基于时滞动力学模型对钻石公主号邮轮疫情的分析  

Modeling the Trend of Outbreak of COVID-19 in the Diamond Princess Cruise Ship Based on a Time-delay Dynamic System

作　　者：罗心悦 邵年 程晋[2,3] 陈文斌 LUO Xinyue;SHAO Nian;CHENG Jin;CHEN Wenbin(School of Mathematics,Shanghai University of Finance and Economics,Shanghai 200433,China;School of Mathematical Sciences,Fudan University,Shanghai 200433,China;Shanghai Key Laboratory for Contemporary Applied Mathematics,Shanghai 200433,China)

机构地区：[1]上海财经大学数学学院,上海200433 [2]复旦大学数学科学学院,上海200433 [3]上海市现代应用数学重点实验室,上海200433

出　　处：《数学建模及其应用》2020年第1期15-22,F0003,共9页Mathematical Modeling and Its Applications

基　　金：国家自然科学基金(11671098,91630309),国家自然科学基金(11971121)资助;111计划(B08018)资助。

摘　　要：2019年末以来,新型冠状病毒肺炎迅速蔓延的疫情引发了全球关注.文献[5-6]提出了一类时滞动力学系统的新冠肺炎传播模型用以描述疫情的发展趋势.文献[7]在此基础上,结合CCDC统计数据,提出了一类基于CCDC统计数据的随机时滞动力学模型.本文将使用以上两类模型研究分析"钻石公主号"邮轮的疫情发展.基于日本厚生劳动省公布的数据,本文准确反演出模型参数,进而有效模拟当前疫情的发展,并预测疫情未来的趋势,发现在疫情爆发初期基本再生数R0(t)较大,而后随着防控措施加强而逐渐减小;约在2月下旬,累计确诊人数增长速度放缓,在3月上旬,累计确诊人数趋于稳定,即无新增确诊人数,疫情得到有效控制;最终累计确诊人数对隔离率变化敏感,隔离率升高,最终累计确诊人数将有显著下降.针对传染率较高、隔离率较低的问题,本文建议日本政府进一步加强防控措施,抑制疫情的大规模爆发.COVID-19 has been impacting on the whole world critically and constantly since late December 2019.Rapidly increasing infections in the Diamond Princess cruise ship raised intense worldwide attention.In this paper,we employ TDD-NCP model and FUDAN-CCDC model to describe and analyze the outbreak of COVID-19 in the Diamond Princess cruise ship based on the cumulative number of confirmed and cured cases reported by Health,Labour,and Welfare,Japan.Numerical simulations indicate that the parameters in models are identified accurately,and the trend of the outbreak of COVID-19 is effectively simulated.We find that the reproductive number R0(t)was large in the early stage of the outbreak,and then gradually decreased with preventive measures involved.Simultaneously,the prediction for total infections is presented for reference.Finally,we would deem that effective interventions and measures are supposed to taken to avoid the large-scale outbreak of COVID-19.

关 键 词：新型冠状病毒肺炎 时滞动力学模型 随机时滞动力学模型 参数反演 疫情预测 

分 类 号：O29[理学—应用数学]