着关键作用,其通过研究登革热病毒(DENV)的基因型和血清型的演化关系,帮助研究者追踪病毒的传播路径和演化历史。这种方法不仅有助于理解病毒的流行病学特征,还能揭示病毒在不同地区间的传播动态。例如,DENV-2和DENV-3的进化速度较快,而DENV-1和DENV-4的进行建设相对较慢。这些差异可能与病毒的适应性进化有关,具体机制仍在研究中[38]。
在登革热追踪溯源中,GIS技术虽不直接采集地理信息数据,但作为核心分析工具,可以通过整合气象、人口、病例等多维数据,构建时空关联模型,揭示传播路径与驱动因素,构建基于地理信息的溯源分析框架。登革热的传播模式受到多种因素的影响,包括气候、环境、社会经济条件和地理特征,理解这些地理信息,可以帮助理解登革热的传播模式,进而更好地进行溯源工作。中国北京师范大学田怀玉教授团队发现热带印度洋海表温度模态指数(Indian Ocean basin-wide,IOBW)会导致局部温度异常,进而影响登革热传播[39]。
GIS与多个学科交叉应用推动溯源方法革新,进而推动对登革热的防控和溯源。如在墨西哥实施的全国性网络地理信息系统,集成了地理参考的流行病学、昆虫学和控制干预措施的数据,实现传播风险的空间可视化,支持短时间内快速锁定登革热的输入性病例来源[40]。在哥伦比亚的研究中,通过空间分析方法探讨了登革热在城市中的分布情况。精准识别疫情时空聚集性,为溯源提供关键靶点[32]。在巴拿马,通过将13年的登革热病例监测数据与病媒监测数据相结合,研究人员使用空间时间建模方法来估计感染风险,将病媒监测数据作为协变量纳入模型,改变检测到的登革热病例聚类的位置、持续时间和相对风险,为登革热的溯源提供了便利[41]。基于网络的GIS应用极大地提升了登革热感染数据的整合与分析效率,增强了国家登革热监测和溯源的能力。这种应用有助于快速识别疫情来源和传播路径,对于登革热的追踪溯源具有重要价值。因此,GIS技术的应用不仅提高了登革热监测工作的有效性,也为制定精准防控策略提供了科学依据。
溯源分析对登革热研究至关重要,但现有技术和方法仍面临挑战。尽管基因组测序技术不断进步,样本质量、成本和数据分析仍是难题;同时,实时监测系统和数据共享的不足,限制了疫情快速响应的能力,及时共享病毒基因组数据对于追踪传播路径和评估疫苗效力非常重要。流行病学调查提供了病例和接触者追踪的详细信息,但在资源有限地区实施困难,偏远地区的卫生系统可能无法支持复杂的采样流程或缺乏迅速报告病例的通讯工具。此外,GIS和系统发育分析为疫情溯源提供有力工具,但应用这些技术需要专业知识,且对数据质量和完整性要求高,任何数据错误都可能导致误导性结论,影响防控策略的有效性。
在登革热溯源研究中新技术研发科技扮演着核心角色,随着新兴技术的发展,人工智能(AI)、机器学习等技术在病毒溯源分析中的应用前景广阔,其应用不仅显著提升了新病毒发现和血清分型鉴定的效率,更为解析病毒进化关系和绘制高分辨率传播路径奠定了基础。
一项丹麦哥本哈根大学和英国牛津大学的联合研究证明,基于人工智能加速识别病毒血清型和分析多源数据,精准定位传播源头[42]。Sebastianelli A等[43]通过整合卫星遥感、气候模型、社会经济等多维数据揭示传播链驱动因素,通过数据交叉验证锁定疫情暴发根源。Hill V等[44]发明新系统,通过整合高通量测序、自动化分类算法和标准化命名规则,实现了登革热病毒溯源的"高分辨率、跨平台可比性、低数据需求"三重突破,为全球疫情的溯源提供分子技术支撑。在此技术的支持下,研究者发现新谱系DENV-3Ⅲ_B.3.2,且其进化根源指向亚洲,证实其为跨洲输入。
登革热的全球传播特性及其造成的重大健康、社会和经济负担,使得跨国界的有效溯源研究必然依赖于强有力的全球合作。全球合作是登革热溯源的基石。在全球层面,GenBank和GISAID等公共数据库收录了大量DENV基因组序列,为溯源研究提供了丰富资源,直接支撑了登革热的跨国传播链的重建。世界卫生组织于2014年发起成立了登革热控制伙伴关系(Partnership for Dengue Control)和启动《全球虫媒病毒倡议》(2022—2025年),这些政策框架为全球登革热的溯源工作的合作提供了支持[1–45]。在国家层面,各个国家应积极响应WHO的号召,积极参与溯源工作并贡献数据到国际数据库中,以推动登革热的溯源工作和政府之间的积极合作。有效的国际合作不仅能够有效地促进溯源,也为未来的突发公共卫生事件准备了更加坚实的防线[46–47]。
登革热作为一种全球性公共卫生问题,其追踪溯源分析对于理解病毒的传播路径、预测疫情趋势和制定防控策略至关重要。通过综合运用流行病学调查、分子生物学方法、GIS技术和系统发育分析等方法,及对病毒基因组、宿主和媒介蚊虫的进化关系以及地区传播模式的研究,可以更好地理解登革热的传播机制。未来的研究应继续增强基因组分析技术,深入研究宿主和媒介蚊虫的进化关系,综合考虑环境和社会经济因素以应对登革热的全球挑战。未来的研究也应聚焦于人工智能和机器学习等新技术与方法的开发,以提高溯源分析的准确性和效率。同时,加强全球合作与数据共享,提升公共卫生基础设施的建设和社区参与水平,对有效应对登革热挑战也至关重要。

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2026年第42卷第2期
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doi: 10.11847/zgggws1148334
  • 接收时间:2025-06-26
  • 首发时间:2026-10-23
  • 出版时间:2026-02-10
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  • 收稿日期:2025-06-26
  • 修回日期:2025-08-26
  • 录用日期:2025-11-10
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    1郑州大学公共卫生学院,郑州 450001
    2中国人民解放军疾病预防控制中心传染病防控科,北京 100071

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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
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红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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