您的位置:山东大学 -> 科技期刊社 -> 《山东大学学报(医学版)》

山东大学学报 (医学版) ›› 2020, Vol. 58 ›› Issue (10): 32-37.doi: 10.6040/j.issn.1671-7554.0.2020.0683

• • 上一篇    下一篇

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

朱雨辰1,李春雨1,齐畅1,王莹1,刘利利1,张丹丹1,王旭1,康殿民2,李秀君1   

  1. 1. 山东大学齐鲁医学院公共卫生学院生物统计学系, 山东 济南 250012;2. 山东省疾病预防控制中心, 山东 济南 250014
  • 发布日期:2020-10-08
  • 通讯作者: 李秀君. E-mail:xjli@sdu.edu.cn 康殿民. E-mail:dmkang66@163.com
  • 基金资助:
    国家自然科学基金(81673238);山东大学新冠肺炎应急攻关科研专项(2020XGC01);国家重点研发计划(2019YFC1200500,2019YFC1200502);山东省重大科技创新工程(2020SFXGFY02-1)

Reproduction number estimation and epidemic analysis of coronavirus disease 2019 in Shandong Province based on Poisson process

ZHU Yuchen1, LI Chunyu1, QI Chang1, WANG Ying1, LIU Lili1, ZHANG Dandan1, WANG Xu1, KANG Dianmin2, LI Xiujun1   

  1. 1. Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, Shandong, China;
    2. Shandong Center for Disease Control and Prevention, Jinan 250014, Shandong, China
  • Published:2020-10-08

摘要: 目的 探讨山东省新型冠状病毒肺炎(COVID-19)的流行动态,为未来预防和控制COVID-19二次暴发以及其他新发传染病的暴发提供科学依据。 方法 收集山东省卫生健康委员会报告的559例COVID-19确诊病例信息,确定病例的感染日期,基于泊松过程建立传播模型并计算山东省COVID-19疫情期间的基本再生数和瞬时再生数,并对比分析基于顺序贝叶斯和时间-依赖方法的瞬时再生数估计结果。 结果 确诊病例的发病日期与被报告日期的天数之差大致服从威布尔分布。山东省COVID-19开始暴发时,基本再生数R0=2.64(95%CI:1.37~4.51),瞬时再生数随着时间的推移,大致呈现逐渐下降趋势,且3种计算方法的结果均显示出此种趋势。 结论 经过防控措施的干预后,山东省COVID-19的本地流行已经基本结束,但仍需提高警惕,防止疫情二次暴发。

关键词: 新型冠状病毒肺炎, 基本再生数, 瞬时再生数, 泊松过程, 贝叶斯推断

Abstract: Objective To explore the epidemic dynamics of coronavirus disease 2019(COVID-19)in Shandong Province, and to provide a scientific basis for the future prevention and control of new outbreaks of COVID-19 and other emerging infectious diseases. Methods After collecting the information of 559 confirmed cases with COVID-19 reported by the Shandong Provincial Health Commission and determining the infection date of the cases, a propagation model was established based on the Poisson process and the basic reproduction number and instantaneous reproduction number were calculated during the COVID-19 epidemic in Shandong Province. The results obtained by calculating the instantaneous reproduction numbers based on sequential Bayesian and time-dependent methods were compared. Results The difference between the date of onset of a confirmed case and the date when it was reported generally followed the Weibull distribution. When the COVID-19 outbreak started in Shandong Province, the basic reproduction number(R0)was 2.64(95%CI:1.37-4.51), and the instantaneous reproduction number showed a gradually downward trend with time. Three calculation methods all showed the same trend. Conclusion After the intervention of prevention and control measures, the local epidemic of COVID-19 in Shandong Province has basically ended, but the constant vigilance is necessary in order to prevent the second outbreak of the epidemic.

Key words: Coronavirus pneumonia disease 2019, Basic reproductive number, Instantaneous reproduction number, Poisson process, Bayesian inference

中图分类号: 

  • R183
[1] Guan WJ, Ni ZY, Hu Y, et al. Clinical characteristics of coronavirus disease 2019 in China[J]. N Engl J Med, 2020, 382(18): 1708-1720.
[2] 中国疾病预防控制中心新型冠状病毒肺炎应急响应机制流行病学组. 新型冠状病毒肺炎流行病学特征分析[J]. 中华流行病学杂志, 2020, 41(2): 145-151. Team TNCPERE. The epidemiological characteristics of an outbreak of 2019 novel coronavirus diseases(COVID-19)in China[J]. Chinese Journal of Epidemiology, 2020, 41(2): 145-151.
[3] 中华预防医学会新型冠状病毒肺炎防控专家组. 关于疫情应急处置阶段转入流行高峰持续防控阶段对策的思考 [J]. 中华流行病学杂志, 2020, 41(3): 297-300. Special Expert Group for Control of the Epidemic of Novel Coronavirus Pneumonia of the Chinese Preventive Medicine. Consideration on the strategies during epidemic stage changing from emergency response to continuous prevention and control[J].Chinese Journal of Epidemiology, 2020, 41(3): 297-300.
[4] 周生余,王春亭,张伟,等. 山东省新型冠状病毒肺炎患者537例临床特征与救治效果[J]. 山东大学学报(医学版), 2020, 58(3): 44-51. ZHOU Shengyu, WANG Chunting, ZHANG Wei, et al. Clinical characteristics and treatment effect of 537 cases of novel coronavirus pneumonia in Shandong Province[J]. Journal of Shandong University(Health Sciences), 2020, 58(3): 44-51.
[5] Tu W, Tang H, Chen F, et al. Epidemic update and risk assessment of 2019 novel coronavirus — China, January 28, 2020[J]. CCDCW, 2020, 2(6): 83-86.
[6] 山东省卫生健康委员会. 2020年3月11日0时至12时山东省新型冠状病毒肺炎疫情情况[EB/OL].(2020-03-11)[2020-04-24]. http://wsjkw.shandong.gov.cn/ztzl/rdzt/qlzhfkgz/tzgg/202003/t20200311_3046408.html.
[7] Su Y, Gelman A, Yajima M. Multiple imputation with diagnostics(mi)in R: opening windows into the black box[J]. J Stat Softw, 2011, 45(2): 1-31.
[8] Buuren S, Groothuis-Oudshoorn K. Mice: multivariate imputation by chained equations in R[J]. J Stat Softw, 2011, 45(3): 1-67.
[9] Honaker J, King G, Blackwell M. Amelia II: a program for missing data[J]. J Stat Softw, 2011, 45(7): 1-47.
[10] Dye C. Infectious diseases of humans: dynamics and control by R.M. Anderson and R.M. May[J]. Trends in Ecology & Evolution, 1991, 6(10): 340-341.
[11] Lipsitch M, Cohen T, Cooper B, et al. Transmission dynamics and control of severe acute respiratory syndrome[J]. Science, 2003, 300(5627): 1966-1970.
[12] White LF, Pagano M. A likelihood-based method for real-time estimation of the serial interval and reproductive number of an epidemic[J].Stat Med, 2008, 27(16): 2999-3016.
[13] Cori A, Ferguson NM, Fraser C, et al. A new framework and software to estimate time-varying reproduction numbers during epidemics[J].Am J Epidemiol, 2013, 178(9): 1505-1512.
[14] Nishiura H, Linton NM, Akhmetzhanov AR. Serial interval of novel coronavirus(COVID-19)infections [J]. Int J Infect Dis, 2020, 93: 284-286. doi:10.1016/j.ijid.2020.02.060.
[15] Du Z, Xu X, Wu Y, et al. Serial interval of COVID-19 among publicly reported confirmed cases[J]. Emerg Infect Dis, 2020, 26(6): 1341-1343.
[16] Bettencourt LMA, Ribeiro RM. Real time Bayesian estimation of the epidemic potential of emerging infectious diseases[J]. PLoS One, 2008, 3(5): e2185. doi: 10.1371/journal.pone.0002185.
[17] Wallinga J, Teunis P. Different epidemic curves for severe acute respiratory syndrome reveal similar impacts of control measures[J]. Am J Epidemiol, 2004, 160(6): 509-516.
[18] Mahase E. China coronavirus: what do we know so far? [J]. BMJ, 2020, 368: m308. doi: 10.1136/bmj.m308.
[19] Wu JT, Leung K, Leung GM. Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study[J].The Lancet, 2020, 395(10225): 689-697.
[20] Backer JA, Klinkenberg D, Wallinga J. Incubation period of 2019 novel coronavirus(2019-nCoV)infections among travellers from Wuhan, China, 20-28 January 2020[J]. Euro Surveill, 2020, 25(5): 2000062. doi: 10.2807/1560-7917.ES.2020.25.5.2000062.
[21] Lauer SA, Grantz KH, Bi Q, et al. The incubation period of coronavirus disease 2019(COVID-19)from publicly reported confirmed cases: estimation and application[J].Ann Intern Med, 2020, 172(9): 577-582.
[22] Ki M, Task Force for 2019-nCoV. Epidemiologic characteristics of early cases with 2019 novel coronavirus(2019-nCoV)disease in Korea [J].Epidemiol Health, 2020, 42: e2020007. doi: 10.4178/epih.e2020007.
[23] Linton NM, Kobayashi T, Yang Y, et al. Incubation period and other epidemiological characteristics of 2019 novel coronavirus infections with right truncation: a statistical analysis of publicly available case data[J]. J Clin Med, 2020, 9(2): 538. doi: 10.3390/jcm9020538.
[24] 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.
[25] Lessler J, Reich NG, Cummings DAT, et al. Outbreak of 2009 pandemic influenza A(H1N1)at a New York City school[J]. N Engl J Med, 2009, 361(27): 2628-2636.
[26] Cauchemez S, Boëlle PY, Thomas G, et al. Estimating in real time the efficacy of measures to control emerging communicable diseases[J]. Am J Epidemiol, 2006, 164(6): 591-597.
[27] Cauchemez S, Bhattarai A, Marchbanks TL, et al. Role of social networks in shaping disease transmission during a community outbreak of 2009 H1N1 pandemic influenza[J]. Proc Natl Acad Sci U S A, 2011, 108(7): 2825-2830.
[1] 王玉淼,崔晓霈,张红雨. 高龄老年新型冠状病毒肺炎患者应用抗凝治疗的短期疗效和安全性[J]. 山东大学学报 (医学版), 2024, 62(12): 21-31.
[2] 王园园,孙云. 合并新型冠状病毒肺炎的维持性血液透析患者死亡危险因素[J]. 山东大学学报 (医学版), 2023, 61(11): 68-73.
[3] 曹义海. 血管生成在疾病治疗中的应用与展望[J]. 山东大学学报 (医学版), 2021, 59(9): 9-14.
[4] 杨璇,李岩志,马伟,贾崇奇. 基于两样本孟德尔随机化的肺功能与新型冠状病毒肺炎病死风险的因果关系[J]. 山东大学学报 (医学版), 2021, 59(7): 104-111.
[5] 周溪,黄霂晗,任玉洁,邱洋. 新型冠状病毒感染与天然免疫及炎症反应[J]. 山东大学学报 (医学版), 2021, 59(5): 15-21.
[6] 于莹,张功,刘晶,颜世童,韩涛,黄海量. 基于网络药理学和分子对接方法探析黄芪预防新型冠状病毒肺炎的潜在作用机制[J]. 山东大学学报 (医学版), 2021, 59(4): 6-16.
[7] 任敏敏,王广梅,张丽,杨瑶瑶,封丹珺. 335名抗疫一线护理人员心理弹性对共情疲劳的影响[J]. 山东大学学报 (医学版), 2021, 59(2): 88-94.
[8] 程召平,段艳华,姚金坤,李岩,顾慧,袁宪顺,刘斌,毕万利,宋照亮,聂佩,陈月芹,孙占国,刘善平,王鲁光,唐忠仁,魏相磊,董亮,王春亭,王锡明. 105例新型冠状病毒肺炎胸部CT影像学特征——山东省多中心回顾性分析[J]. 山东大学学报 (医学版), 2020, 58(5): 38-45.
[9] 袁勇贵,李磊,沈仲夏,陈刚,吴义高,岳莹莹. 新型冠状病毒肺炎疫情下精神障碍诊疗的防控策略[J]. 山东大学学报 (医学版), 2020, 58(4): 1-6.
[10] 常彩云,于秋燕,赵小冬,王芳,李伟,阮师漫,耿兴义. 济南市首例新型冠状病毒肺炎病例及其相关家庭聚集性疫情分析[J]. 山东大学学报 (医学版), 2020, 58(4): 7-11.
[11] 杨丽,李战,刘晓雪,焦海涛,周林,刘庆皆,刘铁诚,耿兴义. 济南市新型冠状病毒肺炎密切接触者隔离医学观察情况分析与评价[J]. 山东大学学报 (医学版), 2020, 58(4): 12-16.
[12] 乔宇,崔亮亮,李帅,王峰,阮师漫,景一鸣,刘翀. 智能问答机器人系统研发及应用研究——以济南市新型冠状病毒肺炎疫情处置应对为例[J]. 山东大学学报 (医学版), 2020, 58(4): 17-22.
[13] 李新蕊,耿兴义,赵小冬,刘岚铮,王蔚茹,崔亮亮,李战,常彩云,阮师漫. 济南市47例新型冠状病毒肺炎疫情流行综合分析[J]. 山东大学学报 (医学版), 2020, 58(4): 23-27.
[14] 赵怀龙,吕燕,赵红,赵宝添,韩莹,杨国樑,王春荣,关恒云,刘辉,刘岚铮. 济南市47例新型冠状病毒肺炎患者取样部位对核酸检测结果的影响[J]. 山东大学学报 (医学版), 2020, 58(4): 28-31.
[15] 李新蕊,耿兴义,王蔚茹,赵小冬,刘岚铮,张晓菲,吕翠霞,常彩云,李战,崔亮亮,阮师漫. 37例新型冠状病毒肺炎聚集性思考[J]. 山东大学学报 (医学版), 2020, 58(4): 32-40.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!