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Article(id=1259565966057398621, tenantId=1146029695717560320, journalId=1259198908832956435, issueId=1259565961842123095, articleNumber=null, orderNo=null, doi=10.16462/j.cnki.zhjbkz.2026.02.002, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1766937600000, receivedDateStr=2025-12-29, revisedDate=1769097600000, revisedDateStr=2026-01-23, acceptedDate=null, acceptedDateStr=null, onlineDate=1778233527912, onlineDateStr=2026-05-08, pubDate=1770652800000, pubDateStr=2026-02-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1778233527912, onlineIssueDateStr=2026-05-08, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1778233527912, creator=13701087609, updateTime=1778233527912, updator=13701087609, issue=Issue{id=1259565961842123095, tenantId=1146029695717560320, journalId=1259198908832956435, year='2026', volume='30', issue='2', pageStart='125', pageEnd='242', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1778233526907, creator=13701087609, updateTime=1778233940379, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1259567696174301345, tenantId=1146029695717560320, journalId=1259198908832956435, issueId=1259565961842123095, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1259567696174301346, tenantId=1146029695717560320, journalId=1259198908832956435, issueId=1259565961842123095, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=131, endPage=142, ext={EN=ArticleExt(id=1259565966749458788, articleId=1259565966057398621, tenantId=1146029695717560320, journalId=1259198908832956435, language=EN, title=The impact of holidays on human mobility and influenza transmission in Kunming City of Yunnan Province, columnId=1259565965784789759, journalTitle=Chinese Journal of Disease Control and Prevention, columnName=Original Articles, runingTitle=null, highlight=null, articleAbstract=
Objective

To evaluate the variation of human mobility patterns in Kunming during holiday and non-holiday periods, and to assess their impact on the transmission of influenza.

Methods

We collected influenza surveillance data in Kunming from July 31, 2023, to August 4, 2024, and mobile phone signaling data from September 1, 2023, to February 29, 2024. We analyzed variations in the influenza composite index, and compared mobility indicators between holiday and non-holiday periods. We also developed an infectious disease transmission model that integrated human mobility using mobile phone signaling data. The differences in the total number of cumulative influenza cases under holiday and non-holiday scenarios to quantify the impact of holidays on influenza transmission.

Results

During the holiday period, the influenza composite index showed a downward trend, followed by an upward trend after the public holidays. The average daily number of travelers and average travel frequency per person during the National Day and Mid-Autumn Festival (48.36/10 000, 0.62 per person), Spring Festival (33.24/10 000, 0.48 per person), and winter vacation(51.87/10 000, 0.69 per person) were lower than those on regular days (all P < 0.05), whereas there was no significant difference on New Year's Day (Z=-0.71, P=0.531). Model simulations indicated that different holidays had varying impacts on the transmission of influenza: the Spring Festival reduces the cumulative number of flu cases by 1 556, while the National Day, Mid-Autumn Festival, New Year's Day, and winter vacation increase the cases by 867, 11, and 1 548, respectively. Additionally, substantial spatial heterogeneity was observed across different administrative districts.

Conclusions

Holidays altered the human mobility patterns and affect the transmission of influenza in Kunming. The impact varied across different types of holidays. In the future, attention should be paid to the interaction between holidays, human mobility, and influenza transmission.

, correspAuthors=null, authorNote=null, correspAuthorsNote=
ZHANG Juanjuan, E-mail:
, copyrightStatement=Copyright © 2026 Chinese Journal of Disease Control & Prevention. 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, authorCompany=null, fund=null, authors=null, authorsList=Yu CHEN, Bowen MING, Yankai LIU, Juanjuan ZHANG), CN=ArticleExt(id=1259565971270918531, articleId=1259565966057398621, tenantId=1146029695717560320, journalId=1259198908832956435, language=CN, title=云南省昆明市假期对人群移动与流行性感冒传播的影响, columnId=1259565966174860033, journalTitle=中华疾病控制杂志, columnName=论著, runingTitle=null, highlight=null, articleAbstract=
目的

评估昆明市假期与非假期期间市内人群移动模式的变化及其对流行性感冒(简称流感)传播的影响。

方法

收集昆明市2023年7月31日―2024年8月4日的流感监测数据与2023年9月1日―2024年2月29日的手机信令数据，分析流感综合指数变化，比较假期与非假期的出行指标差异，并基于手机信令数据构建耦合人群移动的传染病动力学模型，通过比较有假期与无假期情景下累计流感病例数的差异，量化假期对流感传播的影响。

结果

假期期间流感综合指数呈下降趋势，公共假期后呈上升趋势。国庆节与中秋节(48.36万人，0.62人次/人)、春节(33.24万人，0.48人次/人)、寒假(51.87万人，0.69人次/人)期间的日均出行人数与人均出行频次均低于平常日期(均P＜0.05)，元旦与平常则无明显差异(Z=-0.71, P=0.531)。模型结果显示，不同假期对流感传播的影响存在差异：春节使累计流感病例数减少1 556例，而国庆节与中秋节、元旦及寒假则分别增加867例、11例和1 548例。且不同行政区之间存在空间异质性。

结论

假期改变了昆明市市内人群移动模式并影响流感传播，不同类型假期的影响存在差异，未来应关注假期、人群移动与流感传播之间的相互作用。

, correspAuthors=null, authorNote=null, correspAuthorsNote=
张娟娟，E-mail:
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DOI:10.1186/1471-2334-14-480., articleTitle=null, refAbstract=null), Reference(id=1259566000203227857, tenantId=1146029695717560320, journalId=1259198908832956435, articleId=1259565966057398621, doi=null, pmid=null, pmcid=null, year=null, volume=null, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=36, rfOrder=41, authorNames=null, journalName=null, refType=null, unstructuredReference=Tan XH, Yuan LL, Zhou JJ, et al. Modeling the initial transmission dynamics of influenza A H1N1 in Guangdong Province, China[J]. Int J Infect Dis, 2013, 17(7): e479-e484. 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DOI:10.3760/cma.j.issn.0254-6450.2018.08.017., articleTitle=null, refAbstract=null), Reference(id=1259566000798819032, tenantId=1146029695717560320, journalId=1259198908832956435, articleId=1259565966057398621, doi=null, pmid=null, pmcid=null, year=null, volume=null, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=38, rfOrder=44, authorNames=null, journalName=null, refType=null, unstructuredReference=Chen T, Yang J, Wang LJ, et al. The influence of statutory holidays on the proportion of the outpatient and emergency visits for influenza-like illness[J]. Chin J Epidemiol, 2018, 39(8): 1100-1105. DOI:10.3760/cma.j.issn.0254-6450.2018.08.017., articleTitle=null, refAbstract=null), Reference(id=1259566000928842458, tenantId=1146029695717560320, journalId=1259198908832956435, articleId=1259565966057398621, doi=null, pmid=null, pmcid=null, year=null, volume=null, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=39, rfOrder=45, authorNames=null, journalName=null, refType=null, unstructuredReference=宁芳, 段玮, 高培, 等. 流感样病例监测系统数据异常波动与预警分析[J]. 中国公共卫生, 2007, 23(10): 1210-1211. DOI: 10.11847/zgggws2007-23-10-31., articleTitle=null, refAbstract=null), Reference(id=1259566001029505755, tenantId=1146029695717560320, journalId=1259198908832956435, articleId=1259565966057398621, doi=null, pmid=null, pmcid=null, year=null, volume=null, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=39, rfOrder=46, authorNames=null, journalName=null, refType=null, unstructuredReference=Ning F, Duan W, Gao P, et al. Analysis on data deviation of influenza-like case surveillance system and forecasting[J]. Chin J Public Health, 2007, 23(10): 1210-1211. DOI: 10.11847/zgggws2007-23-10-31., articleTitle=null, refAbstract=null)], funds=null, companyList=[AuthorCompany(id=1259565972428546447, tenantId=1146029695717560320, journalId=1259198908832956435, articleId=1259565966057398621, xref=1, ext=[AuthorCompanyExt(id=1259565972487266704, tenantId=1146029695717560320, journalId=1259198908832956435, articleId=1259565966057398621, companyId=1259565972428546447, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai 200032, China), AuthorCompanyExt(id=1259565972504043921, tenantId=1146029695717560320, journalId=1259198908832956435, articleId=1259565966057398621, companyId=1259565972428546447, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1复旦大学上海市重大传染病和生物安全研究院，上海 200032)]), AuthorCompany(id=1259565974114656669, tenantId=1146029695717560320, journalId=1259198908832956435, articleId=1259565966057398621, xref=2, ext=[AuthorCompanyExt(id=1259565974215319969, tenantId=1146029695717560320, journalId=1259198908832956435, articleId=1259565966057398621, companyId=1259565974114656669, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2Key Laboratory of Public Health Safety of Ministry of Education, School of Public Health, Fudan University, Shanghai 200032, China), AuthorCompanyExt(id=1259565974282428835, tenantId=1146029695717560320, journalId=1259198908832956435, articleId=1259565966057398621, companyId=1259565974114656669, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2复旦大学公共卫生学院流行病学教研室，上海 200032)])], figs=[ArticleFig(id=1259565988413039147, tenantId=1146029695717560320, journalId=1259198908832956435, articleId=1259565966057398621, language=EN, label=Figure 1, caption=Characteristics of influenza surveillance data and human mobility data of intra-city, figureFileSmall=Wjh5Ecg4Q7Ss6R8iT5zxWw==, figureFileBig=WqrBcIA5b6q0TBszAgT+Fg==, tableContent=null), ArticleFig(id=1259565988656308787, tenantId=1146029695717560320, journalId=1259198908832956435, articleId=1259565966057398621, language=CN, label=图1, caption=市内的流行性感冒(简称流感)监测数据与人群移动数据特征

A：周ILI/%与周标本阳性率；B：人均出行频次与出行人数；C：流感综合指数与标准化出行人数；ILI：流感样病例。

, figureFileSmall=Wjh5Ecg4Q7Ss6R8iT5zxWw==, figureFileBig=WqrBcIA5b6q0TBszAgT+Fg==, tableContent=null), ArticleFig(id=1259565989243511354, tenantId=1146029695717560320, journalId=1259198908832956435, articleId=1259565966057398621, language=EN, label=Figure 2, caption=Inter-group comparison of mobility indicators of intra-city in different periods, figureFileSmall=7VY/df94KONuPP5cSsRiYw==, figureFileBig=ioKFbeYBXQvVVaaSqFHRHg==, tableContent=null), ArticleFig(id=1259565989574861378, tenantId=1146029695717560320, journalId=1259198908832956435, articleId=1259565966057398621, language=CN, label=图2, caption=市内不同时期人群移动指标的组间比较

A：日均出行人数；B：人均出行频次；C：标准化出行人数；a：P＜0.05；b：P＜0.01；c：P＜0.001。

, figureFileSmall=7VY/df94KONuPP5cSsRiYw==, figureFileBig=ioKFbeYBXQvVVaaSqFHRHg==, tableContent=null), ArticleFig(id=1259565989721662022, tenantId=1146029695717560320, journalId=1259198908832956435, articleId=1259565966057398621, language=EN, label=Figure 3, caption=The coverage rate of Unicom users in Kunming and the mobility networks in different periods, figureFileSmall=42EkBjHar02H+a6hfrsaRQ==, figureFileBig=MjWdOkPcWg5IkrKqTq/U3A==, tableContent=null), ArticleFig(id=1259565989973320266, tenantId=1146029695717560320, journalId=1259198908832956435, articleId=1259565966057398621, language=CN, label=图3, caption=联通用户在昆明市内的覆盖率及不同时期的人群移动网络

A: 联通用户在昆明市的覆盖率; a: 国庆节与中秋节; b: 国庆节与中秋节的对应平常日期; c: 元旦; d: 元旦的对应平常日期; e: 春节; f: 春节的对应平常日期; g: 寒假; h: 寒假的对应平常日期。

, figureFileSmall=42EkBjHar02H+a6hfrsaRQ==, figureFileBig=MjWdOkPcWg5IkrKqTq/U3A==, tableContent=null), ArticleFig(id=1259565990212395601, tenantId=1146029695717560320, journalId=1259198908832956435, articleId=1259565966057398621, language=EN, label=Figure 4, caption=The number of daily new influenza cases estimated by the SEIR model under normal and counterfactual scenarios of different holidays of intra-city, figureFileSmall=ylODbmXc5W+0e8pDR0fy+A==, figureFileBig=j29NhqoN8TKBBmpgkMFhfA==, tableContent=null), ArticleFig(id=1259565990409527890, tenantId=1146029695717560320, journalId=1259198908832956435, articleId=1259565966057398621, language=CN, label=图4, caption=不同假期的正常情景与反事实情景下SEIR模型估计的市内每日新增流行性感冒(简称流感)病例数

A: a, 国庆节与中秋节; B: b, 元旦; C: c, 春节; D: d, 寒假。

, figureFileSmall=ylODbmXc5W+0e8pDR0fy+A==, figureFileBig=j29NhqoN8TKBBmpgkMFhfA==, tableContent=null), ArticleFig(id=1259565990510191190, tenantId=1146029695717560320, journalId=1259198908832956435, articleId=1259565966057398621, language=EN, label=Table 1, caption=

Settings of mobile data in counterfactual scenario

, figureFileSmall=null, figureFileBig=null, tableContent=
假期Holiday被替换的真实数据时间段
Time period of replaced
real data		替换的模拟数据时间段
Time period of replacement simulated data		敏感性分析(第二临近)
Sensitivity analysis (second-nearest)		敏感性分析(第三临近)
Sensitivity analysis (third-nearest)
公共假期Public holiday
  国庆节与中秋节National Day and Mid-Autumn Festival2023/09/29―2023/10/062023/09/15―2023/09/222023/09/08―2023/09/152023/09/01―2023/09/08
  元旦New Year's Day2023/12/30―2024/01/012023/12/16―2023/12/182023/12/09―2023/12/112023/12/02―2023/12/04
  春节Spring Festival2024/02/10―2024/02/172024/01/27―2024/02/032024/01/20―2024/01/272024/01/13―2024/01/20
学校假期School holiday
  寒假Winter vacation2024/01/14―2024/02/252024/01/07―2024/01/132023/12/17―2023/12/232023/12/10―2023/12/16
), ArticleFig(id=1259565990686351961, tenantId=1146029695717560320, journalId=1259198908832956435, articleId=1259565966057398621, language=CN, label=表1, caption=

反事实情境中移动数据的设置

, figureFileSmall=null, figureFileBig=null, tableContent=
假期Holiday被替换的真实数据时间段
Time period of replaced
real data		替换的模拟数据时间段
Time period of replacement simulated data		敏感性分析(第二临近)
Sensitivity analysis (second-nearest)		敏感性分析(第三临近)
Sensitivity analysis (third-nearest)
公共假期Public holiday
  国庆节与中秋节National Day and Mid-Autumn Festival2023/09/29―2023/10/062023/09/15―2023/09/222023/09/08―2023/09/152023/09/01―2023/09/08
  元旦New Year's Day2023/12/30―2024/01/012023/12/16―2023/12/182023/12/09―2023/12/112023/12/02―2023/12/04
  春节Spring Festival2024/02/10―2024/02/172024/01/27―2024/02/032024/01/20―2024/01/272024/01/13―2024/01/20
学校假期School holiday
  寒假Winter vacation2024/01/14―2024/02/252024/01/07―2024/01/132023/12/17―2023/12/232023/12/10―2023/12/16
), ArticleFig(id=1259565990858318431, tenantId=1146029695717560320, journalId=1259198908832956435, articleId=1259565966057398621, language=EN, label=Table 2, caption=

Mean values (95% CI) of mobility indicators of intra-city in different periods

, figureFileSmall=null, figureFileBig=null, tableContent=
假期Holiday日均出行人数Average daily number of travelers/10 000
假期日Holiday平常日期Regular dayZ值valueP值value
公共假期Public holiday
  国庆节与中秋节National Day and Mid-Autumn Festival48.36 (44.66~52.05)61.55 (60.72~62.38)-4.64< 0.001
  元旦New Year's Day54.84 (44.77~64.92)58.63 (57.55~59.70)-0.970.410
  春节Spring Festival33.24 (29.38~37.09)47.63 (45.60~49.66)-2.750.011
学校假期School holiday
  寒假Winter vacation51.87 (49.74~54.00)57.76 (55.20~60.32)-2.540.016

假期Holiday		人均出行频次Average travel frequency per person
假期日Holiday平常日期Regular dayZ值valueP值value
公共假期Public holiday
  国庆节与中秋节National Day and Mid-Autumn Festival0.62 (0.57~0.67)0.79 (0.77~0.79)-4.89< 0.001
  元旦New Year's Day0.63 (0.39~0.87)0.69 (0.67~0.70)-0.710.530
  春节Spring Festival0.48 (0.43~0.54)0.69 (0.66~0.72)-2.680.013
学校假期School holiday
  寒假Winter vacation0.69 (0.67~0.71)0.75 (0.73~0.76)-2.060.040

假期Holiday		标准化出行人数Standardized number of travelers/10 000
假期日Holiday平常日期Regular dayZ值valueP值value
公共假期Public holiday
  国庆节与中秋节National Day and Mid-Autumn Festival-9.06 (-12.76~-5.36)4.13 (3.30~4.96)-4.64< 0.001
  元旦New Year's Day-2.57 (-12.65~7.51)1.21 (0.14~2.28)-0.970.410
  春节Spring Festival-24.18 (-28.04~-20.32)-9.78 (-11.81~-7.75)-2.750.011
学校假期School holiday
  寒假Winter vacation-5.55 (-7.68~-3.42)0.34 (-2.22~2.90)-2.540.016
), ArticleFig(id=1259565991151919715, tenantId=1146029695717560320, journalId=1259198908832956435, articleId=1259565966057398621, language=CN, label=表2, caption=

市内不同时期人群移动指标的均数(95% CI)

, figureFileSmall=null, figureFileBig=null, tableContent=
假期Holiday日均出行人数Average daily number of travelers/10 000
假期日Holiday平常日期Regular dayZ值valueP值value
公共假期Public holiday
  国庆节与中秋节National Day and Mid-Autumn Festival48.36 (44.66~52.05)61.55 (60.72~62.38)-4.64< 0.001
  元旦New Year's Day54.84 (44.77~64.92)58.63 (57.55~59.70)-0.970.410
  春节Spring Festival33.24 (29.38~37.09)47.63 (45.60~49.66)-2.750.011
学校假期School holiday
  寒假Winter vacation51.87 (49.74~54.00)57.76 (55.20~60.32)-2.540.016

假期Holiday		人均出行频次Average travel frequency per person
假期日Holiday平常日期Regular dayZ值valueP值value
公共假期Public holiday
  国庆节与中秋节National Day and Mid-Autumn Festival0.62 (0.57~0.67)0.79 (0.77~0.79)-4.89< 0.001
  元旦New Year's Day0.63 (0.39~0.87)0.69 (0.67~0.70)-0.710.530
  春节Spring Festival0.48 (0.43~0.54)0.69 (0.66~0.72)-2.680.013
学校假期School holiday
  寒假Winter vacation0.69 (0.67~0.71)0.75 (0.73~0.76)-2.060.040

假期Holiday		标准化出行人数Standardized number of travelers/10 000
假期日Holiday平常日期Regular dayZ值valueP值value
公共假期Public holiday
  国庆节与中秋节National Day and Mid-Autumn Festival-9.06 (-12.76~-5.36)4.13 (3.30~4.96)-4.64< 0.001
  元旦New Year's Day-2.57 (-12.65~7.51)1.21 (0.14~2.28)-0.970.410
  春节Spring Festival-24.18 (-28.04~-20.32)-9.78 (-11.81~-7.75)-2.750.011
学校假期School holiday
  寒假Winter vacation-5.55 (-7.68~-3.42)0.34 (-2.22~2.90)-2.540.016
), ArticleFig(id=1259565991466492521, tenantId=1146029695717560320, journalId=1259198908832956435, articleId=1259565966057398621, language=EN, label=Table 3, caption=

Under the scenarios with holidays (normal) and without holidays (counterfactual): the total number of cumulative influenza cases in each administrative district estimated by the SEIR model, and the ratio of the cumulative number of travelers traveling within the administrative district to that between administrative districts

, figureFileSmall=null, figureFileBig=null, tableContent=
累计病例数与累计人数比值
Ratio of cumulative cases to cumulative population		国庆节与中秋节
National Day and Mid-Autumn Festival		元旦
New Year's Day
有假期
Have holiday		无假期
No holiday		差值
Difference		有假期
Have holiday		无假期
No holiday		差值
Difference
  总累计病例数Total cumulative cases225 136224 269867225 136225 12511
行政区累计病例数Cumulative cases by administrative region
  五华区Wuhua District33 49833 37212633 49833 4971
  盘龙区Panlong District30 14430 03111330 14430 1431
  官渡区Guandu District54 35254 15020254 35254 3493
  西山区Xishan District31 37631 25911731 37631 3751
  东川区Dongchuan District3 3913 379123 3913 3910
  呈贡区Chenggong District23 00822 9238523 00823 0071
  晋宁区Jinning District5 4175 395225 4175 4170
  富民县Fumin County2 5102 497132 5102 5100
  宜良县Yiliang County7 3397 310297 3397 3381
  石林彝族自治县Shilin Yi Autonomous County5 2005 179215 2005 1982
  嵩明县Songming County7 2257 195307 2257 2250
  禄劝彝族苗族自治县Luquan Yi and Miao Autonomous County4 3924 369234 3924 3920
  寻甸回族彝族自治县Xundian Hui and Yi Autonomous County4 7274 702254 7274 7270
  安宁市Anning City12 55712 5084912 55712 5561
行政区内与行政区间出行的累计人数比值
The ratio of cumulative travel within and between administrative regions		1.31.270.031.321.320

累计病例数与累计人数比值
Ratio of cumulative cases to cumulative population		春节Spring Festival寒假Winter vacation
有假期
Have holiday		无假期
No holiday		差值
Difference		有假期
Have holiday		无假期
No holiday		差值
Difference
总累计病例数Total cumulative cases225 136226 692-1 556225 136223 5881 548
行政区累计病例数Cumulative cases by administrative region
  五华区Wuhua District33 49833 17832033 49833 398100
  盘龙区Panlong District30 14429 87726730 14430 033111
  官渡区Guandu District54 35253 90045254 35254 28270
  西山区Xishan District31 37631 06730931 37631 32551
  东川区Dongchuan District3 3913 970-5793 3913 521-130
  呈贡区Chenggong District23 00822 83117723 00822 786222
  晋宁区Jinning District5 4175 390275 4175 294123
  富民县Fumin County2 5102 553-432 5102 519-9
  宜良县Yiliang County7 3397 696-3577 3397 528-189
  石林彝族自治县Shilin Yi Autonomous County5 2005 260-605 2005 262-62
  嵩明县Songming County7 2257 618-3937 2256 0561 169
  禄劝彝族苗族自治县Luquan Yi and Miao Autonomous County4 3925 013-6214 3924 395-3
  寻甸回族彝族自治县Xundian Hui and Yi Autonomous County4 7275 880-1 1534 7274 885-158
  安宁市Anning City12 55712 4599812 55712 304253
行政区内与行政区间出行的累计人数比值
The ratio of cumulative travel within and between administrative regions		1.421.380.041.411.370.04
), ArticleFig(id=1259565991718150768, tenantId=1146029695717560320, journalId=1259198908832956435, articleId=1259565966057398621, language=CN, label=表3, caption=

有假期(正常)与无假期(反事实)情景下：SEIR模型估计的各行政区累计流行性感冒病例数(例)、行政区内与行政区间出行的累计人数的比值

, figureFileSmall=null, figureFileBig=null, tableContent=
累计病例数与累计人数比值
Ratio of cumulative cases to cumulative population		国庆节与中秋节
National Day and Mid-Autumn Festival		元旦
New Year's Day
有假期
Have holiday		无假期
No holiday		差值
Difference		有假期
Have holiday		无假期
No holiday		差值
Difference
  总累计病例数Total cumulative cases225 136224 269867225 136225 12511
行政区累计病例数Cumulative cases by administrative region
  五华区Wuhua District33 49833 37212633 49833 4971
  盘龙区Panlong District30 14430 03111330 14430 1431
  官渡区Guandu District54 35254 15020254 35254 3493
  西山区Xishan District31 37631 25911731 37631 3751
  东川区Dongchuan District3 3913 379123 3913 3910
  呈贡区Chenggong District23 00822 9238523 00823 0071
  晋宁区Jinning District5 4175 395225 4175 4170
  富民县Fumin County2 5102 497132 5102 5100
  宜良县Yiliang County7 3397 310297 3397 3381
  石林彝族自治县Shilin Yi Autonomous County5 2005 179215 2005 1982
  嵩明县Songming County7 2257 195307 2257 2250
  禄劝彝族苗族自治县Luquan Yi and Miao Autonomous County4 3924 369234 3924 3920
  寻甸回族彝族自治县Xundian Hui and Yi Autonomous County4 7274 702254 7274 7270
  安宁市Anning City12 55712 5084912 55712 5561
行政区内与行政区间出行的累计人数比值
The ratio of cumulative travel within and between administrative regions		1.31.270.031.321.320

累计病例数与累计人数比值
Ratio of cumulative cases to cumulative population		春节Spring Festival寒假Winter vacation
有假期
Have holiday		无假期
No holiday		差值
Difference		有假期
Have holiday		无假期
No holiday		差值
Difference
总累计病例数Total cumulative cases225 136226 692-1 556225 136223 5881 548
行政区累计病例数Cumulative cases by administrative region
  五华区Wuhua District33 49833 17832033 49833 398100
  盘龙区Panlong District30 14429 87726730 14430 033111
  官渡区Guandu District54 35253 90045254 35254 28270
  西山区Xishan District31 37631 06730931 37631 32551
  东川区Dongchuan District3 3913 970-5793 3913 521-130
  呈贡区Chenggong District23 00822 83117723 00822 786222
  晋宁区Jinning District5 4175 390275 4175 294123
  富民县Fumin County2 5102 553-432 5102 519-9
  宜良县Yiliang County7 3397 696-3577 3397 528-189
  石林彝族自治县Shilin Yi Autonomous County5 2005 260-605 2005 262-62
  嵩明县Songming County7 2257 618-3937 2256 0561 169
  禄劝彝族苗族自治县Luquan Yi and Miao Autonomous County4 3925 013-6214 3924 395-3
  寻甸回族彝族自治县Xundian Hui and Yi Autonomous County4 7275 880-1 1534 7274 885-158
  安宁市Anning City12 55712 4599812 55712 304253
行政区内与行政区间出行的累计人数比值
The ratio of cumulative travel within and between administrative regions		1.421.380.041.411.370.04
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云南省昆明市假期对人群移动与流行性感冒传播的影响
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陈昱 1 , 明博文 2 , 刘彦恺 2 , 张娟娟 1, 2
中华疾病控制杂志 | 论著 2026,30(2): 131-142
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中华疾病控制杂志 | 论著 2026, 30(2): 131-142
云南省昆明市假期对人群移动与流行性感冒传播的影响
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陈昱1, 明博文2, 刘彦恺2, 张娟娟1, 2
作者信息
  • 1复旦大学上海市重大传染病和生物安全研究院，上海 200032
  • 2复旦大学公共卫生学院流行病学教研室，上海 200032

通讯作者:

张娟娟，E-mail:
The impact of holidays on human mobility and influenza transmission in Kunming City of Yunnan Province
Yu CHEN1, Bowen MING2, Yankai LIU2, Juanjuan ZHANG1, 2
Affiliations
  • 1Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai 200032, China
  • 2Key Laboratory of Public Health Safety of Ministry of Education, School of Public Health, Fudan University, Shanghai 200032, China
出版时间: 2026-02-10 doi: 10.16462/j.cnki.zhjbkz.2026.02.002
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摘要
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目的

评估昆明市假期与非假期期间市内人群移动模式的变化及其对流行性感冒(简称流感)传播的影响。

方法

收集昆明市2023年7月31日―2024年8月4日的流感监测数据与2023年9月1日―2024年2月29日的手机信令数据，分析流感综合指数变化，比较假期与非假期的出行指标差异，并基于手机信令数据构建耦合人群移动的传染病动力学模型，通过比较有假期与无假期情景下累计流感病例数的差异，量化假期对流感传播的影响。

结果

假期期间流感综合指数呈下降趋势，公共假期后呈上升趋势。国庆节与中秋节(48.36万人，0.62人次/人)、春节(33.24万人，0.48人次/人)、寒假(51.87万人，0.69人次/人)期间的日均出行人数与人均出行频次均低于平常日期(均P＜0.05)，元旦与平常则无明显差异(Z=-0.71, P=0.531)。模型结果显示，不同假期对流感传播的影响存在差异：春节使累计流感病例数减少1 556例，而国庆节与中秋节、元旦及寒假则分别增加867例、11例和1 548例。且不同行政区之间存在空间异质性。

结论

假期改变了昆明市市内人群移动模式并影响流感传播，不同类型假期的影响存在差异，未来应关注假期、人群移动与流感传播之间的相互作用。

人群移动  /  手机信令  /  流行性感冒  /  假期  /  传染病建模
Objective

To evaluate the variation of human mobility patterns in Kunming during holiday and non-holiday periods, and to assess their impact on the transmission of influenza.

Methods

We collected influenza surveillance data in Kunming from July 31, 2023, to August 4, 2024, and mobile phone signaling data from September 1, 2023, to February 29, 2024. We analyzed variations in the influenza composite index, and compared mobility indicators between holiday and non-holiday periods. We also developed an infectious disease transmission model that integrated human mobility using mobile phone signaling data. The differences in the total number of cumulative influenza cases under holiday and non-holiday scenarios to quantify the impact of holidays on influenza transmission.

Results

During the holiday period, the influenza composite index showed a downward trend, followed by an upward trend after the public holidays. The average daily number of travelers and average travel frequency per person during the National Day and Mid-Autumn Festival (48.36/10 000, 0.62 per person), Spring Festival (33.24/10 000, 0.48 per person), and winter vacation(51.87/10 000, 0.69 per person) were lower than those on regular days (all P < 0.05), whereas there was no significant difference on New Year's Day (Z=-0.71, P=0.531). Model simulations indicated that different holidays had varying impacts on the transmission of influenza: the Spring Festival reduces the cumulative number of flu cases by 1 556, while the National Day, Mid-Autumn Festival, New Year's Day, and winter vacation increase the cases by 867, 11, and 1 548, respectively. Additionally, substantial spatial heterogeneity was observed across different administrative districts.

Conclusions

Holidays altered the human mobility patterns and affect the transmission of influenza in Kunming. The impact varied across different types of holidays. In the future, attention should be paid to the interaction between holidays, human mobility, and influenza transmission.

Human mobility  /  Mobile phone signaling data  /  Influenza  /  Holiday  /  Infectious disease model
陈昱, 明博文, 刘彦恺, 张娟娟. 云南省昆明市假期对人群移动与流行性感冒传播的影响. 中华疾病控制杂志, 2026 , 30 (2) : 131 -142 . DOI: 10.16462/j.cnki.zhjbkz.2026.02.002
Yu CHEN, Bowen MING, Yankai LIU, Juanjuan ZHANG. The impact of holidays on human mobility and influenza transmission in Kunming City of Yunnan Province[J]. Chinese Journal of Disease Control and Prevention, 2026 , 30 (2) : 131 -142 . DOI: 10.16462/j.cnki.zhjbkz.2026.02.002
正文
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流行性感冒(简称流感)是一种由流感病毒引起的急性呼吸道传染病，其流行对社会造成严重的疾病负担[1]。人群移动定义为个体从出发地至目的地的空间轨迹。既往研究指出[2-4]，人口流动性的增强与疾病流行期提前、流行高峰提早形成、病例数增多及传播风险上升呈正相关。假期期间，跨区域(如跨市、跨省或跨国)的出行人数、出行频次和移动距离均有所增加[5-8]，而市域与省内的局部流动则趋于减少[9-11]。此外，已有研究显示，公共假期可能伴随流感病例的增加[12-13]，而学校假期则有助于降低儿童流感病例数和流感有效再生数[14-15]。
既往研究主要使用航空数据、城市公共交通记录、全球定位系统(global positioning system, GPS)、问卷调查及手机信令数据等方式探索人群移动与传染病传播的关系[16-20]。不同数据源在研究应用中各有侧重与优势：例如，航空与城市公共交通数据可刻画特定交通网络内的人群流动模式，GPS与问卷调查则更适用于追踪特定研究对象的出行轨迹。然而，这些数据也因其采集方式存在一定的局限：样本代表性不足、连续性有限与覆盖范围受限等。例如，航空与城市公共交通数据仅反映使用该类交通工具出行的人群，且受地区间交通基建差异的影响；基于GPS的数据收集均依赖于设备开启定位功能；问卷调查则易受回忆偏倚的干扰。而手机信令数据因手机普及率高、数据获取仅需手机处于开机状态且不受用户主观行为干扰等特点，具有更好的代表性和准确性。在假期对人群移动与流感传播影响的研究方面，既往研究多聚焦于跨市、跨省或跨国尺度的长距离移动[21-23]，结果显示长距离移动促进了传染病在更广阔空间范围内传播。然而，人群通常以短距离、高频率的日常移动为主，长距离出行则相对较少[24-25]，这表明较小地理尺度内的频繁人群移动行为可能会成为驱动流感传播的关键因素。但目前少有研究聚焦市内短距离移动对流感传播的影响。为此，本研究基于高精度手机信令数据，评估假期期间昆明市人群移动模式的变化，并进一步探究其对当地流感传播的影响。
1　数据与方法
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1.1　数据来源
收集云南省昆明市2023年7月31日―2024年8月4日的流感监测数据与2023年9月1日―2024年2月29日的手机信令数据。收集的手机信令数据时段覆盖了昆明市流感流行的高峰期[26]。
流感监测数据来源于国家流感监测信息系统[26]，手机信令数据由我国主要电信运营商之一的中国联通(http://www.smartsteps.com/)提供。中国联通在云南省通信运营商市场的份额为17%，用户总数达850万。手机信令数据主要通过2种方式获取：一是主动信令，记录用户在通话、收发短信等主动通信行为时的时空信息；二是被动信令，即使用户未主动通信，只要手机处于开机状态，基站会以约30 min的时间间隔自动记录其时空信息。
1.2　数据处理
本研究使用的手机信令数据由联通公司事先进行清洗，已剔除信令记录过少、移动速度不合理等无效用户，保障了数据输出的质量与适用性。本研究进一步对出行人数、出行频次、平均每人出行停留时长等人群移动指标进行复核，无异常值需剔除。
本研究对手机信令数据进行了预处理。首先，将昆明市划分为999 427个150 m×150 m的网格单元，并对每个网格赋予唯一编号。基于处理后的数据，构建每日的“出发地-目的地(origin-destination, O-D)”矩阵，进而获取全市范围内每日从一个网格移动至另一个网格的人数与次数。其次，对昆明市网格内联通用户居住情况进行统计，筛选出在市内连续停留超过10 d的联通用户，分析每日晚间9点至次日早晨8点间用户在各网格内的停留时长。根据停留时长的累加结果进行排序，将累计停留时长最长的网格判定为该用户的居住网格，据此统计每个网格内的联通用户居住数。
本研究采用的流感监测数据与手机信令数据均不包含个人隐私信息。手机信令数据已由中国联通进行匿名化处理并在地理网格水平进行汇总，确保数据完全无法关联到任何具体个人。
1.3　研究指标
1.3.1　流感流行指标
本研究计算了流感样病例(influenza-like illness, ILI)数、ILI占门(急)诊病例的百分比(ILI%)、ILI标本阳性率及流感综合指数。流感监测数据按周进行汇总(以周日为每周终点)。其中，ILI定义为发热(体温≥38 ℃)，并伴有咳嗽或咽痛至少一种症状的患者[27]。ILI%为门(急)诊ILI总数/周占该周门急诊就诊病例周总数的比例。标本阳性率则为阳性标总数/周占送检标本周总数的比例。流感综合指数的计算采用综合评分法[28]，以昆明市非流感流行高峰季(2023年7月31日―2023年10月29日、2024年3月4日―2024年8月4日)的周ILI%、周标本阳性率的第75百分位数分别作为相应指标的标准值(分别为$ \bar{a}$、$ \bar{b}$)[29-30]，计算流感流行高峰季的周ILI/%、周标本阳性率的相对标准化值(分别为X1、X2)。流感综合指数(Y)的计算公式如下(权重均取0.5)[30]：
$\left\{\begin{array}{c}X_1=\frac{a}{\bar{a}} \\X_2=\frac{b}{\bar{b}} \\Y=0.5 X_1+0.5 X_2\end{array}\right.$
本研究流感的研究时间为2023年9月1日―2024年2月29日。
1.3.2　人群移动指标
对市内出行及其相关指标进行了如下定义与计算：首先，将一次市内出行定义为用户从一个网格移动至另一个网格，并在目标网格停留时间≥30 min；其次，定义并计算出行人数与出行频次。出行人数Uij(t)表示时间t，从网格i出发至网格j的用户数量。总出行人数U(t)为不同网格间出行人数的累计总和$ \left[\sum_{i \neq j} U_{i j}(t)\right]$。出行频次Tij(t)为在时间t，从网格i出发至网格j的出行次数。人均出行频次$ \bar{T}_{i j}(t)$为在时间t，从网格i出发至网格j的出行次数除以网格i的联通用户居住数；最后，对出行人数进行标准化，以研究期间日均出行人数为基准，将每日实际出行人数减去该值，得到每日标准化出行人数，正值表示出行人数相对增加，负值表示相对减少。
本研究计算了中国联通在昆明市各行政区的用户覆盖率。T为总月数。Pi(t)为在月份t，网格i的联通用户居住数。Sk为行政区k所包含的网格集合。$ \sum_{i \in S_k} P_i(t)$为行政区k在月份t的联通用户总居住数。Qk为行政区k的月均联通用户居住数。行政区k的联通用户覆盖率为Qk占第七次人口普查中行政区k常住人口数[31]的比例。
$Q_k=\frac{1}{T} \sum\limits_{t=1}^T\left(\sum\limits_{i \in S_k} P_i(t)\right)$
本研究中上述人群移动的研究时间均为2023年9月1日―2024年2月29日。
1.3.3　假期安排
采用的假期时间来源于中华人民共和国中央人民政府与云南省教育厅发布的官方公告，大学放假安排则参考云南大学的相关公告[32-34]。本研究将公共假期和学校假期统一归为假期，并将各假期所在月份中除假期外的其他日期定义为平常日期。具体假期时间范围如下：国庆节与中秋节为2023年9月29日―10月6日，元旦为2023年12月30日―2024年1月1日，春节为2024年2月10日―2月17日，寒假为2024年1月14日―2月25日(不含春节假期)。
1.4　统计学方法
采用R 4.2.2软件进行数据处理与分析。因进行统计分析的手机信令数据样本量大于50(共182 d)，可以近似为正态分布。本研究使用Bartlett检验进行方差齐性检验，发现人均出行频次(P=0.008)和标准化出行人数(P=0.010)不满足方差齐性，故对于人群移动指标的组间差异，统一采用Kruskal-Wallis检验进行整体比较，并在存在显著差异时进一步采用Dunn检验进行两两比较。采用Benjamini-Hochberg方法校正以控制多重比较带来的一类错误膨胀。检验水准α=0.05。
1.5　基于人群市内移动的流感动力学模型
本研究使用SEIR(susceptible-exposed-infectious-recovered)动力学模型，结合手机信令数据，评估假期对人群移动和流感传播的影响，具体模型结构如公式3所示：
$\left\{\begin{array}{l}\frac{d S(t)}{d t}=-\lambda(t) S(t) \\\frac{d E(t)}{d t}=\lambda(t) S(t)-f E(t) \\\frac{d I(t)}{d t}=f E(t)-r I(t) \\\frac{d R(t)}{d t}=r I(t) \\\lambda(t)=\frac{C_e I(t)}{N} \\C_e=\frac{R_0}{D} \\N=S(t)+E(t)+I(t)+R(t)\end{array}\right.$
式(3)中，t代表单位时间，S(t)代表易感人数，E(t)代表潜伏期人数，I(t)代表感染人数，R(t)代表康复人数，N代表总人数，λ(t)代表易感个体被感染的风险，Ce代表单位时间内人均有效接触人数，R0代表流感的基本再生数(本研究取1.3[35-36])，D代表流感的平均传染期(7 d[37])，f代表潜伏期个体转变为感染个体的概率(即流感平均潜伏期的倒数，平均潜伏期为2 d[37])，r代表感染个体转变为康复个体的概率(即流感平均传染期的倒数$ \frac{1}{7}$)。
为了纳入人群移动过程，本研究基于手机信令数据构建行政区间的每日移动强度矩阵。移动强度Mij(t)定义为从行政区i至行政区j的日移动人数占i区该月联通用户居住数的比例，反映单位人口从i区至j区的流动概率。当i区感染者数Ii(t)>0时，计算从i区流向j区的感染人数Ii(t)×Mij(t)。通过对所有行政区进行上述计算，可得到每日各行政区的感染人口流入与流出情况，进而在SEIR框架中耦合人群移动与流感传播过程。
为评估假期的影响，本研究将实际移动数据与反事实移动数据的模拟结果进行对比。本研究SEIR模型的模拟时间为2023年9月1日―2024年2月29日。在反事实模型中，保持真实数据的总体不变，仅将公共假期或学校假期时段的移动数据替换为对应非假期基准数据，见表 1。非假期基准数据的选取标准如下：首先，该数据位于目标假期之前，且与假期起始日期间隔最短；其次，所选数据的天数需与假期数据的天数完全一致；最后，其工作日或周末的类型必须与被替换的假期数据相匹配。由于寒假时间跨度较长，为更合理构建反事实情景，本研究采用逐周循环替换法，将寒假期间的各周数据均替换为2024年1月7日―13日的对应数据，替换过程中，春节期间的数据保持不变。通过比较替换前后模型模拟所得的总模拟时间内新增病例数/d与累计病例数差异，定量评估假期对昆明市流感传播的影响。为评估基准期选取对模型结果的潜在影响，本研究进行了敏感性分析。将基准数据替换为距目标假期第二临近与第三临近的非假期数据，并重复模型模拟以检验结果的稳健性。
2　结果
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2.1　流感数据特征
2023年9月1日―2024年2月29日，昆明市累计报告69 592例ILI。2023年10月，周ILI/%达到峰值(11.29%)，2023年12月，周ILI标本阳性率/%达到峰值(58.12%)，见图 1A 。流感综合指数于2024年1月达到峰值(3.47)，公共假期和学校假期期间主要呈下降趋势，公共假期后呈上升趋势，见图 1C。
2.2　人群移动数据的特征
2023年9月1日―2024年2月29日，昆明市总体日均出行人数57.42万人(95% CI：56.22万人~58.61万人)，人均出行频次0.73人次/人(95% CI：0.71人次/人~0.74人次/人)。各假期期间的出行规模均较相应月份的平常日期有所下降，其中国庆节与中秋节、春节及寒假的日均出行人数与人均出行频次均低于对应平常日期(均P < 0.05)，而元旦的差异无统计学意义，见图 2A~B、图 3、表 2。具体来看，春节期间出行规模最低，日均出行人数为33.24万人(95% CI：29.38万人~37.09万人)，低于对应平常日期的47.63万人(95% CI：45.60万人~49.66万人)；国庆节与中秋节、寒假的日均出行人数与人均出行频次亦减少。元旦期间出行人数为54.84万人(95% CI：44.77万人~64.92万人)，与对应平常日期的58.63万人(95% CI：57.55万人~59.70万人)相比未呈现显著差异。
2.3　假期与非假期的人群移动模式与流感综合指数的比较
本研究进一步分析了市内标准化出行人数与流感综合指数之间的关系。在平常日期，2023年9―12月出行人数增加的移动模式与流感综合指数的上升趋势同步出现，而在2024年1―2月，出行减少的移动模式与流感综合指数的下降趋势基本一致，见图 1C。公共假期期间，人群移动模式呈现与平常日期不同的特征：市内出行人数减少，国庆节与中秋节、元旦、春节的标准化出行人数分别减少9.06万人(95% CI：5.36万人~12.76万人)、2.57万人(95% CI：-12.65万人~7.51万人)、24.18万人(95% CI：20.32万人~28.04万人)，见图 1C、图 2C、表 2。与此对应的流感综合指数在公共假期期间主要呈现下降趋势，在公共假期后呈现上升趋势，见图 1C。学校假期(寒假)期间，人群移动模式同样以市内出行人数减少为主，流感综合指数亦主要呈下降趋势，见图 1C。
2.4　不同情景下的市内流感传播SEIR模型模拟结果
模拟结果表明，假期对昆明市流感传播的影响存在空间异质性。整体上，春节使累计流感病例数减少1 556例，而国庆节与中秋节、元旦及寒假则分别增加867例、11例和1 548例，见表 3、图 4。仅考虑各假期期间，累计流感病例数的变化趋势与整体相同，即春节可减少累计流感病例数，而国庆与中秋节、元旦及寒假则可使其增加，见图 4。敏感性分析结果稳健，选择第二临近与第三临近的非假期数据进行检验，累计流感病例数的变化趋势与主分析保持一致，具体数值存在差异：与主分析相比，国庆与中秋节的差值分别为77例与15例、元旦为3例与0例、春节为175例与76例、寒假为367例与514例。从空间分布来看，春节和寒假对累计流感病例数的影响最明显：春节使寻甸回族彝族自治县(-1 153例)、禄劝彝族苗族自治县(-621例)、东川区(-579例)等地的病例数降低，但使官渡区(452例)、五华区(320例)、西山区(309例)等地的病例数增加；寒假使嵩明县(+1 169例)、安宁市(+253例)、呈贡区(+222例)等地的病例数增加，但使宜良县(-189例)、寻甸回族彝族自治县(-158例)、东川区(-130例)等地的病例数降低；国庆节与中秋节对累计流感病例数的影响次之，主要表现为官渡区(+202例)、五华区(+126例)、西山区(+117例)等地的病例数增加；元旦的影响整体较弱，仅个别区域如官渡区(+3例)的病例数出现增加。
进一步分析行政区内与行政区间出行的累计人数比值，结果显示国庆节与中秋节、春节、寒假期间的比值均有所上升，但元旦期间的比值未见较大变化，见表 3。
3　讨论
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本研究对比了假期与非假期的市内人群移动模式，并基于动力学模型，探索假期移动模式的变化对流感在城市内传播的影响。本研究发现假期期间市内出行人数减少；动力学模型结果显示，假期移动模式的改变对流感传播产生了影响，不同类型假期的影响存在差异，且假期对流感传播的影响具有空间异质性。
本研究发现假期期间市内人群出行规模下降，与既往研究结果一致[9-11]。其原因可能在于：平日以通勤为主的人群在假期中更倾向于居家休息，减少了外出活动。国庆节与中秋节、春节及寒假的日均出行人数和人均出行频次均低于对应的平常日期，而元旦的差异无统计学意义，这可能表明假期时长会影响出行模式的变化强度，较长的假期对市内流动的抑制作用更明显。标准化出行人数与流感综合指数的变化趋势表现出月度同步性，这一现象可能与不同阶段出行模式的变化有关：出行人数从2023年9―11月的相对增加，过渡为12月―次年1月的增减交替模式，至2024年2月转变为普遍减少，流感综合指数亦自12月起整体呈现下降态势，见图 1C。这提示人群移动变化与流感传播动态之间在时间上可能存在耦合关系。
模型模拟结果显示，不同假期对流感传播的影响存在差异：春节使累计流感病例数下降，而国庆节与中秋节、元旦、寒假使累计流感病例数上升，这种趋势在整体模拟与假期内部中保持一致。这种差异可能与各假期的出行特征相关。国庆节与中秋节、寒假期间，尽管行政区间的出行人数较非假期时段有所下降，但行政区内与行政区间出行人数的比值却上升，反映出感染者的活动更多集中在行政区内部，易形成局部聚集。例如，国庆假期(在某些年份与中秋节相邻，形成连休)常被称为“十一黄金周”，是旅游活动的高峰期，这一现象客观上加剧了人群在旅游景区等特定场所的空间聚集程度；即使整体流动性降低，这种聚集性仍可能提高流感在区域内的传播风险，从而促使累计流感病例数增加，见表 3。具体来看，对于国庆节与中秋节、元旦等短假而言，人们可能更倾向于在交通便利的中心城区内部活动，而寒假主要面向学生群体，移动特点可能更倾向于从城市返回家乡。相比之下，春节期间不仅行政区之间的出行人数降至最低，整体的流动性也处于研究期内的最低水平，这一现象的产生受到多因素影响，例如春节期间多数公众的活动范围通常局限在家附近，且商业活动也大多暂停，一般需至农历正月初五后才陆续恢复营业。这种大幅度的活动收缩可能限制了流感的扩散，缩小其传播范围，使累计流感病例数降低，见图 2~3、表 3。上述结果进一步提示不同类型假期对流感传播的影响可能不同。
本研究的创新点主要体现在以下3个方面：(1)系统比较不同类型的假期对市内流感传播的影响，揭示了假期移动模式差异与流感传播动态之间的关联。(2)在公共假期研究方面，区别于既往多关注跨市或跨国长距离出行[12-13]，本研究聚焦于更为普遍和频繁的市域内短距离移动，揭示了此类日常流动性变化对流感传播的潜在作用。(3)在学校假期研究方面，既往研究多从接触模式变化的角度分析学校假期对流感传播的抑制作用[14-15]，本研究则从人群移动模式的角度出发，探讨了学校假期期间移动模式的变化可能带来的传播影响，为理解假期与传染病动态提供了新的视角。
本研究存在的局限性：(1)使用的手机信令数据仅来自中国联通，未纳入其他运营商的数据，且只在昆明市中心城区具有较高的覆盖率，因此无法完全代表全市所有手机用户的出行模式。(2)由于流感监测数据以周为单位汇总，导致单个假期的流感监测数据样本量有限(公共假期最多2个，寒假4个)，加之缺乏针对单个假期的多年流感监测数据，无法全面评估假期对流感的影响；这一点体现在本研究分析假期与流感综合指数的关系上。不仅如此，其他研究针对此类问题得出的结论不一：有研究显示ILI%在假期期间上升[38]，亦有研究表明其在假期期间下降[39]，这提示不同地区流感监测数据的差异和假期期间的就医行为可能会影响研究结论，这一现象可能源于中国不同地区在地理气候、经济水平及人文环境等方面存在的地域异质性，此类差异会直接影响流感监测数据的收集及公众在假期期间的就医行为等，从而可能导致不同地区的研究得出相异的结论。(3)所构建的动力学模型仅考虑人群移动本身对市内流感传播的影响，未考虑流感再生数随气候、社会等因素的动态变化，也未纳入假期接触模式的年龄、性别等人口特征差异。未来研究可通过拓展数据来源、延长观测时间、细化模型参数等途径，进一步完善对假期、人群移动与传染病传播关系的评估。
假期改变了昆明市内人群移动模式，这种改变进一步影响流感在当地的传播，且不同类型假期的影响存在差异。本研究定量估计了假期对市内人群移动模式与流感传播的影响，为公共卫生措施的制定和调整提供了科学依据。未来公共卫生措施可进行针对性调整：在国庆、元旦等非春节假期及寒假期间，可重点加强景区等人员密集场所的人群流动监测，提升流感个人防护宣传的针对性与覆盖范围，并依据各假期的人群移动模式特征，灵活调整流感防控策略。
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doi: 10.16462/j.cnki.zhjbkz.2026.02.002
  • 接收时间：2025-12-29
  • 首发时间：2026-05-08
  • 出版时间：2026-02-10
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  • 收稿日期：2025-12-29
  • 修回日期：2026-01-23
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    1复旦大学上海市重大传染病和生物安全研究院，上海 200032
    2复旦大学公共卫生学院流行病学教研室，上海 200032

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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
光柄菇属 Pluteus 5 2.39
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