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Article(id=1322609108113539500, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1322609030938346427, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202505418, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1748448000000, receivedDateStr=2025-05-29, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793264184263, onlineDateStr=2026-10-29, pubDate=1764000000000, pubDateStr=2025-11-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793264184263, onlineIssueDateStr=2026-10-29, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793264184263, creator=13701087609, updateTime=1793264184263, updator=13701087609, issue=Issue{id=1322609030938346427, tenantId=1146029695717560320, journalId=1227665162245664772, year='2025', volume='52', issue='22', pageStart='4033', pageEnd='4224', issueExtLink='null', onlineDate='null', pubDate='1764000000000', pubDateStr='2025-11-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793264165863, creator='13701087609', updateTime=1793264299902, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322609593264489401, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1322609030938346427, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322609593264489402, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1322609030938346427, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=4052, endPage=4058, ext={EN=ArticleExt(id=1322609108289700269, articleId=1322609108113539500, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Application of spatial-temporal multicomponent model based on different spatial weight settings to influenza incidence data in Sichuan Province with complex terrain, columnId=1228016567443718970, journalTitle=Modern Preventive Medicine, columnName=Epidemiology and Statistical Methods Advances, runingTitle=null, highlight=null, articleAbstract=
Objective

To compare the spatial-temporal multicomponent model with different spatial weight settings in different terrains, and to explore the optimal weight settings in different terrains. Then to study the distribution and influencing factors of influenza incidence in different terrains of Sichuan Province, so as to provide scientific basis for spatial weight settings and identification of risk influencing factors as well as the formulation of prevention and control measures.

Methods

Based on the influenza incidence data in Sichuan Province from 2016 to 2018, we conducted a descriptive analysis of the spatial and temporal influenza situation in different terrain areas（plains, hills and plateau mountainous regions）, explored the optimal weight settings for different terrains using a spatial-temporal multicomponent model, and further analyzed the spatial and temporal influenza propagation dynamics through the optimal model to quantify the effects of meteorological and socio-economic factors.

Results

The optimal weights were inverse distance weights for plains and Power-law weights for hills and plateau and mountainous regions. The results of the factor analysis of the optimal model showed that the average temperature in the plains（RR=0.970, 95% CI: 0.958-0.982, P<0.001）, hills（RR=0.960, 95% CI: 0.942-0.978, P<0.001）and plateau and mountainous regions（RR=0.913, 95% CI: 0.868-0.960, P<0.001）were all inhibitory to the risk of influenza. Air pressure was protective in the plains（RR=0.990, 95% CI: 0.979-1.000, P=0.049）, while showing elevated risk in the hills（RR=1.044, 95% CI: 1.016-1.072, P=0.002）and plateau and mountainous regions（RR=1.005, 95% CI: 1.002-1.009, P=0.004）.

Conclusion

The spatial weights of plains, hills, plateau and mountainous regions should be carefully considered when constructing the model. Inverse distance weights are more suitable for plains, while Power-law weights are more suitable for hills, plateau and mountainous regions. Meanwhile, there are differences in the influence of influenza morbidity risk in different topographic areas, and it is suggested that different early warning and control measures should be developed according to the key risk factors in different areas and key risk characteristics of the areas.

, authors=Hai-ping HUANG1, Ying CHEN1, Heng YUAN2, Tao ZHANG1, authorsList=Hai-ping HUANG, Ying CHEN, Heng YUAN, Tao ZHANG, authorCompany=null, correspAuthors=Heng YUAN, Tao ZHANG, 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=1322609112400118215, articleId=1322609108113539500, tenantId=1146029695717560320, journalId=1227665162245664772, language=CN, title=基于不同空间权重设置下的时空多成分模型在四川省复杂地形流感发病数据中的应用, columnId=1228016567632462653, journalTitle=现代预防医学, columnName=流行病与统计方法, runingTitle=null, highlight=null, articleAbstract=
目的

比较不同地形下不同空间权重设置的时空多成分模型，探讨不同地形下的最优权重设置，并研究四川省不同地形地区流感发病的分布及影响因素，为空间权重设置和风险影响因素识别及防控措施制定提供科学依据。

方法

基于2016—2018年四川省流感发病数据，对不同地形地区（平原、丘陵和高原山地）的流感时空情况进行描述性分析，采用时空多成分模型探讨不同地形的最优权重设置，并进一步通过最优模型分析流感的时空传播动态变化，量化气象因素和社会经济因素等因素在不同组分上的效应大小。

结果

平原地区最优权重设置为反距离权重，丘陵和高原山地地区为Power-law权重。影响因素分析结果显示平均气温在平原地区（RR=0.970，95% CI：0.958~0.982，P<0.001），丘陵（RR=0.960，95% CI：0.942~0.978，P<0.001）和高原山地地区（RR=0.913，95% CI：0.868~0.960，P<0.001）均对流感发病风险起到抑制作用，平原地区气压起保护作用（RR=0.990，95% CI：0.979~1.000，P=0.049），而丘陵（RR=1.044，95% CI：1.016~1.072，P=0.002）和高原山地地区（RR=1.005，95% CI：1.002~1.009，P=0.004）气压均起到促进作用。

结论

平原、丘陵和高原山地地区在构建模型时的空间权重应慎重考虑，平原地区更适用于反距离权重、而丘陵和高原山地地区更适用于Power-law权重，同时不同地形地区流感发病风险的影响存在差异，建议应根据不同地区重点风险因素和地区重点风险特征制定不同的预警防控措施。

, authors=黄海平1, 陈莹1, 袁珩2, 张韬1, authorsList=黄海平, 陈莹, 袁珩, 张韬, authorCompany=null, correspAuthors=袁珩, 张韬, authorNote=

黄海平（1999—），男，硕士在读，研究方向：卫生统计学

, correspAuthorsNote=
张韬和袁珩为共同通信作者。张韬，E-mail：；
袁珩，E-mail：
, copyrightStatement=本刊刊出的所有文章不代表中华预防医学会和本刊编委会的观点，除非特别声明。, copyrightOwner=中华预防医学会和四川大学华西公共卫生学院, extLink=null, articleAbsUrl=null, sourceXml=NjQ9KvjubtP5HqEuMufl1A==, magXml=McLRq1oNIKm4wlTmh0ebRg==, pdfUrl=null, pdf=lm1yOnyHMSugepLrQZTpRg==, pdfFileSize=1660065, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=PCjfng1kDIQIQTRAIUcYUw==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=mP6Zz72HV37BkEArJ8Un1Q==, mapNumber=null, fund=null)}, authors=[Author(id=1322609112882463182, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322609108113539500, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=null, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1322609112983126480, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322609108113539500, authorId=1322609112882463182, language=EN, stringName=Hai-ping HUANG, firstName=Hai-ping, middleName=null, lastName=HUANG, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=1, address=West China School of Public Health/West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1322609113050235345, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322609108113539500, authorId=1322609112882463182, language=CN, stringName=黄海平, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=1, address=1.四川大学华西公共卫生学院/华西第四医院，四川　成都　610041, bio={"content":"

黄海平（1999—），男，硕士在读，研究方向：卫生统计学

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BMC Public Health, 2025, 25 (1): 526., articleTitle=The mediating effect of air pollution on the association between meteorological factors and influenza-like illness in China, refAbstract=null)], funds=[Fund(id=1322609117047407099, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322609108113539500, awardId=2022YFC3600604, language=CN, fundingSource=国家重点研发计划项目(2022YFC3600604), fundOrder=null, country=null), Fund(id=1322609117135487484, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322609108113539500, awardId=SYYXH202403; SYYXHPT202406, language=CN, fundingSource=四川省预防医学会基层公共卫生专项科研项目(SYYXH202403; SYYXHPT202406), fundOrder=null, country=null), Fund(id=1322609117202596349, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322609108113539500, awardId=2024ZYD0102, language=CN, fundingSource=四川省科技计划项目-中央引导地方科技发展项目(2024ZYD0102), fundOrder=null, country=null), Fund(id=1322609117290676734, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322609108113539500, awardId=2025YFHZ0069, language=CN, fundingSource=四川省科技计划项目-省院省校合作项目(2025YFHZ0069), fundOrder=null, country=null), Fund(id=1322609117353591295, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322609108113539500, awardId=2024-YF05-01784-SN, language=CN, fundingSource=成都市科技局项目(2024-YF05-01784-SN), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1322609112706302408, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322609108113539500, xref=1., ext=[AuthorCompanyExt(id=1322609112718885321, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322609108113539500, companyId=1322609112706302408, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=West China School of Public Health/West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China), AuthorCompanyExt(id=1322609112731468234, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322609108113539500, companyId=1322609112706302408, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1.四川大学华西公共卫生学院/华西第四医院，四川　成都　610041)]), AuthorCompany(id=1322609112819548619, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322609108113539500, xref=2., ext=[AuthorCompanyExt(id=1322609112827937228, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322609108113539500, companyId=1322609112819548619, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2.四川省疾病预防控制中心急性传染病预防控制所)])], figs=[ArticleFig(id=1322609114530824683, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322609108113539500, language=EN, label=Fig.1, caption=Temporal distribution of the number of influenza cases in different topographic areas of Sichuan Province, 2016-2018, figureFileSmall=eOywtWEaBgZQOcBfSBdrAA==, figureFileBig=PCjfng1kDIQIQTRAIUcYUw==, tableContent=null), ArticleFig(id=1322609114589544940, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322609108113539500, language=CN, label=图1, caption=2016—2018年四川省不同地形地区流感发病数时间分布, figureFileSmall=eOywtWEaBgZQOcBfSBdrAA==, figureFileBig=PCjfng1kDIQIQTRAIUcYUw==, tableContent=null), ArticleFig(id=1322609115961082349, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322609108113539500, language=EN, label=Fig.2, caption=Correlation analysis diagram, figureFileSmall=OfN7aGBHx10gF22hQFyKow==, figureFileBig=SZ46PDJCpi2F1wRDEbKepA==, tableContent=null), ArticleFig(id=1322609116028191214, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322609108113539500, language=CN, label=图2, caption=相关分析图

注：*表示P<0.05;**表示P<0.01;***表示P<0.001。

, figureFileSmall=OfN7aGBHx10gF22hQFyKow==, figureFileBig=SZ46PDJCpi2F1wRDEbKepA==, tableContent=null), ArticleFig(id=1322609116124660207, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322609108113539500, language=EN, label=Fig.3, caption=Distribution of transmission components of influenza cases in different terrain areas in Sichuan Province, 2016-2018, figureFileSmall=S8WiBaUe25s+ftc4fyDq+w==, figureFileBig=9EXJYwYb+P/o6+7EekaXpw==, tableContent=null), ArticleFig(id=1322609116195963376, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322609108113539500, language=CN, label=图3, caption=2016—2018年四川省不同地形地区流感病例传播成分分布, figureFileSmall=S8WiBaUe25s+ftc4fyDq+w==, figureFileBig=9EXJYwYb+P/o6+7EekaXpw==, tableContent=null), ArticleFig(id=1322609116258877937, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322609108113539500, language=EN, label=Table 1, caption=

Evaluation results of fixed effects model under four weights in different terrain areas

, figureFileSmall=null, figureFileBig=null, tableContent=
模型模型AIC
平原丘陵高原山地
Power-law权重
基础模型3 115.0455 452.0813 488.082
时间自回归3 090.8475 435.4843 475.871
时间自回归+空间流行成分3 073.0835 435.9503 458.355
时间自回归+空间流行成分+地方性成分3 086.1565 391.3043 462.955
Rook权重
基础模型3 115.7055 480.6343 548.933
时间自回归3 092.8925 460.6643 539.976
时间自回归+空间流行成分3 097.9785 462.2633 531.873
时间自回归+空间流行成分+地方性成分3 095.0505 441.4503 517.465
反距离权重
基础模型3 115.6245 459.8243 507.486
时间自回归3 090.7175 443.4833 497.247
时间自回归+空间流行成分3 072.2325 446.3023 486.591
时间自回归+空间流行成分+地方性成分3 090.1425 412.4783 485.139
K邻近算法权重
基础模型3 115.7055 487.1433 535.921
时间自回归3 092.8925 468.3403 529.108
时间自回归+空间流行成分3 097.9785 470.6803 526.797
时间自回归+空间流行成分+地方性成分3 095.0505 456.1873 516.812
), ArticleFig(id=1322609116334375410, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322609108113539500, language=CN, label=表1, caption=

不同地形地区四种权重下的固定效应模型评价结果

, figureFileSmall=null, figureFileBig=null, tableContent=
模型模型AIC
平原丘陵高原山地
Power-law权重
基础模型3 115.0455 452.0813 488.082
时间自回归3 090.8475 435.4843 475.871
时间自回归+空间流行成分3 073.0835 435.9503 458.355
时间自回归+空间流行成分+地方性成分3 086.1565 391.3043 462.955
Rook权重
基础模型3 115.7055 480.6343 548.933
时间自回归3 092.8925 460.6643 539.976
时间自回归+空间流行成分3 097.9785 462.2633 531.873
时间自回归+空间流行成分+地方性成分3 095.0505 441.4503 517.465
反距离权重
基础模型3 115.6245 459.8243 507.486
时间自回归3 090.7175 443.4833 497.247
时间自回归+空间流行成分3 072.2325 446.3023 486.591
时间自回归+空间流行成分+地方性成分3 090.1425 412.4783 485.139
K邻近算法权重
基础模型3 115.7055 487.1433 535.921
时间自回归3 092.8925 468.3403 529.108
时间自回归+空间流行成分3 097.9785 470.6803 526.797
时间自回归+空间流行成分+地方性成分3 095.0505 456.1873 516.812
), ArticleFig(id=1322609116426650099, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322609108113539500, language=EN, label=Table 2, caption=

Evaluation results of random effects model in different terrain areas

, figureFileSmall=null, figureFileBig=null, tableContent=
模型平原丘陵高原山地
LogSRPSLogSRPSLogSRPS
最优固定效应模型4.11937.6883.6417.0614.83724.640
时间自回归4.64342.5163.6657.2674.74223.396
空间流行成分4.11937.6653.6877.0574.85324.225
地方性成分4.11731.1273.6356.9914.75023.460
时间自回归+空间流行成分4.70542.6053.6957.2214.84424.769
时间自回归+地方性成分4.44035.2153.6367.0044.74423.831
空间流行成分+地方性成分4.11731.1273.6817.1234.75123.906
时间自回归+空间流行成分+地方性成分4.44035.2153.6887.2224.83724.640
), ArticleFig(id=1322609116510536180, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322609108113539500, language=CN, label=表2, caption=

不同地形地区随机效应模型评价结果

, figureFileSmall=null, figureFileBig=null, tableContent=
模型平原丘陵高原山地
LogSRPSLogSRPSLogSRPS
最优固定效应模型4.11937.6883.6417.0614.83724.640
时间自回归4.64342.5163.6657.2674.74223.396
空间流行成分4.11937.6653.6877.0574.85324.225
地方性成分4.11731.1273.6356.9914.75023.460
时间自回归+空间流行成分4.70542.6053.6957.2214.84424.769
时间自回归+地方性成分4.44035.2153.6367.0044.74423.831
空间流行成分+地方性成分4.11731.1273.6817.1234.75123.906
时间自回归+空间流行成分+地方性成分4.44035.2153.6887.2224.83724.640
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Results of the final model impact factor analysis for the plains

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解释变量RR（95% CI）P
时间自回归成分
Log（GRP）1.552（1.321~1.824）<0.001
平均温度0.970（0.958~0.982）<0.001
气压0.992（0.986~0.999）0.015
日照时长0.979（0.939~1.020）0.309
空间流行成分
Log（GRP）1.894（0.723~4.959）0.194
平均温度0.976（0.936~1.017）0.245
气压0.990（0.979~1.000）0.049
日照时长1.006（0.868~1.166）0.933
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平原地区的最终模型影响因素分析结果

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解释变量RR（95% CI）P
时间自回归成分
Log（GRP）1.552（1.321~1.824）<0.001
平均温度0.970（0.958~0.982）<0.001
气压0.992（0.986~0.999）0.015
日照时长0.979（0.939~1.020）0.309
空间流行成分
Log（GRP）1.894（0.723~4.959）0.194
平均温度0.976（0.936~1.017）0.245
气压0.990（0.979~1.000）0.049
日照时长1.006（0.868~1.166）0.933
), ArticleFig(id=1322609116728639991, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322609108113539500, language=EN, label=Table 4, caption=

Results of the final model impact factor analysis for hilly regions

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解释变量RR（95% CI）P
时间自回归成分
公路里程数1.000（1.000~1.000）0.835
平均温度0.960（0.942~0.978）<0.001
气压1.004（0.992~1.016）0.549
日照时长0.970（0.919~1.023）0.262
空间流行成分
公路里程数1.000（1.000~1.000）0.424
气压0.996（0.969~1.024）0.775
日照时长1.002（0.908~1.106）0.968
地方性成分
公路里程数1.000（1.000~1.000）0.268
气压1.044（1.016~1.072）0.002
日照时长1.015（0.957~1.077）0.612
), ArticleFig(id=1322609116795748856, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322609108113539500, language=CN, label=表4, caption=

丘陵地区的最终模型影响因素分析结果

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解释变量RR（95% CI）P
时间自回归成分
公路里程数1.000（1.000~1.000）0.835
平均温度0.960（0.942~0.978）<0.001
气压1.004（0.992~1.016）0.549
日照时长0.970（0.919~1.023）0.262
空间流行成分
公路里程数1.000（1.000~1.000）0.424
气压0.996（0.969~1.024）0.775
日照时长1.002（0.908~1.106）0.968
地方性成分
公路里程数1.000（1.000~1.000）0.268
气压1.044（1.016~1.072）0.002
日照时长1.015（0.957~1.077）0.612
), ArticleFig(id=1322609116858663417, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322609108113539500, language=EN, label=Table 5, caption=

Results of the final model impact factor analysis for the plateau and mountains regions

, figureFileSmall=null, figureFileBig=null, tableContent=
解释变量RR（95% CI）P
时间自回归成分
公路里程数1.000（1.000~1.000）0.319
平均温度0.989（0.958~1.021）0.509
气压1.005（1.002~1.009）0.004
日照时长1.081（0.997~1.172）0.061
空间流行成分
公路里程数1.000（1.000~1.000）0.236
气压1.003（0.996~1.010）0.410
平均温度0.913（0.868~0.960）<0.001
), ArticleFig(id=1322609116929966586, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322609108113539500, language=CN, label=表5, caption=

高原山地地区的最终模型影响因素分析结果

, figureFileSmall=null, figureFileBig=null, tableContent=
解释变量RR（95% CI）P
时间自回归成分
公路里程数1.000（1.000~1.000）0.319
平均温度0.989（0.958~1.021）0.509
气压1.005（1.002~1.009）0.004
日照时长1.081（0.997~1.172）0.061
空间流行成分
公路里程数1.000（1.000~1.000）0.236
气压1.003（0.996~1.010）0.410
平均温度0.913（0.868~0.960）<0.001
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基于不同空间权重设置下的时空多成分模型在四川省复杂地形流感发病数据中的应用
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黄海平 1 , 陈莹 1 , 袁珩 2 , 张韬 1
现代预防医学 | 流行病与统计方法 2025,52(22): 4052-4058
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现代预防医学 |流行病与统计方法 2025 , 52 (22) : 4052 -4058
基于不同空间权重设置下的时空多成分模型在四川省复杂地形流感发病数据中的应用
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黄海平（1999—），男，硕士在读，研究方向：卫生统计学

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黄海平1, 陈莹1, 袁珩2 , 张韬1
作者信息
  • 1.四川大学华西公共卫生学院/华西第四医院，四川　成都　610041
  • 2.四川省疾病预防控制中心急性传染病预防控制所
通讯作者:
张韬和袁珩为共同通信作者。张韬，E-mail：；
袁珩，E-mail：
作者简介:

黄海平（1999—），男，硕士在读，研究方向：卫生统计学

Application of spatial-temporal multicomponent model based on different spatial weight settings to influenza incidence data in Sichuan Province with complex terrain
Hai-ping HUANG1, Ying CHEN1, Heng YUAN2 , Tao ZHANG1
Affiliations
  • West China School of Public Health/West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China
出版时间: 2025-11-25 doi: 10.20043/j.cnki.MPM.202505418
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摘要
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目的

比较不同地形下不同空间权重设置的时空多成分模型，探讨不同地形下的最优权重设置，并研究四川省不同地形地区流感发病的分布及影响因素，为空间权重设置和风险影响因素识别及防控措施制定提供科学依据。

方法

基于2016—2018年四川省流感发病数据，对不同地形地区（平原、丘陵和高原山地）的流感时空情况进行描述性分析，采用时空多成分模型探讨不同地形的最优权重设置，并进一步通过最优模型分析流感的时空传播动态变化，量化气象因素和社会经济因素等因素在不同组分上的效应大小。

结果

平原地区最优权重设置为反距离权重，丘陵和高原山地地区为Power-law权重。影响因素分析结果显示平均气温在平原地区（RR=0.970，95% CI：0.958~0.982，P<0.001），丘陵（RR=0.960，95% CI：0.942~0.978，P<0.001）和高原山地地区（RR=0.913，95% CI：0.868~0.960，P<0.001）均对流感发病风险起到抑制作用，平原地区气压起保护作用（RR=0.990，95% CI：0.979~1.000，P=0.049），而丘陵（RR=1.044，95% CI：1.016~1.072，P=0.002）和高原山地地区（RR=1.005，95% CI：1.002~1.009，P=0.004）气压均起到促进作用。

结论

平原、丘陵和高原山地地区在构建模型时的空间权重应慎重考虑，平原地区更适用于反距离权重、而丘陵和高原山地地区更适用于Power-law权重，同时不同地形地区流感发病风险的影响存在差异，建议应根据不同地区重点风险因素和地区重点风险特征制定不同的预警防控措施。

时空多成分模型  /  流感  /  空间权重矩阵  /  影响因素  /  时空分析
Objective

To compare the spatial-temporal multicomponent model with different spatial weight settings in different terrains, and to explore the optimal weight settings in different terrains. Then to study the distribution and influencing factors of influenza incidence in different terrains of Sichuan Province, so as to provide scientific basis for spatial weight settings and identification of risk influencing factors as well as the formulation of prevention and control measures.

Methods

Based on the influenza incidence data in Sichuan Province from 2016 to 2018, we conducted a descriptive analysis of the spatial and temporal influenza situation in different terrain areas（plains, hills and plateau mountainous regions）, explored the optimal weight settings for different terrains using a spatial-temporal multicomponent model, and further analyzed the spatial and temporal influenza propagation dynamics through the optimal model to quantify the effects of meteorological and socio-economic factors.

Results

The optimal weights were inverse distance weights for plains and Power-law weights for hills and plateau and mountainous regions. The results of the factor analysis of the optimal model showed that the average temperature in the plains（RR=0.970, 95% CI: 0.958-0.982, P<0.001）, hills（RR=0.960, 95% CI: 0.942-0.978, P<0.001）and plateau and mountainous regions（RR=0.913, 95% CI: 0.868-0.960, P<0.001）were all inhibitory to the risk of influenza. Air pressure was protective in the plains（RR=0.990, 95% CI: 0.979-1.000, P=0.049）, while showing elevated risk in the hills（RR=1.044, 95% CI: 1.016-1.072, P=0.002）and plateau and mountainous regions（RR=1.005, 95% CI: 1.002-1.009, P=0.004）.

Conclusion

The spatial weights of plains, hills, plateau and mountainous regions should be carefully considered when constructing the model. Inverse distance weights are more suitable for plains, while Power-law weights are more suitable for hills, plateau and mountainous regions. Meanwhile, there are differences in the influence of influenza morbidity risk in different topographic areas, and it is suggested that different early warning and control measures should be developed according to the key risk factors in different areas and key risk characteristics of the areas.

Spatial-temporal multicomponent model  /  Influenza  /  Spatial weight matrix  /  Influencing factors  /  Spatial-temporal analysis
黄海平, 陈莹, 袁珩, 张韬. 基于不同空间权重设置下的时空多成分模型在四川省复杂地形流感发病数据中的应用. 现代预防医学, 2025 , 52 (22) : 4052 -4058 . DOI: 10.20043/j.cnki.MPM.202505418
Hai-ping HUANG, Ying CHEN, Heng YUAN, Tao ZHANG. Application of spatial-temporal multicomponent model based on different spatial weight settings to influenza incidence data in Sichuan Province with complex terrain[J]. Modern Preventive Medicine, 2025 , 52 (22) : 4052 -4058 . DOI: 10.20043/j.cnki.MPM.202505418
正文
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流行性感冒（简称流感）是由流感病毒感染引起的急性呼吸道传染病[1]。世界卫生组织（WHO）推测全球每年流感流行在全球可导致300万~500万重症病例，约有多达65万人死于流感相关的呼吸系统疾病[2]。四川省作为我国流感的高发地区之一，各地区发病居高不下[3-4]，由于四川各地区的地理、人口及社会经济条件的差异[5]，流感发病的分布及传播风险在不同地理经济因素的地区存在异质性。因此，在进行时空分析时考虑多因素及空间权重对于准确分析疾病发病的时空特征具有重要意义。
2014年Meyer和Held提出基于人类短期旅行行为可以用幂律（Power-law）来描述的事实，将幂律权重作为空间权重整合到EE模型的空间流行性成分中[6]，该研究在空间权重设置上相对单一，但空间权重矩阵的设置对准确分析疾病发病的空间相关特征具有重要意义[7]，因此本研究采用Held等人提出的时空多成分模型（endemic-epidemic model，EE模型），在原始的幂律权重的基础上进一步考虑更多权重设置，比较在不同地形下不同空间权重设置的模型结果的差异，探讨不同地形下的最优权重设置，并研究四川省不同地形地区流感发病的分布及影响因素，为空间权重设置和风险影响因素识别及防控措施制定提供科学依据。
1　资料与方法
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1.1　资料来源
本研究病例数据来源于四川省疾病预防与控制中心，收集《国家传染病疾病监测系统》2016年1月1日至2018年12月31日四川省内所有流感报告病例的相关信息。同期社会经济因素数据来源于《四川省统计年鉴》，包括四川省21个市州的常住人口数（万人）、地区生产总值（Gross Regional Product，GRP）、公路总里程数。同期气象因素数据来源于国家气象数据共享中心（http://data.cma.cn/），包括平均气温（℃）、气压（hpa）、相对湿度（%）、降雨量（mm）、风速（m/s）以及日照总时数（h）。同期空气质量数据来源于四川省环境检测中心（https://sthjt.Sc.gov.cn/sthjt/c104334/scemc.shtml），包括PM10、PM2.5、SO2、NO2、O3和空气质量指数（Air Quality Index ，AQI）
1.2　研究方法
1.2.1　描述性统计和相关性分析
本研究首先将四川省各市州根据地形划分为平原、高原山地和丘陵，平原地区包括成都、德阳、绵阳和眉山，高原山地地区包括阿坝藏族羌族自治州（阿坝）、甘孜藏族自治州（甘孜）、凉山彝族自治州（凉山）、乐山、攀枝花和雅安，丘陵地区包括自贡、遂宁、内江、南充、资阳、广安、达州和宜宾，对三种地形地区报告流感病例的时空分布进行描述。采用Spearman相关系数法作相关性检验，由于变量间的强相关可能导致共线性的问题，因此排除相关系数大于0.7的变量，并综合考虑既往文献初步筛选纳入模型的变量。
1.2.2　时空多成分模型建模
本研究采用时空多成分模型对流感发病的多区域时空数据进行分析，该模型目前已被广泛运用于传染病的时空分析，模型结构可用下列公式表示：
其中Yi,t表示对于区域i（iϵ1,2,3…n）在时间t（tϵ1,2,3…m）内的发病数，μi,t是Yi,t作为负二项分布的条件均值；vi,t、λi,t和ϕi,t分别为模型中的地方性成分系数、时间自回归成分系数以及空间流行成分系数；ei,t表示地区i在时间t的偏移变量，本研究采用各区域的人口占比作为偏移变量（offset）。对三种不同的传播成分分别建立对数回归模型，具体如下：
其中α0、β0和γ0为对数回归模型中三种传播成分的截距项；αi、βi、γi为三种传播成分的随机效应；α、β和γ表示协变量、和对地方性成分、时间自回归成分以及空间流行成分的作用强度；表示季节效应。ωji表示空间权重矩阵。本研究采用四种权重矩阵，包括：（1）Rook邻接权重矩阵；（2）反距离权重矩阵;（3）Power-law权重矩阵;（4）K邻近算法权重矩阵（k=1）。
1.2.3　模型选择及评价
本研究基于相关性分析结果初步筛选协变量，首先，从仅有截距的模型开始分别在模型不同成分中纳入气象因素和社会人口因素等变量，以赤池信息准则（Akaike information criterion，AIC）选择最优的固定效应模型。其次，在最优固定效应模型的不同成分中分别纳入随机效应，使用对数分数（The logarithmic score，LogS）和排序概率分数（Ranked probability score, RPS）选择相对最优的随机效应模型，分数越小表示预测性能越好。
本文检验水准α=0.05，数据整理、清洗及统计分析分别采用Excel 2016和R 4.3.1软件，采用surveillance程序包构建时空多成分模型。
2　结果
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2.1　描述性分析
四川省平原地区2016—2018年累计发病数为11 404人，年平均发病率为13.85/10万人，丘陵地区累计发病数为6 098人，年平均发病率为6.30/10万人，高原山地地区累计发病数为3 404人，年平均发病率为8.71/10万人。各地形地区在2016—2017年流感报告病例较为规律，每年呈现两次流行高峰，而后病例数逐步下降，呈现明显季节性规律，流行高峰在每年3~4月及10~11月，但在2017年年底至2018年3月以及2018年年底，四川省流感报告病例较往年明显增加。具体见图1（以周为单位）。
2.2　相关性分析
流感发病数与平均气温、日照时长、降雨量和臭氧呈负相关，与平均气压、AQI、PM2.5、NO2、CO和PM10呈正相关。由于平均气温和降雨量与AQI、PM2.5、NO2、CO、O3和PM10变量间存在强相关性（r>0.7）[8]，如果都纳入模型可能导致共线性问题，同时，既往研究中显示地区经济、区域交通设施等因素对于传染病的传播存在影响[9-10]，因此综合相关性分析及既往研究纳入的变量，最终模型选择平均气温、平均气压、日照时长、GRP、公路里程数等变量逐步纳入模型各成分中。详见图2。
2.3　时空多成分模型构建结果
根据相关性分析在各成分中逐步纳入GRP、公路总里程、平均温度、平均气压、日照时长等变量，根据表1所示，平原地区反距离权重最优，最优固定效应模型为在时间自回归和空间流行成分中纳入协变量（AIC=3 072.232）；不同于平原地区，丘陵地区和高原山地地区均为Power-law权重设置最优，最优固定效应模型分别为在所有成分中纳入协变量（AIC=5 391.304），和在时间自回归与空间流行成分中纳入协变量（AIC=3 458.355）。根据各地形最优固定效应模型，在不同成分中纳入随机效应选择最优随机效应模型，结果见表2，结果显示，平原地区（LogS=4.117，RPS=31.127）和丘陵地区（LogS=3.635，RPS=6.991）最优随机效应模型均为在地方性成分纳入随机效应，高原山地地区最优随机效应模型为在时间自回归成分纳入随机效应（LogS=4.742，RPS=23.396）。
2.4　四川流感时空多成分分析
在平原地区，时间自回归成分中Log（GRP）（RR=1.552，95% CI：1.321~1.824，P<0.001）与流感风险升高显著相关；平均温度（RR=0.970，95% CI：0.958~0.982，P<0.001）和气压（RR=0.992，95% CI：0.986~0.999，P=0.015）对流感发病起抑制作用。空间流行成分中，气压的保护作用依然显著（RR=0.990，95% CI：0.979~1.000，P=0.049），具体见表3。丘陵地区的时间自回归成分显示，平均温度（RR=0.960，95% CI：0.942~0.978，P<0.001）对流感发病起抑制作用。与平原地区不同气压的效应不同，丘陵地区地方性成分中气压却与风险升高相关（RR=1.044，95% CI：1.016~1.072，P=0.002），提示气压对流感风险的调控在不同地形可能存在空间异质性，具体见表4。高原山地地区的时间自回归成分显示，气压（RR=1.005，95% CI：1.002~1.009，P=0.004）对流感发病起到促进作用。空间流行成分中，平均温度（RR=0.913，95% CI：0.868~0.960，P<0.001）对流感发病起抑制作用，具体见表5。
平原地区结果显示，绵阳市和成都市2016—2018年流感发病以时间自回归成分为主，德阳市和眉山市则主要以空间流行成分为主。丘陵地区结果显示，大多数地区主要受到时空自回归的影响，少数地区受空间流行成分作用影响相比较大，例如内江。南充市相比其他地区受地方性成分较多，但总体随时间较为稳定。高原山地结果显示，大部分地区主要受到空间流行成分的影响，攀枝花、甘孜、雅安和阿坝受空间流行成分作用影响相比较大。具体见图3。
3　讨论
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研究结果显示不同地形地区的最优权重存在差异，平原地区反距离权重矩阵表现最优，丘陵和高原地区则Power-law权重最优，这可能是由于成都、绵阳等平原地区，其地形平坦且交通网络密集，随人口流动的病原体传播受地理上的阻碍影响较为有限，反距离权重矩阵根据两空间区域的欧氏距离计算，其空间权重矩阵服从地理第一定律，即空间相关性随距离增大而降低的特征，同时区别于另外三种基于邻接关系的权重矩阵，其可以区分某地区相同阶数的邻接区域的空间相关性差异，而非将相同邻接阶数的区域一视同仁，因此，反距离权重在平原地区可以更准确描述流感的传播。而丘陵和高原山地地区存在天然地形的阻碍，此时反距离权重则忽略了地形阻隔的影响，而Power-law权重通过高阶邻域关系和基于数据驱动的衰减参数形成权重矩阵，削弱了地形阻隔的影响，同时相比Rook权重和K近邻算法权重忽略高阶邻接关系，Power-law权重也更加符合流感存在的高阶传播情况，由于Power-law权重的一个重要特点是其随邻接阶数增加逐步收敛至零，这在流感传播中可以描述除了主要的短距离感染外，还能描述病原体的长距离传播，结果与既往研究一致，既往研究将Power-law权重模型与简单高斯模型和一阶权重模型比较，也发现Power-law权重相比一阶权重能够更准确地描述疾病在空间上的传播模式[6]。
影响因素分析显示平原、丘陵和高原山地地区，平均温度对流感发病均起到抑制作用，这与既往研究结果一致[11-12]，Chong 等人[13]基于2004—2019 年香港流感数据，采用回归树模型发现低温有利于流感传播风险的提高。这可能是由于低温会延长流感病毒的存活时间，增强流感病毒的传播性，进而增加宿主对流感病毒的易感性，从而促进流感的传播[14]。同时，在低温条件下，人们可能会减少户外活动，而留在温暖和相对封闭的室内环境中，因此增加了流感传播的机会[15]。在平原地区，GRP与流感风险升高显著相关，可能是由于经济发达地区会促进人口流动[16-17]，且人口聚集性会更高，进而增加了病毒传播机会。气压在不同地形上存在差异，平原地区气压与流感风险呈现负相关，但丘陵与高原山地地区效应作用于其相反，这可能是由于气压与空气污染物和其他气象因素存在复杂关联，有研究认为气压可能通过空气污染物间接影响流感发病情况，影响病毒的附着与传播，进而影响流感发病风险[18]，平原地区人口和工业区密集，受到空气污染物的影响较大。另有研究认为，高气压通常意味着晴天，而低气压意味着降雨，进而影响人们户外接触病毒的风险[19-20]，丘陵和高原山地地区人们出行主要受到天气影响，高气压时的无雨天气人们出行频率增加，增加了易感者接触病原体的几率，进而促进流感传播。
在平原地区，绵阳市和成都市流感发病以时间自回归成分为主，即主要受到历史发病的影响，这可能是由于这些地区人口密集，当出现流感疫情时容易快速通过人传人的方式流行，因此针对这些地区应及时向公众发布预警消息，如鼓励出门佩戴口罩和服用抗病毒药物等，快速控制疫情传播。德阳市和眉山市则主要以空间流行成分为主，表明其发病主要受周边地区的流行情况影响，因此这些地区需要注意加强周边地区的联防联控，减小跨地区传播风险。在丘陵地区，多数地区受到时间自回归的影响，而内江市则受到空间流行成分的影响较大，因此针对内江市需加强周边地区的流感防控，同时加强其他地区早期预警机制。在高原山地地区，流感病例较少的地区如攀枝花，主要受到空间流行成分的作用，即主要受到周边地区疫情的影响，这可能是由于这些地区地域相对较广，人口密度较低，人与人之间的接触机会相比平原地区更低，病例主要来自周边地区输入，因此这些地区的流感防控更需要周边地区的疫情控制。
本研究也存在一定的局限性。首先，由于数据可及性的问题，研究虽然纳入了多种影响因素，但仍有许多对流感存在影响的因素未纳入考虑中，如人群的免疫水平、医疗卫生资源配置等，这些因素可能影响流感的传播和发病，同时，社会经济因素如GRP，仅能获取年尺度的数据，这可能影响影响模型的准确性及结果的可解释性。
综上所述，本研究通过对不同空间权重设置下的时空多成分模型在不同地形中的情况进行比较，并纳入气象因素和社会经济因素对不同地形的流感时空传播进行分析，量化气象因素和社会经济因素对于不同传播成分的影响，可加深对四川省对于不同地形地区的流感时空传播动态的认知，为流感的预防和控制策略提供了科学依据。
基金
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  • 国家重点研发计划项目(2022YFC3600604)
  • 四川省预防医学会基层公共卫生专项科研项目(SYYXH202403; SYYXHPT202406)
  • 四川省科技计划项目-中央引导地方科技发展项目(2024ZYD0102)
  • 四川省科技计划项目-省院省校合作项目(2025YFHZ0069)
  • 成都市科技局项目(2024-YF05-01784-SN)
文献
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2025年第52卷第22期
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doi: 10.20043/j.cnki.MPM.202505418
  • 接收时间：2025-05-29
  • 首发时间：2026-10-29
  • 出版时间：2025-11-25
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  • 收稿日期：2025-05-29
基金
国家重点研发计划项目(2022YFC3600604)
四川省预防医学会基层公共卫生专项科研项目(SYYXH202403; SYYXHPT202406)
四川省科技计划项目-中央引导地方科技发展项目(2024ZYD0102)
四川省科技计划项目-省院省校合作项目(2025YFHZ0069)
成都市科技局项目(2024-YF05-01784-SN)
作者信息
    1.四川大学华西公共卫生学院/华西第四医院，四川　成都　610041
    2.四川省疾病预防控制中心急性传染病预防控制所

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张韬和袁珩为共同通信作者。张韬，E-mail：；
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total species (%)		 属
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
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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
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