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Article(id=1311350363136029457, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1311350193946194882, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202512445, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1766592000000, receivedDateStr=2025-12-25, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1790579890282, onlineDateStr=2026-09-28, pubDate=1786291200000, pubDateStr=2026-08-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790579890282, onlineIssueDateStr=2026-09-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790579890282, creator=13701087609, updateTime=1790579890282, updator=13701087609, issue=Issue{id=1311350193946194882, tenantId=1146029695717560320, journalId=1227665162245664772, year='2026', volume='53', issue='15', pageStart='2689', pageEnd='2880', issueExtLink='null', onlineDate='null', pubDate='1786291200000', pubDateStr='2026-08-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1790579849945, creator='13701087609', updateTime=1790588319569, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1311385718195315029, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1311350193946194882, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1311385718195315030, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1311350193946194882, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=2860, endPage=2864, ext={EN=ArticleExt(id=1311350363417047827, articleId=1311350363136029457, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Construction and tiered early warning system for the influenza composite index in Guizhou Province during winter and spring, columnId=1228016573156360233, journalTitle=Modern Preventive Medicine, columnName=Disease Control and Prevention, runingTitle=null, highlight=null, articleAbstract=
Objective

To construct an influenza composite index for Guizhou Province and predict the epidemic threshold and graded thresholds for influenza.

Methods

Winter-spring influenza surveillance data from the 2017/2018 to 2024/2025 influenza seasons in Guizhou Province were selected. The influenza composite index was constructed using three indicators: the influenza-like illness percentage, influenza nucleic acid positivity rate, and influenza-like illness outbreak count. The moving epidemic method (MEM) was applied to predict the epidemic threshold and graded thresholds for the 2025/2026 influenza season in Guizhou Province. The model performance was evaluated using sensitivity, specificity, and the Youden index.

Results

A total of six influenza seasons (2017/2018, 2018/2019, 2019/2020, 2021/2022, 2023/2024, and 2024/2025) were finally included for constructing the MEM model and conducting early warning analysis. The sensitivity, specificity, and Youden index of the model were 0.90, 0.96, and 0.86, respectively. For the 2025/2026 influenza season, the pre-epidemic threshold was 0.73,the epidemic start week was week 45 of 2025, the end week was week 9 of 2026, and the estimated epidemic duration was 17 weeks.

Conclusion

The constructed influenza composite index demonstrates good performance in assessing influenza epidemic levels in Guizhou Province and can provide a scientific basis for influenza surveillance and early warning in the region.

, authors=Xin ZHENG1, Guo-mei LUO2, Ming-yu LEI1, authorsList=Xin ZHENG, Guo-mei LUO, Ming-yu LEI, authorCompany=null, correspAuthors=Ming-yu LEI, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, 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=1311350364474012446, articleId=1311350363136029457, tenantId=1146029695717560320, journalId=1227665162245664772, language=CN, title=贵州省冬春季流感综合指数构建及分级预警研究, columnId=1228016573844226129, journalTitle=现代预防医学, columnName=疾病预防控制, runingTitle=null, highlight=null, articleAbstract=
目的

构建贵州省流感综合指数，预测流感预警阈值及分级阈值。

方法

选取贵州省2017/2018—2024/2025年流感流行季的冬春季流感监测数据，选取流感样病例百分比、流感核酸检测阳性率和流感样病例暴发疫情数三个指标构建流感综合指数，采用移动流行区间法预测贵州省2025/2026流行季流感流行阈值及分级阈值，评价指标为灵敏度、特异度和约登指数。

结果

最终选取了2017/2018、2018/2019、2019/2020、2021/2022、2023/2024、2024/2025年度六个流行季的监测数据并构建移动流行区间法模型进行预警分析，模型的灵敏度、特异度、约登指数分别为0.90、0.96、0.86。2025/2026流行季的流行前阈值为0.73，流行开始周次为2025年第45周，结束周次为2026年第9周，流行持续时间为17周。

结论

构建的流感综合指数评估贵州省流感流行水平效果较好，可为贵州省流感监测预警提供依据。

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郑鑫（1998—），男，硕士，医师，研究方向：疾病预防与控制

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雷明玉，E-mail：
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Influenza and Other Respiratory Viruses, 2020, 14(5):507-514., articleTitle=Applying the moving epidemic method to determine influenza epidemic and intensity thresholds using influenza-like illness surveillance data 2009-2018 in Tunisia, refAbstract=null)], funds=[Fund(id=1311350367162561352, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350363136029457, awardId=gzwkj2021-549, language=CN, fundingSource=贵州省流感流行病学特征及疾病负担研究(gzwkj2021-549), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1311350364662756127, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350363136029457, xref=1., ext=[AuthorCompanyExt(id=1311350364675339040, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350363136029457, companyId=1311350364662756127, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=Institute for Infectious Disease Prevention and Control, Guizhou Provincial Center for Disease Control and Prevention, Guiyang, Guizhou 550004, China), AuthorCompanyExt(id=1311350364687921953, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350363136029457, companyId=1311350364662756127, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1.贵州省疾病预防控制中心传染病防治研究所，贵州 贵阳 550004)]), AuthorCompany(id=1311350364771808034, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350363136029457, xref=2., ext=[AuthorCompanyExt(id=1311350364780196643, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350363136029457, companyId=1311350364771808034, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2.贵州医科大学公共卫生与健康学院)])], figs=[ArticleFig(id=1311350366244008765, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350363136029457, language=EN, label=Figure 1, caption=Nucleic acid positivity rate and subtype composition of influenza viruses during the 2017—2025 influenza seasons, figureFileSmall=TJ+k1loDmXIPxkQhY4iK1w==, figureFileBig=jhsSsWwu6MOgV2/hJOX4bw==, tableContent=null), ArticleFig(id=1311350366302729022, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350363136029457, language=CN, label=图1, caption=2017—2025年流感流行季核酸检测阳性率及毒株亚型构成, figureFileSmall=TJ+k1loDmXIPxkQhY4iK1w==, figureFileBig=jhsSsWwu6MOgV2/hJOX4bw==, tableContent=null), ArticleFig(id=1311350366491472703, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350363136029457, language=EN, label=Figure 2, caption=Distribution map of seasonal influenza composite index, 2017—2025, figureFileSmall=IPR79fVNaoRW0wcq+uaa+A==, figureFileBig=fkPCnUTOPTeGxlkT4u7Ghg==, tableContent=null), ArticleFig(id=1311350366571164481, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350363136029457, language=CN, label=图2, caption=2017—2025年流行季流感综合指数分布图, figureFileSmall=IPR79fVNaoRW0wcq+uaa+A==, figureFileBig=fkPCnUTOPTeGxlkT4u7Ghg==, tableContent=null), ArticleFig(id=1311350366642467650, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350363136029457, language=EN, label=Figure 3, caption=Distribution map of influenza composite index for the 2025—2026 influenza season, figureFileSmall=lTunUgbr74cJbSLgHDrXVQ==, figureFileBig=SzbrcycSUOuMpPqGWdxw7Q==, tableContent=null), ArticleFig(id=1311350366730548035, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350363136029457, language=CN, label=图3, caption=2025—2026年流行季流感综合指数分布图, figureFileSmall=lTunUgbr74cJbSLgHDrXVQ==, figureFileBig=SzbrcycSUOuMpPqGWdxw7Q==, tableContent=null), ArticleFig(id=1311350366801851204, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350363136029457, language=EN, label=Table 1, caption=

Cross-validation fitting effect table for each season

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指标流行季（年）
2017/20182018/20192019/20202021/20222023/20242024/2025
灵敏度0.890.950.920.921.000.69
特异度0.850.921.001.001.001.00
约登指数0.740.870.920.921.000.69
), ArticleFig(id=1311350366877348677, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350363136029457, language=CN, label=表1, caption=

各流行季的交叉验证拟合效果表

, figureFileSmall=null, figureFileBig=null, tableContent=
指标流行季（年）
2017/20182018/20192019/20202021/20222023/20242024/2025
灵敏度0.890.950.920.921.000.69
特异度0.850.921.001.001.001.00
约登指数0.740.870.920.921.000.69
), ArticleFig(id=1311350366952846150, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350363136029457, language=EN, label=Table 2, caption=

Comparison of model performance and epidemic season parameters under different analytical schemes

, figureFileSmall=null, figureFileBig=null, tableContent=
分析方案灵敏度特异度约登指数流行开始（周次）流行结束（周次）持续时间（周）
原始模型0.900.960.862025年第45周2026年第9周17
敏感性分析0.780.970.752025年第50周2026年第11周14
), ArticleFig(id=1311350367032537927, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350363136029457, language=CN, label=表2, caption=

不同分析方案的模型性能与流行季参数比较

, figureFileSmall=null, figureFileBig=null, tableContent=
分析方案灵敏度特异度约登指数流行开始（周次）流行结束（周次）持续时间（周）
原始模型0.900.960.862025年第45周2026年第9周17
敏感性分析0.780.970.752025年第50周2026年第11周14
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贵州省冬春季流感综合指数构建及分级预警研究
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郑鑫 1 , 罗国美 2 , 雷明玉 1
现代预防医学 | 疾病预防控制 2026,53(15): 2860-2864
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现代预防医学 |疾病预防控制 2026 , 53 (15) : 2860 -2864
贵州省冬春季流感综合指数构建及分级预警研究
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郑鑫1, 罗国美2, 雷明玉1
作者信息
  • 1.贵州省疾病预防控制中心传染病防治研究所，贵州 贵阳 550004
  • 2.贵州医科大学公共卫生与健康学院
通讯作者:
雷明玉，E-mail：
作者简介:

郑鑫（1998—），男，硕士，医师，研究方向：疾病预防与控制

Construction and tiered early warning system for the influenza composite index in Guizhou Province during winter and spring
Xin ZHENG1, Guo-mei LUO2, Ming-yu LEI1
Affiliations
  • Institute for Infectious Disease Prevention and Control, Guizhou Provincial Center for Disease Control and Prevention, Guiyang, Guizhou 550004, China
出版时间: 2026-08-10 doi: 10.20043/j.cnki.MPM.202512445
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摘要
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目的

构建贵州省流感综合指数，预测流感预警阈值及分级阈值。

方法

选取贵州省2017/2018—2024/2025年流感流行季的冬春季流感监测数据，选取流感样病例百分比、流感核酸检测阳性率和流感样病例暴发疫情数三个指标构建流感综合指数，采用移动流行区间法预测贵州省2025/2026流行季流感流行阈值及分级阈值，评价指标为灵敏度、特异度和约登指数。

结果

最终选取了2017/2018、2018/2019、2019/2020、2021/2022、2023/2024、2024/2025年度六个流行季的监测数据并构建移动流行区间法模型进行预警分析，模型的灵敏度、特异度、约登指数分别为0.90、0.96、0.86。2025/2026流行季的流行前阈值为0.73，流行开始周次为2025年第45周，结束周次为2026年第9周，流行持续时间为17周。

结论

构建的流感综合指数评估贵州省流感流行水平效果较好，可为贵州省流感监测预警提供依据。

流感  /  综合指数  /  预警  /  强度分级
Objective

To construct an influenza composite index for Guizhou Province and predict the epidemic threshold and graded thresholds for influenza.

Methods

Winter-spring influenza surveillance data from the 2017/2018 to 2024/2025 influenza seasons in Guizhou Province were selected. The influenza composite index was constructed using three indicators: the influenza-like illness percentage, influenza nucleic acid positivity rate, and influenza-like illness outbreak count. The moving epidemic method (MEM) was applied to predict the epidemic threshold and graded thresholds for the 2025/2026 influenza season in Guizhou Province. The model performance was evaluated using sensitivity, specificity, and the Youden index.

Results

A total of six influenza seasons (2017/2018, 2018/2019, 2019/2020, 2021/2022, 2023/2024, and 2024/2025) were finally included for constructing the MEM model and conducting early warning analysis. The sensitivity, specificity, and Youden index of the model were 0.90, 0.96, and 0.86, respectively. For the 2025/2026 influenza season, the pre-epidemic threshold was 0.73,the epidemic start week was week 45 of 2025, the end week was week 9 of 2026, and the estimated epidemic duration was 17 weeks.

Conclusion

The constructed influenza composite index demonstrates good performance in assessing influenza epidemic levels in Guizhou Province and can provide a scientific basis for influenza surveillance and early warning in the region.

Influenza  /  Composite index  /  Early warning  /  Intensity grading
郑鑫, 罗国美, 雷明玉. 贵州省冬春季流感综合指数构建及分级预警研究. 现代预防医学, 2026 , 53 (15) : 2860 -2864 . DOI: 10.20043/j.cnki.MPM.202512445
Xin ZHENG, Guo-mei LUO, Ming-yu LEI. Construction and tiered early warning system for the influenza composite index in Guizhou Province during winter and spring[J]. Modern Preventive Medicine, 2026 , 53 (15) : 2860 -2864 . DOI: 10.20043/j.cnki.MPM.202512445
正文
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流感是一种由RNA病毒引起的急性呼吸道疾病[1]，人群普遍易感。世界卫生组织估算每年有300～500万严重病例以及29～ 65万呼吸道相关死亡病例[2]。在我国每年约有340万例流感样疾病患者,每年平均约有8.8万人死于流感相关呼吸系统疾病，占所有呼吸系统疾病死亡的8.2%，疾病负担严峻[3]。流感预警模型是流感防控的重要工具。现有研究主要采用时间序列模型、广义相加模型、传播动力学模型等，大都是用单一监测指标预测流感流行特征，且不能对流行水平进行分级[4]。Vega等人[5]提出的移动流行区间法（moving epidemic method，MEM）模型，可利用历史流感监测数据对流感进行预测并分级，以便于采取对应的防控措施，已在全球多个国家得到应用。我国北京、深圳、江苏等地也据此构建了各地的流感综合指数和预警分级指标。然而，流感流行特征受气候和地域因素影响显著[6]，贵州省地处云贵高原，气候湿润、多雨多雾，冬春季湿冷，与北方干燥寒冷及南方湿热气候均有差异[7]，其流感流行规律可能具有独特性，有必要建立适用于贵州省的专属流感综合指数和预警分级标准。目前贵州省尚未构建本地化的流感预警体系，因此，本研究以贵州省流感哨点医院监测数据为基础，采用MEM模型构建流感综合指数和预警分级标准，拟为贵州省流感分级预警提供科学依据。
1　资料与方法
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1.1　数据来源
选择贵州省2017/2018—2024/2025年共八个流行季的相关流感监测数据，选取流感样病例百分比、流感病毒核酸阳性率及流感样病例暴发疫情数三个关键指标进行分析。
我国北方每年流感活动高峰一般均发生在当年11月底至来年的2月底，具有明确的冬春季流行特征，该时间窗口能完整覆盖流感流行的上升期、高峰期和下降期全周期[8]，因此，本研究将流行季定义为当年第40周至次年第20周[5]。
1.2　纳入排除标准
纳入标准：（1）2017—2025年期间哨点医院监测数据；（2）监测指标数据无连续缺失超过2周。
排除标准：（1）每年第21—39周的监测数据；（2）未形成明确流行峰的流行季；（3）主要流行峰不在当年第40周—次年20周之内的流行季。
1.3　指标定义
流感样病例定义为：患者出现发热症状（体温≥38°C），并伴有咳嗽或咽痛中的至少一种临床表现。流感样病例百分比是指在哨点医院监测门诊中，符合流感样病例定义的病例数占总就诊人数的比例。流感病毒核酸阳性率是指哨点医院依据《全国流感监测方案》要求，每周采集流感样病例和住院严重急性呼吸道感染病例的咽拭子样本，送至辖区网络实验室进行流感病毒核酸检测，检测结果为阳性的样本所占的比例。
流感样病例暴发疫情数的标准为：在一周时间内，同一所学校、托幼机构或其他集体单位中出现10例及以上的流感样病例，即计为一起暴发事件。
1.4　研究方法
1.4.1　MEM
MEM通过分析多个流行季的历史监测数据，构建一条反映疾病基线活动水平的动态阈值曲线。根据MEM模型得到的流行前、中等强度、高强度、极高强度4个阈值，可将疾病流行强度分为基线、低、中、高、超高5个等级[5]。为评估上述阈值在流感流行判别中的效果，本研究以灵敏度衡量模型捕捉流行周的能力，以特异度反映其排除非流行周的准确性，并以约登指数综合评价整体区分效能。若三项指标均高于0.7，则表明模型拟合效果良好。
1.4.2　流感综合指数的构建
（1）　指标标准值的计算
流感样病例百分比用纳入数据的第75百分位数作为标准值，将MEM模型得出的流感病毒核酸阳性率和流感样病例暴发疫情数的流行前阈值作为其标准值。
（2）　流感综合指数
流感综合指数计算公式如下：
公式（1）中，Xij为第i项指标在第j周的实测值；Mi为其对应标准值；Wi为经专家咨询法赋权的权重系数（i=1、2、3）。参照专家意见，流感样病例百分比、病毒核酸阳性率、暴发疫情数分别赋权 0.4、0.4、0.2，据此测算贵州省流感综合指数并划分流行强度等级。
1.5　敏感性分析
为检验剔除2020/2021和2022/2023年两个异常流行季对模型结果的影响，本研究进行了敏感性分析，比较纳入与剔除该两个流行季前后模型参数及流行特征的变化。
1.6　统计学分析
运用Excel软件清洗数据并建立数据库，利用R 4.5.0中“mem”包构建MEM模型，以预测贵州省2025/2026年流行季的流感强度和预警分级标准，模型构建过程包括数据选取、最优参数选择、流行阈值确定和拟合优度检验等。
2　结果
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2.1　流感核酸检测结果
2017/2018至2024/2025年度共八个流行季，贵州省30家网络实验室累积检测流感核酸样本103 405份，其中阳性标本22 605份，阳性率为21.86%。八个流行季的核酸阳性率分别为：2017/2018年27.00%、2018/2019年30.94%、2019/2020年17.42%、2020/2021年5.38%、2021/2022年28.58%、2022/2023年20.76%、2023/2024年34.25%和2024/2025年17.75%。除2020/2021无明显峰值，2022/2023年出现夏季高峰外，其余流行季均表现为冬春季流行，综合考虑，选取2017/2018、2018/2019、2019/2020、 2021/2022、 2023/2024和2024/2025年六个流行季的监测数据构建流感综合指数。见图1。
2.2　指标标准值
根据上述六个流行季的流感核酸检测阳性率及流感样病例暴发疫情数进行MEM模型分析，结果显示，当关键参数δ分别为1.9和3.9时，流感核酸检测阳性率及流感样病例暴发疫情数的MEM模型拟合效果最好，得到的流行前阈值分别为21.51%和0.77，流感样病例百分比的标准值为纳入所有值的第75百分位数7.91%。
2.3　流感综合指数
基于六个流行季三个指标数据及其标准值构建流感综合指数，MEM模型结果显示，当关键参数δ为2.1时，模型灵敏度为0.90,特异度为0.96，约登指数为0.86，拟合效果较好。各流行季的交叉验证拟合效果均较好。见表1。
MEM模型划分6个流行季的流行前阈值分别为0.68、0.69、0.74、0.70、0.73、0.69，流行持续时间分别为20、22、14、28、21和17周。根据分级阈值及峰值大小评估，2017/2018年流行季流行强度为超高流行水平，2018/2019、2019/2020和2021/2022年流行季流行强度均为中等流行水平，2023/2024年流行季流行强度为超高流行水平，2024/2025年流行季流行强度为低等流行水平。见图2、表2。
MEM模型预测结果显示，2025/2026年流行季的流行前阈值为0.73，流行开始周次为2025年第45周，结束周次为2026年第9周，持续时间为17周，中等、高、超高流行阈值分别为1.66、2.62、3.21。见图3。
2.4　敏感性分析
为评估剔除2020/2021和2022/2023年两个异常流行季对模型结果的影响，本研究纳入全部8个流行季进行敏感性分析。结果显示，敏感性分析模型的灵敏度为0.78、特异度为0.97、约登指数为0.75，与原始模型相比，灵敏度和约登指数均降低，导致模型性能下降和流行参数偏移，对典型冬春季流行模式的识别能力下降。鉴于本研究的目的是构建针对贵州省冬春季流感典型流行特征的预警模型，且剔除标准基于预先设定，因此剔除这两个异常流行季具有方法学合理性。
3　讨论
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文献指出[5]，MEM模型应用条件需要至少五个流行季的数据。贵州省2017—2025年共八个流感流行季，受新冠疫情的影响，相关防控措施的实施遏制流感的传播流行，导致2020/2021年流行季流感呈低流行状态，大量易感人群堆积，但随着2022年底新冠防控措施调整及2023年初新冠开始实施“乙类乙管”后，人口流动性增加促进了流感的传播[9]，导致2022/2023年流行季出现夏季流行高峰，破坏了冬春季流行规律，故剔除2020/2021和2022/2023年两个流行季，选取了2017/2018、2018/2019、2019/2020、2021/2022、2023/2024和2024/2025年等六个流行季的数据，符合模型数据要求。结果显示MEM模型的灵敏度为0.90,特异度为0.96，约登指数为0.86，拟合效果较好。
当前哨点医院是以症状和病原体结合的方式对流感进行监测[10]，流感样病例百分比、流感病毒核酸检测阳性率及流感样病例暴发疫情数等监测内容分别从病毒传播的活动水平、医疗机构的接诊负荷及流感在集体单位中引发的集中发病情况等不同维度反映流感病毒在人群中的流行情况，且有研究建议将实验室数据与流行病学参数结合起来，以增强阈值的稳健性[11]，基于上述三项指标构建的流感综合指数，能够更为全面地衡量流感的整体活动水平和流行强度，为流感的精准预警和流行强度的科学分级提供依据不同流行强度下所需的防控资源配置差异显著，分级预警更具实际指导意义。
MEM模型应用于流感预警与分级的研究中，已有部分文献探讨了其在不同数据源和组合中的表现。四川省[12]和宁波市[13]仅使用流感核酸阳性率建立监测预警模型，均有较好的应用效果，北京市[14]采用流感样病例数、流感病毒核酸阳性率和流感聚集性疫情数进行模型模拟，在灵敏度和特异度方面也取得了良好结果。本研究结果显示，使用流感样病例百分占比、流感病毒核酸阳性率和流感样病例暴发疫情数构建的MEM模型灵敏度为0.90，特异度为0.96，约登指数为0.86，模型整体拟合效果较好。在六个流行季中，流行前阈值范围在0.68～0.74之间，超高流行阈值介于2.71～3.16之间，流行一般始于第47—49周，持续时间为13～27周。上述分级阈值及流行持续时间与其他研究报道的范围基本一致[15-16]。未来可进一步比较MEM模型在不同监测指标组合中的适用性，筛选出最优指标组合，以提高流感监测预警分级的及时性和准确性。
评价指标直接反映模型的拟合效果[17]。2024/2025年流行季的模型灵敏度为0.69，低于其它流行季灵敏度，该季模型对疫情高峰的识别能力显著下降。其原因可能是，贵州省2023年4月哨点监测范围从13家市州级医院扩大到含区县级医院29家，2024年又对个别监测点进行调整，因此2024/2025年流行季的模型灵敏度较低可能与监测体系处于启动与调整期有关，具体表现为部分新增哨点医院在病例定义理解、样本采集、送检流程、检测质量及报告时效等方面的执行能力尚不完善，导致监测数据质量不佳，进而影响了模型的拟合效果。虽然2024/2025年流行季模型灵敏度较低，影响了该季疫情高峰的预警效果，但模型特异度较高，表明其对非流行时段的识别能力良好。在流感防控实践中，高特异度有助于避免因误报而启动不必要的公共卫生响应，从而节约有限的医疗和防控资源[18]。因此，后续要加强对哨点医院及网络实验室的培训和指导，制定数据质控细则，严格报告及时率、数据完整率等核心指标，加快推进医院与疾控信息平台对接，部署实时上报系统，以提升报告效率、减少漏报错报，进一步提升监测数据的可靠性与代表性，从而提高预警模型的及时性与准确性。
本研究存在以下局限性：（1）受模型限制，未纳入气象因素和社会行为因素等协变量分析，可能影响模型在不同情境下的适用性；（2）未对不同流感亚型分别建模，而各亚型的流行规律和传播强度存在显著差异，统一建模可能掩盖特定亚型的早期暴发信号；（3）模型的预测能力尚未经过前瞻性验证，需待2025/2026年流行季结束后利用实际监测数据进行回代验证，才能客观评估其真实预警性能。针对上述局限，本研究得到的MEM模型将在后续监测工作中进一步优化，包括纳入气象与社会行为因素、分亚型建模、扩展全周期数据窗口以及开展前瞻性验证。
基金
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  • 贵州省流感流行病学特征及疾病负担研究(gzwkj2021-549)
文献
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参考文献 引证文献
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2026年第53卷第15期
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doi: 10.20043/j.cnki.MPM.202512445
  • 接收时间：2025-12-25
  • 首发时间：2026-09-28
  • 出版时间：2026-08-10
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  • 收稿日期：2025-12-25
基金
贵州省流感流行病学特征及疾病负担研究(gzwkj2021-549)
作者信息
    1.贵州省疾病预防控制中心传染病防治研究所，贵州 贵阳 550004
    2.贵州医科大学公共卫生与健康学院

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2种不同金属材料的力学参数
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Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
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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
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
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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