Application of moving epidemic method in early warning of influenza incidence intensity in winter-spring season in Ningbo city
-
摘要:
目的 探索移动流行区间法（MEM）在浙江省宁波市冬春季流行性感冒（流感）流行强度预警中的应用，为采取相应的干预措施提供参考依据。 方法 收集2013年1月 — 2022年5月宁波市2家国家级流感哨点医院的流感监测数据，最终选择2013年1月 — 2019年12月的冬春季流感病毒检测阳性率建立MEM模型，对宁波市2019 — 2020年流感流行季（2019年第40周至2020年第20周）开始、结束以及流行强度进行分析，并与实际流行情况进行比较，分析MEM模型的应用效果。 结果 宁波市2013 — 2022年流感病毒分为甲型H1N1、甲型H3N2和乙型（以Victoria株和Yamagata株为主），分别占24.59%、37.03%和38.38%；本研究建立的MEM模型参数 δ 为2.7，灵敏度为87.97%，特异度为87.68%，约登指数为0.76，拟合优度最高；经MEM模型拟合，2019 — 2020年流感季流行开始阈值为22.76%，流行结束阈值为25.05%，中流行强度阈值为43.18%，高流行强度阈值为63.22%，极高流行强度阈值为74.83%；2019年第40周至第48周为流行前期，在2019年第49周流感突破流行开始阈值进入低流行阶段，从第51周开始达到中等流行强度一直持续到2020年第3周，2020年第4周后再降为低流行强度；2020年第7周以后处于流行后阶段，第10周流感流行结束；2019 — 2020年与2013 — 2019年比较，流感流行季的开始时间提前了1周，但流行结束时间提前了3周。 结论 MEM模型在宁波市冬春季流感流行早期识别和流行强度预警中的应用效果较好。 -
关键词:
- 流行性感冒（流感） /
- 流行强度 /
- 预警 /
- 移动流行区间法（MEM） /
- 应用
Abstract:Objective To explore the application of moving epidemic method (MEM) in early warning of influenza incidence intensity in winter-spring season in Ningbo city and to provide a reference for developing effective intervention measures. Methods Influenza surveillance data for the period of January 2013 – May 2022 were collected from 2 national influenza sentinel hospitals in Ningbo city, Zhejiang province and a part of the data on positive rate of influenza virus detection among the registered cases in winter-spring seasons from January 2013 to December 2019 were extracted to establish a MEM model. The constructed MEM model was adopted to analyze the beginning, the end and incidence intensity of seasonal influenza epidemic during 2019 – 2020 (40th week, 2019 – 20th week, 2020) in Ningbo city; the model analysis results were compared with actual situation of the epidemic. Results For the virus-positive cases in the city during the 2013 – 2022, the proportions of isolated viral strains were 24.59% for influenza A (H1N1), 37.03% for influenza A (H3N2), and 38.38% for influenza B (mainly Victoria and Yamagata strain), respectively. The established MEM model showed a better goodness-of-fit, with the parameter δ of 2.7, the sensitivity of 87.97%, the specificity of 87.68%, and the Yoden index of 0.76. Based on the fitting results of the established MEM model, the thresholds of virus-positive rate were 22.76% and 25.05% for identifying the onset and the end of the influenza epidemic and the thresholds were 43.18%, 63.22%, and 74.83% for indicating a moderate-, high-, extremely high-intensity of the influenza epidemic during 2019 – 2020 influenza season. With the established MEM threshold estimations, the trajectory of the influenza could described as following: pre-epidemic stage from 40th week to 48th week of 2019, onset/low-intensity stage from 49th week of 2019, moderate-intensity stage from 51th week of 2019 to third week of 2020, subsequent low-intensity stage from 4th week of 2020, late stage from 7th week of 2020, and the end of the epidemic by the 10th week of 2020. Compared to the seasonal epidemics between 2013 – 2019, the 2019 – 2020 winter spring influenza epidemic occurred one week earlier but ended three weeks sooner. Conclusion MEM model could be adopted effectively in early identification and intensity warning of seasonal influenza epidemic. -
Key words:
- influenza /
- epidemic intensity /
- early warning /
- moving epidemic method /
- application
-
图 1 宁波市2013年第1周至2022年第20周流感病原学分型及阳性率分布
Figure 1. Week-specific viral positivity number and rate among detected influenza cases in Ningbo city, 2013 – week 20, 2022 based on national sentinel surveillance data
图 2 宁波市2013 — 2020年各流感流行季流感病毒阳性率时间序列图
Figure 2. Week-specific viral positivity rate among detected influenza cases during nine epidemic seasons in Ningbo city, 2013 – 2022 based on national sentinel surveillance data
图 3 不同参数（δ 值）设置下的模型评价效果
Figure 3. Sensitivity, specificity, positive predictive value, negative predictive value, and Jordan index in prediction of epidemic intensity of 2019 – 2020 influenza season in Ningbo city by moving epidemic method models with different δ values established using national sentinel surveillance data of 2013 – 2019
图 4 宁波市2013 — 2019年流感流行强度及2019 — 2020年流感流行季阈值估计图
Figure 4. Weekly viral positivity rate among detected influenza cases by yearly epidemic seasons in Ningbo city, 2013 – 2019 based on national sentinel surveillance data and moving epidemic method model-derived cutoff values of weekly viral positivity rate for prediction of the beginning, peak, end, and the epidemic intensity of 2019 – 2020 influenza season in Ningbo city
-
[1] Iuliano AD, Roguski KM, Chang HH, et al. Estimates of global seasonal influenza-associated respiratory mortality: a modelling study[J]. Lancet, 2018, 391(10127): 1285 – 1300. [2] 林梦宣, 陈辉, 宋宏彬, 等. 基于互联网大数据的传染病预测预警研究进展[J]. 中国公共卫生, 2021, 37(10): 1478 – 1482. doi: 10.11847/zgggws1136289 [3] 祝丙华, 王立贵, 孙岩松, 等. 基于大数据传染病监测预警研究进展[J]. 中国公共卫生, 2016, 32(9): 1276 – 1279. doi: 10.11847/zgggws2016-32-09-38 [4] Wang XL, Wu SS, MacIntyre CR, et al. Using an adjusted Serfling regression model to improve the early warning at the arrival of peak timing of influenza in Beijing[J]. PLoS One, 2015, 10(3): e0119923. doi: 10.1371/journal.pone.0119923 [5] Ly S, Arashiro T, Ieng V, et al. Establishing seasonal and alert influenza thresholds in Cambodia using the WHO method: implications for effective utilization of influenza surveillance in the tropics and subtropics[J]. Western Pacific Surveillance and Response Journal, 2017, 8(1): 22 – 32. doi: 10.5365/wpsar.2017.8.1.002 [6] 钱程, 戴启刚, 许可, 等. 移动流行区间法在评估江苏省流感流行强度中的应用研究[J]. 中国卫生统计, 2020, 37(1): 10 – 13,17. [7] Wu W, Guo JQ, An SY, et al. Comparison of two hybrid models for forecasting the incidence of hemorrhagic fever with renal syndrome in Jiangsu province, China[J]. PLoS One, 2015, 10(8): e0135492. doi: 10.1371/journal.pone.0135492 [8] Vega T, Lozano JE, Meerhoff T, et al. Influenza surveillance in Europe: comparing intensity levels calculated using the moving epidemic method[J]. Influenza and Other Respiratory Viruses, 2015, 9(5): 234 – 246. doi: 10.1111/irv.12330 [9] 王宇, 张莉, 吴双胜, 等. 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]. 中华流行病学杂志, 2020, 41(2): 201 – 206. [10] 聂陶然, 崔金朝, 任敏睿, 等. 应用移动流行区间法确定我国南方地区手足口病流行强度阈值[J]. 中华流行病学杂志, 2020, 41(7): 1047 – 1053. doi: 10.3760/cma.j.cn112338-20200114-00030 [11] 丁克琴, 谷少华, 劳旭影, 等. 基于移动流行区间法的浙江省宁波市冬春季流行性感冒流行强度研究[J]. 疾病监测, 2020, 35(2): 126 – 131. doi: 10.3784/j.issn.1003-9961.2020.02.010 [12] 徐文体, 董晓春, 李琳, 等. 2017 – 2018天津市流感季流感流行强度研究[J]. 中国人兽共患病学报, 2018, 34(12): 1155 – 1158,1164. [13] 刘天, 程勋杰, 姚梦雷, 等. 移动流行区间法在手足口病监测预警中的应用[J]. 疾病监测, 2019, 34(4): 348 – 353. doi: 10.3784/j.issn.1003-9961.2019.04.015 [14] 王宇, 吴双胜, 张莉, 等. 应用移动流行区间法和综合指数建立北京市流感流行分级预警标准[J]. 国际病毒学杂志, 2020, 27(4): 271 – 274. doi: 10.3760/cma.j.issn.1673-4092.2020.04.002 [15] Kang M, Tan XH, Ye MY, et al. The moving epidemic method applied to influenza surveillance in Guangdong, China[J]. International Journal of Infectious Diseases, 2021, 104: 594 – 600. doi: 10.1016/j.ijid.2021.01.058 [16] 谭亚运, 曾令佳, 秦颖, 等. 移动流行区间法在中国7个气候区流感流行阈值制定中的应用效果评价[J]. 中华预防医学杂志, 2019, 53(10): 1007 – 1011. [17] Bouguerra H, Boutouria E, Zorraga M, et al. Applying the moving epidemic method to determine influenza epidemic and intensity thresholds using influenza - like illness surveillance data 2009 – 2018 in Tunisia[J]. Influenza and Other Respiratory Viruses, 2020, 14(5): 507 – 514. doi: 10.1111/irv.12748 [18] Murray JLK, Marques DFP, Cameron RL, et al. Moving epidemic method (MEM) applied to virology data as a novel real time tool to predict peak in seasonal influenza healthcare utilization. The Scottish experience of the 2017/18 season to date[J]. Euro-surveillance, 2018, 23(11): 18 – 00079. [19] 程勋杰, 陈涛, 舒跃龙, 等. 移动流行区间法在我国北方15省份流感流行阈值制定中的应用效果评价[J]. 中国卫生统计, 2016, 33(6): 979 – 982. [20] 刘天, 姚梦雷, 黄继贵, 等. 移动流行区间法在流感监测预警中的应用及其参数设置[J]. 国际病毒学杂志, 2018, 25(6): 415 – 418. doi: 10.3760/cma.j.issn.1673-4092.2018.06.015 -
下载:
点击查看大图
计量
- 文章访问数: 343
- HTML全文浏览量: 141
- PDF下载量: 130
- 被引次数: 0