| 王宇,张莉,吴双胜,段玮,孙瑛,张漫,张惺惺,张奕,马春娜,王全意,杨鹏.移动流行区间法在北京市流感流行阈值估计及强度分级中的应用[J].Chinese journal of Epidemiology,2020,41(2):201-206 |
| 移动流行区间法在北京市流感流行阈值估计及强度分级中的应用 |
| Application of the moving epidemic method in the development of epidemic thresholds and tiered warning alert approachs for influenza prevention in Beijing |
| Received:April 19, 2019 |
| DOI：10.3760/cma.j.issn.0254-6450.2020.02.012 |
| KeyWord: 流感 阈值 强度 移动流行区间法 交叉验证 |
| English Key Word: Influenza Threshold Intensity Moving epidemic method Cross-validation procedure |
| FundProject:北京市优秀人才培养资助青年拔尖个人项目（2014000021223ZK36）；首都卫生发展科研专项（2018-2-1013） |
| Author Name | Affiliation | E-mail | | Wang Yu | Institute for Infectious Disease and Endemic Disease Control, Beijing Center for Disease Prevention and Control, Beijing Research Center for Preventive Medicine, Beijing 100013, China | | | Zhang Li | Institute for Infectious Disease and Endemic Disease Control, Beijing Center for Disease Prevention and Control, Beijing Research Center for Preventive Medicine, Beijing 100013, China | | | Wu Shuangsheng | Institute for Infectious Disease and Endemic Disease Control, Beijing Center for Disease Prevention and Control, Beijing Research Center for Preventive Medicine, Beijing 100013, China | | | Duan Wei | Institute for Infectious Disease and Endemic Disease Control, Beijing Center for Disease Prevention and Control, Beijing Research Center for Preventive Medicine, Beijing 100013, China | | | Sun Ying | Institute for Infectious Disease and Endemic Disease Control, Beijing Center for Disease Prevention and Control, Beijing Research Center for Preventive Medicine, Beijing 100013, China | | | Zhang Man | Institute for Infectious Disease and Endemic Disease Control, Beijing Center for Disease Prevention and Control, Beijing Research Center for Preventive Medicine, Beijing 100013, China | | | Zhang Xingxing | Institute for Infectious Disease and Endemic Disease Control, Beijing Center for Disease Prevention and Control, Beijing Research Center for Preventive Medicine, Beijing 100013, China | | | Zhang Yi | Institute for Infectious Disease and Endemic Disease Control, Beijing Center for Disease Prevention and Control, Beijing Research Center for Preventive Medicine, Beijing 100013, China | | | Ma Chunna | Institute for Infectious Disease and Endemic Disease Control, Beijing Center for Disease Prevention and Control, Beijing Research Center for Preventive Medicine, Beijing 100013, China | | | Wang Quanyi | Institute for Infectious Disease and Endemic Disease Control, Beijing Center for Disease Prevention and Control, Beijing Research Center for Preventive Medicine, Beijing 100013, China | | | Yang Peng | Institute for Infectious Disease and Endemic Disease Control, Beijing Center for Disease Prevention and Control, Beijing Research Center for Preventive Medicine, Beijing 100013, China
School of Public Health, Capital Medical University, Beijing 100069, China | yangpengcdc@163.com |
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| Abstract: |
| 目的 估计北京市流感流行阈值和分级强度阈值，对2018－2019年流行季流感流行水平进行分级预警，并对估计阈值的方法进行评价。方法 应用北京市近5个流感流行季的流感样病例数和流感样病例百分比（ILI%）监测数据，采用移动流行区间法（MEM）估计流感流行阈值和分级强度阈值。应用交叉验证方法评价MEM与2种监测数据类型估计流行阈值的预警效果，评价指标为马修相关系数、约登指数、灵敏度和特异度。结果 估计预警2018－2019年流行季的流感样病例数的流行阈值为12 984例、中位强度阈值为22 503例、高强度阈值为37 589例、超高强度阈值为47 157例，评价流行阈值的指标马修相关系数为62%、约登指数为60%、灵敏度为69%、特异度为91%。应用ILI%监测数据，估计预警2018－2019年流行季流感流行阈值、中位强度阈值、高强度阈值及超高强度阈值分别为1.66%、2.46%、3.84%和4.66%，评价流行阈值的指标马修相关系数为59%、约登指数为54%、灵敏度为60%、特异度为94%。结论 MEM对预警流感流行有较好特异性，准确性在可接受范围内，该方法可在北京市流感分级预警中进行实际应用。应用流感样病例数监测资料预警效果略优于ILI%。 |
| English Abstract: |
| Objective To calculate both the epidemic and intensity thresholds for different levels in Beijing and to establish a tiered alert system in the 2018-2019 influenza season as well as to evaluate the performance of calculated thresholds. Method Weekly count of influenza-like illness and percentage of influenza-like illness (ILI%) of the last five influenza seasons were modeled by ‘moving epidemic method’ (MEM) to calculate the influenza epidemic and intensity thresholds at different levels. A cross-validation procedure was used to evaluate the performance. Indicators of Matthew correlation coefficient, Youden's index, sensitivity and specificity were calculated. Results For weekly count of influenza-like illness, data showed that the epidemic threshold for 2018-2019 influenza season was 12 984 and the medium, high and very high intensity thresholds were 22 503, 37 589, 47 157, respectively. Matthew correlation coefficient of the epidemic threshold was 62% and youden's index as 60%, sensitivity as 69%, specificity as 91%. Data on weekly ILI%, the epidemic threshold for 2018-2019 influenza season was 1.66%, with medium, high and very high intensity thresholds as 2.46%, 3.84% and 4.66%, respectively. The overall Matthew correlation coefficient of the epidemic threshold was 59%, with 54% for the Youden's index, sensitivity as 60% and specificity as 94%. Conclusions MEM produced a good specific signal for detecting the influenza epidemics and the accuracy of the method was acceptable. The early warning performance regarding the application of weekly count on influenza-like illness was slightly better than ILI%. This method could be applied in the practical influenza epidemic alert "work in Beijing". |
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