武汉交通管制和集中隔离对新型冠状病毒肺炎疫情影响的动力学模型研究

doi:10.6040/j.issn.1671-7554.0.2020.0712

摘要/Abstract

摘要： 目的定量评价武汉市采取交通管制和集中隔离措施对新型冠状病毒肺炎疫情的控制作用,为疫情防控提供科学依据。方法基于SEIR动力学模型,考虑无症状感染者和未确诊隔离患者的特征,构建SEIAHR模型。基于防控措施的实施时间,将疫情分为三阶段,并分别进行参数拟合和计算基本再生数,并对疫情的发展趋势进行预测。结果交通管制和集中隔离实施后,R0显著降低,三阶段的R0分别为3.684 1(95%CI:3.106 1~4.048 0)、2.178 8(95%CI: 1.725 8~3.577 6)、0.362 5(95%CI: 0.349 9~0.367 6),发病高峰也发生前移,从交通管制前的2020年4月19日前移至2020年3月14日,疫情规模也在防控措施的作用下减小。结论武汉交通管制和集中隔离措施对于疫情控制具有相当良好的作用,可以为其他国家疫情防控提供参考。

关键词: 新型冠状病毒肺炎, SEIAHR传染病动力学模型, 交通管制, 定点医院, 基本再生数

Abstract: Objective To quantitatively evaluate the effects of traffic control and centralized quarantine measures on COVID-19 epidemic in Wuhan, so as to provide scientific basis for epidemic prevention and control. Methods The SEIAHR model was established based on SEIR dynamic model, which took into account the characteristics of asymptomatic carriers and unconfirmed quarantined patients. Based on the timing of prevention measures, the epidemic was divided into three stages, the parameters were fitted, the basic reproduction numbers of different stages were calculated, and the development trend of epidemic was predicted. Results The R0 decreased dramatically. The R0 of the three stages were 3.684 1(95%CI: 3.106 1-4.048 0), 2.178 8(95%CI: 1.725 8-3.577 6)and 0.362 5(95%CI: 0.349 9-0.367 6), respectively. Due to the traffic control travel and centralized quarantine, the peak of the disease moved forward from April 19 to March 14, 2020. The scale of the epidemic had also been reduced by prevention and control measures. Conclusion The traffic control and centralized quarantine measures implemented in Wuhan were effective for the epidemic control, which can provide reference for other countries.

Key words: Coronavirus disease 2019, SEIAHR infectious disease dynamics model, Traffic control, Centralized quarantine, Basic reproduction number

中图分类号:

R181.1

金新叶,卢珍珍,丁中兴,陈峰,彭志行. 武汉交通管制和集中隔离对新型冠状病毒肺炎疫情影响的动力学模型研究[J]. 山东大学学报 (医学版), 2020, 58(10): 25-31.

JIN Xinye, LU Zhenzhen, DING Zhongxing, CHEN Feng, PENG Zhihang. A dynamic modeling study on the effects of Wuhan traffic control and centralized quarantine measures on COVID-19 epidemic[J]. Journal of Shandong University (Health Sciences), 2020, 58(10): 25-31.

参考文献

[1] Wu F, Zhao S, Yu B, et al. A new coronavirus associated with human respiratory disease in China [J]. Nature, 2020, 579(7798): 265-269.
[2] World Health Organization. Novel Coronavirus(2019-nCoV)Situation Report—22 [EB/OL].(2020-02-11)[2020-02-20].https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200211-sitrep-22-ncov.pdf?sfvrsn=fb6d49b1_2.
[3] 李士雪, 单莹. 新型冠状病毒肺炎研究进展述评[J]. 山东大学学报(医学版), 2020, 58(3): 19-25. LI Shixue, SHAN Ying. Latest research advances on novel coronavirus pneumonia[J]. Journal of Shandong University(Health Sciences), 2020, 58(3): 19-25.
[4] LI Qun, GUAN Xuhua, WU Peng,et al. Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia [J]. N Engl J Med, 2020, 382(13): 1199-1207.
[5] 鞠秀丽. 间充质干细胞治疗新型冠状病毒肺炎的潜在机制和研究进展[J]. 山东大学学报(医学版), 2020, 58(3): 32-37. JU Xiuli. Potential mechanism and research progress of mesenchymal stem cells in the treatment of 2019 novel coronavirus pneumonia [J]. Journal of Shandong University(Health Sciences), 2020, 58(3): 32-37.
[6] 中华人民共和国国家卫生健康委员会. 截止3月19日24时新型冠状病毒肺炎疫情最新情况 [EB/OL].(2020-03-20)[2020-03-21]. http://www.nhc.gov.cn/xcs/yqtb/202003/0fc43d6804b04a4595a2eadd846c0a6e.shtml.
[7] Sun T, Weng D. Estimating the Effects of Asymptomatic and Imported Patients on COVID-19 Epidemic Using Mathematical Modeling [J]. J Med Virol, 2020. doi:10.1002/jmv.25939.
[8] 中华人民共和国国家卫生健康委员会. 新型冠状病毒肺炎诊疗方案(试行第五版)[EB/OL].(2020-02-08)[2020-02-20]. http://www.nhc.gov.cn/yzygj/s7653p/202002/d4b895337e19445f8d728fcaf1e3e13a.shtml.
[9] 高文静, 李立明. 新型冠状病毒肺炎潜伏期或隐性感染者传播研究进展[J]. 中华流行病学杂志, 2020, 41(4): 485-488. GAO Wenjing, LI Liming. Advances on presymptomatic or asymptomatic carrier transmission of COVID-19 [J]. Chinese Journal of Epidemiology, 2020, 41(4): 485-488.
[10] 唐三一, 肖燕妮, 彭志行, 等. 新型冠状病毒肺炎疫情预测建模、数据融合与防控策略分析[J]. 中华流行病学杂志, 2020, 41(4): 480-484. TANG Sanyi, XIAO Yanni, PENG Zhihang, et al. Prediction modeling with data fusion and prevention strategy analysis for the COVID-19 outbreak[J]. Chinese Journal of Epidemiology, 2020, 41(4): 480-484.
[11] Karako K, Song P, Chen Y,et al. Analysis of COVID-19 infection spread in Japan based on stochastic transition model [J]. Biosci Trends, 2020, 14(2): 134-138.
[12] Tang B, Wang X, Li Q, et al. Estimation of the Transmission Risk of the 2019-nCoV and Its Implication for Public Health Interventions [J]. J Clin Med, 2020, 9(2): 462.
[13] Prem K, Liu Y, Russell TW, et al. The effect of control strategies to reduce social mixing on outcomes of the COVID-19 epidemic in Wuhan, China: a modelling study [J]. Lancet Public Health, 2020, 5(5): 261-270.
[14] 黄丽红, 沈思鹏, 余平, 等. 基于动态基本再生数的新型冠状病毒肺炎疫情防控现状评估[J]. 中华流行病学杂志, 2020, 41(4): 466-469. HUANG Lihong, SHEN Sipeng, YU Ping, et al. Dynamic basic reproduction number based evaluation for current prevention and control of COVID-19 outbreak in China [J]. Chinese Journal of Epidemiology, 2020, 41(4): 466-469.
[15] 湖北省卫生健康委员会.湖北省新冠肺炎疫情情况 [EB/OL].(2020-03-20)[2020-03-20]. http://wjw.hubei.gov.cn/fbjd/dtyw/.
[16] He Z. What further should be done to control COVID-19 outbreaks in addition to cases isolation and contact tracing measures? [J]. BMC Med, 2020, 18(1): 80.
[17] He X, Lau EHY, Wu P, et al. Temporal dynamics in viral shedding and transmissibility of COVID-19 [J]. Nat Med, 2020, 26(5): 672-675.
[18] Zhao S, Lin Q, Ran J, et al. Preliminary estimation of the basic reproduction number of novel coronavirus(2019-nCoV)in China, from 2019 to 2020: A data-driven analysis in the early phase of the outbreak [J]. Int J Infect Dis, 2020, 92: 214-217. doi:10.1016/j.ijid.
[19] Wang C, Liu L, Hao X, et al. Evolving Epidemiology and Impact of Non-pharmaceutical Interventions on the Outbreak of Coronavirus Disease 2019 in Wuhan, China [J]. medRxiv, 2020. doi:10.1101/2020.03.03.20030593
[20] van den Driessche P, Watmough J. Reproduction numbers and sub-threshold endemic equilibria for compartmental models of disease transmission [J]. Math Biosci, 2002, 180: 29-48. doi:10.1016/s0025-5564(02)00108-6.
[21] Delurgio S. Forecasting Principles and Applications [M]. City: McGraw-Hill, 1998: 147-167.
[22] 罗成, 许青, 孙霖, 等. SIR模型在成人麻疹爆发及其疫情控制评价中的应用[J]. 山东大学学报(医学版), 2016, 54(9): 87-91. LUO Cheng, XU Qing, SUN Lin, et al. Use of SIR model in evaluation of control measures for adults measles outbreak [J]. Journal of Shandong University(Health Sciences), 2016, 54(9): 87-91.
[23] Hellewell J, Abbott S, Gimma A, et al. Feasibility of controlling COVID-19 outbreaks by isolation of cases and contacts [J]. Lancet Glob Health, 2020, 8(4): 488-496.
[24] Quilty BJ, Clifford S, Flasche S, et al. Effectiveness of airport screening at detecting travellers infected with novel coronavirus(2019-nCoV)[J]. Euro Surveill, 2020, 25(5): 2000080.
[25] Tian H, Li Y, Liu Y. The impact of transmission control measures during the first 50 days of the COVID-19 epidemic in China [J]. medRxiv, 2020, 368(6491): 638-642.
[26] Zhao S, Zhuang Z, Ran J, et al. The association between domestic train transportation and novel coronavirus(2019-nCoV)outbreak in China from 2019 to 2020: A data-driven correlational report [J]. Travel Med Infect Dis, 2020, 33: 101568. doi: 10.1016/j.tmaid.2020.101568.
[27] Tian H, Liu Y, Li Y, et al. An investigation of transmission control measures during the first 50 days of the COVID-19 epidemic in China [J]. Science, 2020, 368(6491): 638-642.
[28] Prem K, Liu Y, Russell TW, et al. The effect of control strategies to reduce social mixing on outcomes of the COVID-19 epidemic in Wuhan, China: a modelling study [J]. Lancet Public Health, 2020, 5(5): 261-270.
[29] Li RY, Pei S, Chen B, et al. Substantial Undocumented Infection Facilitates the Rapid Dissemination of Novel Coronavirus(SARS-CoV-2)[J]. Science, 2020, 368(6490): 489-493.
[30] 中华人民共和国国家卫生健康委员会. [湖北] 集中隔离点逐步投入使用武汉全力攻坚“应收尽收” [EB/OL].(2020-02-09)[2020-04-26]. http://www.nhc.gov.cn/xcs/fkdt/202002/043c8edae79f404c8f768db5e8-a3ff6f.shtml.

相关文章 15

[1]	牛占丛,王彦霞,王晓亚,王晓庆,李亚轻,边竞. 新型冠状病毒肺炎2例报告[J]. 山东大学学报 (医学版), 2020, 58(10): 134-136.
[2]	李焕杰,欧兰香,陈虹,陈健,耿军,高志鹏,王岩,丁兴龙,陈振,朱之炜,刘伦琴,汪运山. 新型冠状病毒IgM-IgG抗体检测试剂盒的制备及对15例患者临床应用初试[J]. 山东大学学报 (医学版), 2020, 58(10): 120-126.
[3]	王彬,布学慧,孔祥亘,张照华,吴谙诏,肖迪,蒋雪梅. 成人37例与儿童10例新型冠状病毒肺炎的临床特点比较[J]. 山东大学学报 (医学版), 2020, 58(10): 112-116.
[4]	王玲,曹海霞,张玲,张文娜,潘艳萍,史颖,张伟,崔峰. 淄博市一起新型冠状病毒肺炎家族聚集性疫情调查分析[J]. 山东大学学报 (医学版), 2020, 58(10): 100-104.
[5]	白尧,陈志军,宋姝璇,贺真,陈保忠,邵中军,刘昆. 西安市一起新型冠状病毒肺炎家族聚集性疫情调查分析[J]. 山东大学学报 (医学版), 2020, 58(10): 95-99.
[6]	张辉, 宋姝璇, 刘继锋, 贺真, 邵中军, 刘昆. 西安市新型冠状病毒肺炎疫情分析[J]. 山东大学学报 (医学版), 2020, 58(10): 89-94.
[7]	刘利利,贾艳,齐畅,朱雨辰,李春雨,佘凯丽,刘廷轩,李秀君. 基于时空统计方法分析温州市2020年1~3月新型冠状病毒肺炎的聚集性分布[J]. 山东大学学报 (医学版), 2020, 58(10): 82-88.
[8]	刘廷轩,齐畅,佘凯丽,贾艳,朱雨辰,李春雨,刘利利,王旭,章志华,李秀君. 河北省新型冠状病毒肺炎流行特征与时空聚集性分析[J]. 山东大学学报 (医学版), 2020, 58(10): 74-81.
[9]	贾艳,李春雨,刘利利,佘凯丽,刘廷轩,朱雨辰,齐畅,张丹丹,王旭,陈恩富,李秀君. 浙江省新型冠状病毒肺炎的流行特征与空间分析[J]. 山东大学学报 (医学版), 2020, 58(10): 66-73.
[10]	齐畅,朱雨辰,李春雨,刘利利,张丹丹,王旭,佘凯丽,陈鸣,康殿民,李秀君. 基于地理加权广义线性模型探索山东省新型冠状病毒肺炎的影响因素[J]. 山东大学学报 (医学版), 2020, 58(10): 53-59.
[11]	佘凯丽,张丹丹,齐畅,刘廷轩,贾艳,朱雨辰,李春雨,刘利利,王旭,苏虹,李秀君. 安徽省新型冠状病毒肺炎流行病学特征及其潜伏期估计[J]. 山东大学学报 (医学版), 2020, 58(10): 44-52.
[12]	李春雨,朱雨辰,齐畅,刘利利,张丹丹,王旭,徐学利,李秀君. 河南省信阳市新型冠状病毒肺炎的流行动态[J]. 山东大学学报 (医学版), 2020, 58(10): 38-43.
[13]	朱雨辰,李春雨,齐畅,王莹,刘利利,张丹丹,王旭,康殿民,李秀君. 基于泊松过程的山东省新型冠状病毒肺炎的再生数估计及流行动态分析[J]. 山东大学学报 (医学版), 2020, 58(10): 32-37.
[14]	徐丽君,刘文辉,刘远,李美霞,罗雷,欧春泉. SEIQCR传染病模型的构建及在广州市新型冠状病毒肺炎公共卫生防控效果评估中的应用[J]. 山东大学学报 (医学版), 2020, 58(10): 20-24.
[15]	李秀君,李新楼,刘昆,赵晓波,马盟,孙博. 地理信息系统在新型冠状病毒肺炎疫情防控中的应用进展述评[J]. 山东大学学报 (医学版), 2020, 58(10): 13-19.

多维度评价

Viewed

Full text

Abstract

Cited

Shared

Discussed