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Article(id=1322609042653036590, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1322609030938346427, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202506072, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1749052800000, receivedDateStr=2025-06-05, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793264168655, onlineDateStr=2026-10-29, pubDate=1764000000000, pubDateStr=2025-11-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793264168655, onlineIssueDateStr=2026-10-29, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793264168655, creator=13701087609, updateTime=1793264168655, 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=4046, endPage=4051, ext={EN=ArticleExt(id=1322609044435615792, articleId=1322609042653036590, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Noise reduction and prediction performance optimization of influenza surveillance data based on mechanism modeling and data assimilation, columnId=1228016567443718970, journalTitle=Modern Preventive Medicine, columnName=Epidemiology and Statistical Methods Advances, runingTitle=null, highlight=null, articleAbstract=
Objective

To improve the structural quality of influenza surveillance data and enhance the performance of predictive models by leveraging an epidemic dynamics-informed data assimilation approach.

Methods

This study constructed a data assimilation framework that combined a Susceptible–Infectious–Recovered–Susceptible（SIRS）model with the Ensemble Adjustment Kalman Filter（EAKF）to assimilate weekly Goldstein indices（Influenza-like Illness Percentage × Positive Rate）from 21 cities in Sichuan Province. The signal-to-noise ratio（SNR）was used to evaluate the effect of data structure optimization, and predictive performance before and after assimilation was assessed using three forecasting models: ARIMA, RNN, and LSTM. Linear mixed-effect models were applied to quantify the impact of assimilation on SNR and root mean squared error（RMSE）. Statistical significance was defined as P<0.05.

Results

After applying the SIRS-EAKF assimilation framework, the SNR significantly increased in 18 cities（85.7%）, with an average improvement of 3.26（P<0.01）. Across all three prediction models, RMSE were significantly reduced in the assimilated data group（all P<0.05）.

Conclusion

The SIRS-EAKF framework effectively suppressed the noise and enhanced the expression of the dominant epidemic signal through incorporating dynamical mechanisms. Without altering the structure of the prediction model, it significantly improved both the quality of influenza surveillance data and the predictive accuracy. This approach provides a robust methodological foundation for real-time epidemic forecasting and public health decision-making.

, authors=Xin LI1, Yu-xing TIAN1, Heng YUAN2, Tao ZHANG1, authorsList=Xin LI, Yu-xing TIAN, Heng YUAN, Tao ZHANG, authorCompany=null, correspAuthors=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=1322609045530329148, articleId=1322609042653036590, tenantId=1146029695717560320, journalId=1227665162245664772, language=CN, title=基于机制建模与数据同化的流感监测数据降噪与预测性能优化研究, 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

本研究旨在构建融合动力学机制的数据同化模型，以提升流感监测数据的质量与预测模型的准确性。

方法

本研究构建了一个将易感-感染-康复-易感（SIRS）模型与集合调整卡尔曼滤波（EAKF）相结合的数据同化框架，并对四川省21个城市的每周Goldstein指数（ILI%×PR）进行同化。采用信噪比（SNR）评估数据结构优化效果，通过ARIMA、RNN和LSTM三种预测模型对比同化前后的预测性能，并利用线性混合效应模型分析SIRS-EAKF方法对SNR和均方根误差（RMSE）的影响，显著性检验水准设为α<0.05。

结果

SIRS-EAKF处理后，18个地市（85.7%）的SNR显著提升，平均增幅为3.26（P<0.01）；在三类预测模型中，处理组的RMSE均显著低于对照组（P均<0.05）。

结论

SIRS-EAKF框架通过引入传播机制，有效抑制噪声，增强了主流行信号的表达。在不改变预测模型结构的前提下，显著提升了流感监测数据质量与预测精度，为实时疫情预测和公共卫生决策提供了坚实的方法基础。

, authors=李鑫1, 田宇星1, 袁珩2, 张韬1, authorsList=李鑫, 田宇星, 袁珩, 张韬, authorCompany=null, correspAuthors=张韬, authorNote=

李鑫（2000—），男，硕士在读，研究方向：流行病与卫生统计学

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张韬，E-mail:
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李鑫（2000—），男，硕士在读，研究方向：流行病与卫生统计学

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BMC Infectious Diseases, 2017, 17 (1): 648., articleTitle=Predictive accuracy of particle filtering in dynamic models supporting outbreak projections, refAbstract=null)], funds=[Fund(id=1322609053365289071, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322609042653036590, awardId=2022YFC3600600, language=CN, fundingSource=国家重点研发计划项目(2022YFC3600600), fundOrder=null, country=null), Fund(id=1322609053570809968, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322609042653036590, 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=1322609053667278961, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322609042653036590, 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=1322609053805690994, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322609042653036590, awardId=2024-YF05-01784-SN, language=CN, fundingSource=成都市科技局项目(2024-YF05-01784-SN), fundOrder=null, country=null), Fund(id=1322609053885382771, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322609042653036590, 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)], companyList=[AuthorCompany(id=1322609045861679165, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322609042653036590, xref=1., ext=[AuthorCompanyExt(id=1322609045865873470, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322609042653036590, companyId=1322609045861679165, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=Department of Epidemiology and Health Statistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China), AuthorCompanyExt(id=1322609045874262079, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322609042653036590, companyId=1322609045861679165, 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/华西第四医院流行病与卫生统计学系，四川　成都　610041)]), AuthorCompany(id=1322609045941370944, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322609042653036590, xref=2., ext=[AuthorCompanyExt(id=1322609045945565249, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322609042653036590, companyId=1322609045941370944, 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=1322609049699467361, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322609042653036590, language=EN, label=Fig.1, caption=SIRS-EAKF framework, figureFileSmall=492VcWg8F9OVLAencXutdA==, figureFileBig=udQWjzdpwe5SiA2IBjBLHw==, tableContent=null), ArticleFig(id=1322609049787547746, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322609042653036590, language=CN, label=图1, caption=SIRS-EAKF框架, figureFileSmall=492VcWg8F9OVLAencXutdA==, figureFileBig=udQWjzdpwe5SiA2IBjBLHw==, tableContent=null), ArticleFig(id=1322609049980485731, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322609042653036590, language=EN, label=Fig.2, caption=Comparison of optimization of influenza surveillance data in 21 cities of Sichuan Province, 2017-2019, figureFileSmall=SOj4ckFZN4aHJfrq55x9mg==, figureFileBig=6A3k1zgiXnf8u4fgvdfc8A==, tableContent=null), ArticleFig(id=1322609050072760420, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322609042653036590, language=CN, label=图2, caption=2017—2019年四川省21个地市流感监测数据的结构优化对比

注：红色曲线代表原始数据，蓝色曲线代表经过SIRS-EAKF处理的数据。

, figureFileSmall=SOj4ckFZN4aHJfrq55x9mg==, figureFileBig=6A3k1zgiXnf8u4fgvdfc8A==, tableContent=null), ArticleFig(id=1322609050152452197, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322609042653036590, language=EN, label=Fig.3, caption=Comparison of SNR between raw data and SIRS-EAKF processed data, figureFileSmall=Mr0Ll8jeSJUvS41IIUMlhw==, figureFileBig=I5ym3iGwEZIEd4SzNovDaQ==, tableContent=null), ArticleFig(id=1322609050219561062, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322609042653036590, language=CN, label=图3, caption=原始数据与SIRS-EAKF处理后的SNR对比, figureFileSmall=Mr0Ll8jeSJUvS41IIUMlhw==, figureFileBig=I5ym3iGwEZIEd4SzNovDaQ==, tableContent=null), ArticleFig(id=1322609050458636391, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322609042653036590, language=EN, label=Table 1, caption=

Comparison of SNR before and after SIRS-EAKF processing in 21 cities of Sichuan Province, 2017-2019

, figureFileSmall=null, figureFileBig=null, tableContent=
区域SNR
处理前处理后
阿坝2.994.19
巴中1.154.72
成都17.2618.86
达州1.665.39
德阳4.5016.29
甘孜1.752.30
广安5.3511.87
广元7.397.86
乐山3.637.17
凉山1.990.96
泸州5.098.55
眉山8.458.09
绵阳21.2417.51
南充1.926.51
内江5.786.52
攀枝花6.5820.03
遂宁1.384.71
雅安4.686.21
宜宾8.0314.10
资阳3.008.12
自贡2.914.32
P值<0.01
), ArticleFig(id=1322609050550911080, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322609042653036590, language=CN, label=表1, caption=

2017—2019年四川省21个地市中SIRS-EAKF处理前后的SNR对比

, figureFileSmall=null, figureFileBig=null, tableContent=
区域SNR
处理前处理后
阿坝2.994.19
巴中1.154.72
成都17.2618.86
达州1.665.39
德阳4.5016.29
甘孜1.752.30
广安5.3511.87
广元7.397.86
乐山3.637.17
凉山1.990.96
泸州5.098.55
眉山8.458.09
绵阳21.2417.51
南充1.926.51
内江5.786.52
攀枝花6.5820.03
遂宁1.384.71
雅安4.686.21
宜宾8.0314.10
资阳3.008.12
自贡2.914.32
P值<0.01
), ArticleFig(id=1322609050618019945, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322609042653036590, language=EN, label=Table 2, caption=

Performance comparison of ARIMA Models before and after SIRS-EAKF processing in 21 cities of Sichuan Province, 2017-2019: RMSE for training and testing sets

, figureFileSmall=null, figureFileBig=null, tableContent=
区域RMSE（训练集）RMSE（测试集）
处理前处理后处理前处理后
阿坝27.9812.9548.6239.24
巴中7.872.838.577.03
成都67.2759.05231.21138.12
达州15.255.6136.1618.61
德阳9.864.1619.777.95
甘孜38.5824.7529.8924.68
广安30.8816.5849.1941.79
广元83.0378.93207.34183.31
乐山19.538.5723.4511.15
凉山213.41157.5817.5320.80
泸州66.6955.74174.74100.17
眉山30.1320.9243.1244.18
绵阳38.7238.35195.06175.28
南充75.8038.30305.36244.89
内江108.3494.98144.04141.69
攀枝花46.2820.6584.5888.43
遂宁24.7614.5888.1236.18
雅安55.1538.6052.5959.12
宜宾103.7662.28240.01227.91
资阳44.7618.5055.3451.12
自贡31.4619.8066.6240.72
P值<0.05<0.05
), ArticleFig(id=1322609050936787050, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322609042653036590, language=CN, label=表2, caption=

2017—2019年四川省21个地市中SIRS-EAKF处理前后的ARIMA模型性能对比：训练集和测试集的RMSE

, figureFileSmall=null, figureFileBig=null, tableContent=
区域RMSE（训练集）RMSE（测试集）
处理前处理后处理前处理后
阿坝27.9812.9548.6239.24
巴中7.872.838.577.03
成都67.2759.05231.21138.12
达州15.255.6136.1618.61
德阳9.864.1619.777.95
甘孜38.5824.7529.8924.68
广安30.8816.5849.1941.79
广元83.0378.93207.34183.31
乐山19.538.5723.4511.15
凉山213.41157.5817.5320.80
泸州66.6955.74174.74100.17
眉山30.1320.9243.1244.18
绵阳38.7238.35195.06175.28
南充75.8038.30305.36244.89
内江108.3494.98144.04141.69
攀枝花46.2820.6584.5888.43
遂宁24.7614.5888.1236.18
雅安55.1538.6052.5959.12
宜宾103.7662.28240.01227.91
资阳44.7618.5055.3451.12
自贡31.4619.8066.6240.72
P值<0.05<0.05
), ArticleFig(id=1322609051029061739, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322609042653036590, language=EN, label=Table 3, caption=

Performance comparison of RNN Models before and after SIRS-EAKF processing in 21 cities of Sichuan Province, 2017-2019: RMSE for training and testing sets

, figureFileSmall=null, figureFileBig=null, tableContent=
区域RMSE（训练集）RMSE（测试集）
处理前处理后处理前处理后
阿坝31.299.4437.8636.80
巴中2.222.0315.775.60
成都35.4950.1760.5127.75
达州13.884.5333.7517.86
德阳9.603.116.794.55
甘孜23.6111.9156.4747.82
广安27.4820.1650.3839.64
广元50.8166.60106.0480.53
乐山15.127.8617.7117.63
凉山186.71125.8633.1633.20
泸州60.4347.3270.2235.48
眉山32.7126.5137.5135.93
绵阳38.5440.7588.3589.47
南充63.9537.4293.1292.39
内江81.0377.43192.18160.32
攀枝花35.2818.7864.4746.35
遂宁15.4413.5229.2216.71
雅安57.4332.6468.9332.77
宜宾66.0657.36184.26154.31
资阳38.7825.9656.1236.61
自贡27.6214.4646.2845.22
P值<0.05<0.05
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2017—2019年四川省21个地市中SIRS-EAKF处理前后的RNN模型性能对比：训练集和测试集的RMSE

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区域RMSE（训练集）RMSE（测试集）
处理前处理后处理前处理后
阿坝31.299.4437.8636.80
巴中2.222.0315.775.60
成都35.4950.1760.5127.75
达州13.884.5333.7517.86
德阳9.603.116.794.55
甘孜23.6111.9156.4747.82
广安27.4820.1650.3839.64
广元50.8166.60106.0480.53
乐山15.127.8617.7117.63
凉山186.71125.8633.1633.20
泸州60.4347.3270.2235.48
眉山32.7126.5137.5135.93
绵阳38.5440.7588.3589.47
南充63.9537.4293.1292.39
内江81.0377.43192.18160.32
攀枝花35.2818.7864.4746.35
遂宁15.4413.5229.2216.71
雅安57.4332.6468.9332.77
宜宾66.0657.36184.26154.31
资阳38.7825.9656.1236.61
自贡27.6214.4646.2845.22
P值<0.05<0.05
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Performance comparison of LSTM Models before and after SIRS-EAKF processing in 21 cities of Sichuan Province, 2017-2019: RMSE for training and testing sets

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区域RMSE（训练集）RMSE（测试集）
处理前处理后处理前处理后
阿坝26.688.7548.0437.99
巴中4.451.9114.125.51
成都28.7746.2198.7138.00
达州14.214.5233.6217.56
德阳9.793.187.724.64
甘孜22.7711.2366.4044.84
广安28.0019.0049.7440.67
广元53.8175.47107.3887.16
乐山19.727.7612.358.62
凉山185.93117.9630.8325.51
泸州62.0855.7459.0034.87
眉山34.1526.0233.0328.51
绵阳40.8045.0278.0888.95
南充62.0535.1482.0577.26
内江89.4397.24187.94175.58
攀枝花39.9021.7257.5842.58
遂宁25.9815.2822.9320.52
雅安55.3734.9558.2332.46
宜宾81.7652.84188.12178.65
资阳47.3427.9847.6134.26
自贡31.9217.2848.9738.44
P值<0.05<0.05
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2017—2019年四川省21个地市中SIRS-EAKF处理前后的LSTM模型性能对比：训练集和测试集的RMSE

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区域RMSE（训练集）RMSE（测试集）
处理前处理后处理前处理后
阿坝26.688.7548.0437.99
巴中4.451.9114.125.51
成都28.7746.2198.7138.00
达州14.214.5233.6217.56
德阳9.793.187.724.64
甘孜22.7711.2366.4044.84
广安28.0019.0049.7440.67
广元53.8175.47107.3887.16
乐山19.727.7612.358.62
凉山185.93117.9630.8325.51
泸州62.0855.7459.0034.87
眉山34.1526.0233.0328.51
绵阳40.8045.0278.0888.95
南充62.0535.1482.0577.26
内江89.4397.24187.94175.58
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遂宁25.9815.2822.9320.52
雅安55.3734.9558.2332.46
宜宾81.7652.84188.12178.65
资阳47.3427.9847.6134.26
自贡31.9217.2848.9738.44
P值<0.05<0.05
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基于机制建模与数据同化的流感监测数据降噪与预测性能优化研究
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李鑫 1 , 田宇星 1 , 袁珩 2 , 张韬 1
现代预防医学 | 流行病与统计方法 2025,52(22): 4046-4051
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现代预防医学 |流行病与统计方法 2025 , 52 (22) : 4046 -4051
基于机制建模与数据同化的流感监测数据降噪与预测性能优化研究
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李鑫1, 田宇星1, 袁珩2, 张韬1
作者信息
  • 1.四川大学华西公共卫生学院/华西第四医院流行病与卫生统计学系，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，E-mail:
作者简介:

李鑫（2000—），男，硕士在读，研究方向：流行病与卫生统计学

Noise reduction and prediction performance optimization of influenza surveillance data based on mechanism modeling and data assimilation
Xin LI1, Yu-xing TIAN1, Heng YUAN2, Tao ZHANG1
Affiliations
  • Department of Epidemiology and Health Statistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China
出版时间: 2025-11-25 doi: 10.20043/j.cnki.MPM.202506072
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摘要
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目的

本研究旨在构建融合动力学机制的数据同化模型，以提升流感监测数据的质量与预测模型的准确性。

方法

本研究构建了一个将易感-感染-康复-易感（SIRS）模型与集合调整卡尔曼滤波（EAKF）相结合的数据同化框架，并对四川省21个城市的每周Goldstein指数（ILI%×PR）进行同化。采用信噪比（SNR）评估数据结构优化效果，通过ARIMA、RNN和LSTM三种预测模型对比同化前后的预测性能，并利用线性混合效应模型分析SIRS-EAKF方法对SNR和均方根误差（RMSE）的影响，显著性检验水准设为α<0.05。

结果

SIRS-EAKF处理后，18个地市（85.7%）的SNR显著提升，平均增幅为3.26（P<0.01）；在三类预测模型中，处理组的RMSE均显著低于对照组（P均<0.05）。

结论

SIRS-EAKF框架通过引入传播机制，有效抑制噪声，增强了主流行信号的表达。在不改变预测模型结构的前提下，显著提升了流感监测数据质量与预测精度，为实时疫情预测和公共卫生决策提供了坚实的方法基础。

流感监测  /  数据同化  /  SIRS模型  /  EAKF  /  时间序列预测
Objective

To improve the structural quality of influenza surveillance data and enhance the performance of predictive models by leveraging an epidemic dynamics-informed data assimilation approach.

Methods

This study constructed a data assimilation framework that combined a Susceptible–Infectious–Recovered–Susceptible（SIRS）model with the Ensemble Adjustment Kalman Filter（EAKF）to assimilate weekly Goldstein indices（Influenza-like Illness Percentage × Positive Rate）from 21 cities in Sichuan Province. The signal-to-noise ratio（SNR）was used to evaluate the effect of data structure optimization, and predictive performance before and after assimilation was assessed using three forecasting models: ARIMA, RNN, and LSTM. Linear mixed-effect models were applied to quantify the impact of assimilation on SNR and root mean squared error（RMSE）. Statistical significance was defined as P<0.05.

Results

After applying the SIRS-EAKF assimilation framework, the SNR significantly increased in 18 cities（85.7%）, with an average improvement of 3.26（P<0.01）. Across all three prediction models, RMSE were significantly reduced in the assimilated data group（all P<0.05）.

Conclusion

The SIRS-EAKF framework effectively suppressed the noise and enhanced the expression of the dominant epidemic signal through incorporating dynamical mechanisms. Without altering the structure of the prediction model, it significantly improved both the quality of influenza surveillance data and the predictive accuracy. This approach provides a robust methodological foundation for real-time epidemic forecasting and public health decision-making.

Influenza surveillance  /  Data assimilation  /  SIRS model  /  EAKF  /  Time series forecasting
李鑫, 田宇星, 袁珩, 张韬. 基于机制建模与数据同化的流感监测数据降噪与预测性能优化研究. 现代预防医学, 2025 , 52 (22) : 4046 -4051 . DOI: 10.20043/j.cnki.MPM.202506072
Xin LI, Yu-xing TIAN, Heng YUAN, Tao ZHANG. Noise reduction and prediction performance optimization of influenza surveillance data based on mechanism modeling and data assimilation[J]. Modern Preventive Medicine, 2025 , 52 (22) : 4046 -4051 . DOI: 10.20043/j.cnki.MPM.202506072
正文
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流感是由流感病毒引起的一种传染性强的急性呼吸道传染病，具有明显的季节性和区域性分布特征，对全球公共健康构成持续威胁[1]。2024年7月30日，世卫组织发布了一份最新报告[2]，将流感病毒列为西太平洋地区的高度优先病原体。然而，现行监测指标如流感样病例就诊百分比（Influenza-like Illness Percentage, ILI%）及实验室阳性率（Positive Rate, PR）[3]，虽具有可及性强、成本低等优势，但是经常出现不规则和短期波动[4-5]，导致监测数据存在观测噪声[6]，可能会掩盖潜在的传播信号，从而影响预测模型的性能。
近年来，将传播动力学模型与数据同化技术相结合的建模策略逐渐受到关注[7-10]。该策略把传播机制作为先验信息，动态更新观测状态，在观测与模型之间重新分配信息权重，在保证生物学合理性的同时，最大化主要传播信号的贡献。通过将观测数据与传播动力学模型进行融合，该策略能够动态估计一系列关键流行病学参数与状态变量，包括随时间变化的传播率、人群免疫持续时间以及潜在感染人数等。此外，该框架还可估计参数与状态变量的动态不确定性，为高峰时间、峰值规模等关键预测指标提供可信区间。本研究以流感监测数据为基础，构建融合传播机制和数据同化方法的联合模型，并评估其在流感时序监测数据中的降噪作用和预测优化效果，为传染病的的实时预警与科学决策提供方法基础。
1　对象与方法
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1.1　资料来源
本研究的每周ILI%和PR数据来源于四川省疾病预防控制中心，纳入四川省21个地市2017年40周至2019年39周的哨点监测数据。本研究按照WHO推荐计算Goldstein指数[11-12]=ILI%×PR，作为流感活动强度的代表指标。从国家气象数据共享中心（http://data.cma.cn/）检索获取四川省2017-2019年各个站点的气象值，并转换为周均数据。人口密度和自然死亡率数据来自四川统计年鉴。
1.2　SIRS-EAKF建模基本过程
本研究构建了以传播动力学易感-感染-康复-再易感（Susceptible-Infectious-Recovered- Susceptible, SIRS）模型为基础、融合集合调整卡尔曼滤波（Ensemble Adjustment Kalman Filter, EAKF）的建模框架，简称SIRS-EAKF模型，见图1。该模型通过引入流行病学机制来捕捉流感的传播趋势，同时引入与绝对湿度相关的时变传播率以反映环境因子的影响。数据同化部分构建高维状态空间，在观测过程中引入协方差调整机制，以提升模型对高噪声流感监测数据的鲁棒性和预测一致性，从而实现传播机制与观测信息的动态融合[7,13-14]。
1.3　研究方法
利用 R 4.4.1 统计软件，基于 tgp 和 MASS 软件包实现拉丁超立方抽样（LHS）生成参数组合，通过EAKF算法与 SIRS传染病模型构建动态预测框架；基于forecast软件包构建时间序列模型；基于lme4软件包构建线性混合效应模型；基于ggplot2软件包进行结果可视化。利用Python 3.8.20软件，基于PyTorch 2.3.0框架构建循环神经网络模型；基于 EarlyStopping自定义类实现早停机制，利用Adam优化器和均方误差损失函数进行模型训练。
1.3.1　研究指标
本研究围绕“信号增强”和“预测能力提升”两个方面对比分析原始观测数据与经SIRS-EAKF模型处理后的Goldstein数据的表现差异。
将是否引入SIRS-EAKF方法处理定义为干预变量EAKF_flag：EAKF_flag=0代表原始Goldstein指数序列，EAKF_flag =1代表处理后的Goldstein指数序列。因变量分为两类：（1）用信噪比（Signal-to-Noise Ratio, SNR）衡量数据信号质量；（2）分别利用原始观测数据与EAKF滤波数据构建自回归差分移动平均模型（Autoregressive Integrated Moving Average Model，ARIMA），普通循环神经网络（Recurrent Neural Network, RNN），长短期记忆网络（Long Short-Term Memory，LSTM）预测模型，并采用均方根误差（Root Mean Squared Error, RMSE）衡量不同输入数据下预测模型的性能表现。
1.3.2　研究设计
为系统评估SIRS-EAKF方法对流感监测数据建模的作用，本研究构建多个线性混合效应模型，统计显著性定义为P<0.05。
模型1评估SIRS-EAKF处理对信号强度的影响，形式如下：
其中，SNRij表示第j个城市在第i条记录中的信噪比，EAKF_flagij表示是否使用SIRS-EAKF增强，β1捕捉SIRS-EAKF处理对信噪比的平均效应，uj表示地区的随机效应项，ϵij为残差项。该模型用于验证在机制一致性的约束下，SIRS-EAKF是否能够有效抑制随机扰动、提升时序数据中的结构信号比重。
模型2~7评估SIRS-EAKF处理对预测性能的影响。本研究将2017年40周至2019年39周的流感监测数据按7:3的比例分为训练集和测试集，分别用于模型拟合和未来趋势预测。针对三种预测模型（ARIMA、RNN、LSTM）和误差指标（RMSE），分别建立6个独立的混合效应模型，通用形式如下：
γ1为SIRS-EAKF处理对预测误差指标的平均效应，其余解释同上。
2　结果
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2.1　四川省流感监测数据的时序信号增强效果评估
图2对比展示了四川省21个地市的原始Goldstein指数序列（红色）与SIRS-EAKF处理后序列（蓝色）的变化情况。
实验结果显示，SIRS-EAKF处理表现出良好的信号优化效果，主要体现在以下三个方面：（1）噪声抑制：经SIRS-EAKF处理后的流感活动曲线（蓝色）相比原始数据（红色）显著减少了短周期观测噪声波动，呈现出更加平滑和连贯的变化趋势。宜宾、眉山、遂宁等地的噪声抑制效果尤为显著。（2）保留流行高峰：SIRS-EAKF增强后的序列在保留主要季节性高峰特征方面表现突出。例如，成都、绵阳、内江等地的冬春季流行高峰位置和幅度与原始数据基本一致，而背景噪声水平大幅降低，从而提升了主要信号的可识别性。（3）异常点平滑：对于部分原始数据中存在异常剧烈波动的地区（如甘孜、凉山），SIRS-EAKF处理有效削弱了这些异常极值对整体趋势的干扰，使得流感活动信号更加符合生物物理机制的期望轨迹。
2.2　机制约束下的数据信号重塑：SIRS-EAKF增强主信号表达能力
采用SIRS-EAKF处理后，数据的平均SNR值提升了约3.26个单位，差异具有统计学显著性（P<0.01），说明SIRS-EAKF方法在减少观测误差、抑制随机波动方面具有稳定而显著的优势，见图3。
进一步按城市比较，18个城市（85.7%）在应用SIRS-EAKF后实现了SNR的提升，详见表1。其中，部分城市的SNR提升幅度尤为显著：德阳市的SNR从处理前的4.50大幅提升至16.29，攀枝花市从6.58提升至20.03，宜宾市从8.03提升至14.10，广安市从5.35提升至11.87，资阳市从3.00提升至8.12，均体现出处理后主信号成分的明显增强。这些结果显示SIRS-EAKF处理能够提取主要流行信号，减少随机波动。
2.3　基于数据结构优化的预测性能提升：ARIMA、RNN与LSTM案例分析
总体结果表明，SIRS-EAKF处理普遍降低了各模型的拟合和预测误差，体现了更优的趋势拟合与波动捕捉能力。具体而言，在ARIMA模型中，SIRS-EAKF处理后的RMSE均低于原始监测数据（P均<0.05）。表2展示了ARIMA模型在RMSE指标下的性能，其中SIRS-EAKF组显示出更集中的误差分布和更少的异常值，表明模型拟合和预测稳定性得到了改善。
在RNN模型中，SIRS-EAKF处理同样带来了明显改进。处理后模型的RMSE均显著低于原始数据对应的指标（P均<0.05）。表3显示，SIRS-EAKF处理所提供的增强信号提高了预测的稳定性和概括性，误差分布更加均匀，并消除了极端偏差。
对于LSTM模型，处理后模型的RMSE均显著降低（P均<0.05），见表4。这提示在复杂时序任务中，输入信号结构的改善对于LSTM类模型尤为重要，有助于它们更好地捕捉长期依赖特征并提高预测精度。
从三类模型的综合表现来看，SIRS-EAKF数据处理对时间序列模型（ARIMA）和深度学习模型（RNN、LSTM）均具有广泛适用性。其带来的性能提升不仅体现在平均误差的下降，还反映在预测结果波动性减小和稳定性增强。
3　讨论
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在流感和其他复杂公共卫生事件的时间序列建模中，传统研究对数据中固有的流行病学信号结构关注不够。本研究提供了一个新的视角：数据信号输入的质量也影响着预测系统的可靠性。本研究结果表明，SIRS-EAKF处理能持续改善不同地区流感监测数据的信噪比，并显著降低三种预测模型的拟合和预测误差。这些结果表明，SIRS-EAKF不仅是一种信号增强技术，而且在实际建模场景中具有广泛的适用性和鲁棒性，在现实场景中具有明显的实用性。
以往研究多集中于优化模型架构（如深度神经网络、集成学习）[15-17]，而对数据信号本身的建模能力关注较少。虽然已有部分研究尝试通过Loess平滑[18]、经验滤波[19]、粒子滤波[20]等方法抑制噪声，但纯粹的平滑和滤波往往忽略了流感传播的生物物理机制，难以将机制信息作为先验约束；粒子滤波在参数漂移时易发生退化，且计算复杂度较高。上述方法通常缺乏流行病学机制约束，难以有效分离主要传播信号与高频干扰，同时也不具备良好的泛化能力。SIRS-EAKF通过引入物理机制约束与贝叶斯更新，在动态演化过程中抑制观测噪声、强化真实流行信号，实现了“在动力学先验指导下的信号重建”。
本研究还存在一些局限性，如指标评价维度较为单一，未来可以引入更多的评价维度，如信息熵、频谱特征分解等，以更全面地刻画信号处理效果。除流感外，这一信号处理框架还可考虑扩展到其他具有动态背景的传染病（如登革热、新冠病毒、猴痘等）的监测建模中，以评估其跨疾病的适用性和扩展潜力。
基金
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  • 国家重点研发计划项目(2022YFC3600600)
  • 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)
  • 四川省预防医学会基层公共卫生专项科研项目(SYYXH202403; SYYXHPT202406)
文献
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参考文献 引证文献
排序方式：
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Yang L, Li G, Yang J, et al. Deep-Learning model for influenza prediction from multisource heterogeneous data in a megacity: model development and evaluation [J]. Journal of Medical Internet Research, 2023, 25: e44238.
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Zhao D, Zhang R. A new hybrid model SARIMA-ETS-SVR for seasonal influenza incidence prediction in mainland China [J]. Journal of Infection in Developing Countries, 2023, 17 (11): 1581-1590.
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Chunikhina E, Logan P, Kovchegov Y, et al. The C-SHIFT algorithm for normalizing covariances [J]. IEEE-ACM Transactions on Computational Biology and Bioinformatics, 2023, 20 (1): 720-730.
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Tsai CW, Hsiao YR, Lin ML, et al. Development of a noise-assisted multivariate empirical mode decomposition framework for characterizing PM 2.5 air pollution in Taiwan and its relation to hydro-meteorological factors [J]. Environment International, 2020, 139: 105669.
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Safarishahrbijari A, Teyhouee A, Waldner C, et al. Predictive accuracy of particle filtering in dynamic models supporting outbreak projections [J]. BMC Infectious Diseases, 2017, 17 (1): 648.
2025年第52卷第22期
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文章信息
doi: 10.20043/j.cnki.MPM.202506072
  • 接收时间：2025-06-05
  • 首发时间：2026-10-29
  • 出版时间：2025-11-25
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  • 收稿日期：2025-06-05
基金
国家重点研发计划项目(2022YFC3600600)
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)
四川省预防医学会基层公共卫生专项科研项目(SYYXH202403; SYYXHPT202406)
作者信息
    1.四川大学华西公共卫生学院/华西第四医院流行病与卫生统计学系，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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https://castjournals.cast.org.cn/joweb/xdyfyx/CN/10.20043/j.cnki.MPM.202506072
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2种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
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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