关于新型冠状病毒肺炎一类基于CCDC统计数据的随机时滞动力学模型  

Some novel statistical time delay dynamic model by statistics data from CCDC on novel coronavirus pneumonia

作　　者：邵年 陈瑜 程晋[1,3] 陈文斌[1,3] SHAO Nian;CHEN Yu;CHENG Jin;CHEN Wen-bin(School of Mathematical Science,Fudan University,Shanghai 200433,China;School of Mathematics,Shanghai University of Finance and Economics,Shanghai 200433,China;Shanghai Key Laboratory for Contemporary Applied Mathematics,Fudan University,Shanghai 200433,China)

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

出　　处：《控制理论与应用》2020年第4期697-704,共8页Control Theory & Applications

基　　金：国家自然科学基金项目(11671098,91630309,11971121);高等学校学科创新引智计划项目(B08018)资助.

摘　　要：科学地预测新型冠状病毒肺炎疫情发展趋势对疫情防控至关重要.本文对中国疾病预防控制中心(CCDC)发布[1]的数据进行了分析,给出了关于新型冠状病毒肺炎的一些可能的统计模型:传播链中连续病例的发病时间间隔分布、感染至发病的时间间隔分布和发病至住院的间隔时间3个分布,并形成了感染至确诊的时间间隔分布表达.结合CCDC统计数据和程晋团队的时滞动力学模型(TDD-NCP模型),本文发展了新的随机时滞动力学模型(Fudan-CCDC模型),并给出了参数反演结果与疫情分析.Scientific prediction of the development trend of the novel coronavirus pneumonia epidemic is very important for epidemic prevention and control.This paper analyzes the data released by the Centers for Disease Control and Prevention(CCDC)and provides several statistical models of novel coronavirus pneumonia including the explicit probability distributions on:the time interval between infection and illness onset;the interval between two illness onsets in successive cases in a transmission chain;the time from illness onset to hospitalization.As a result,the distribution of time delay from infection to hospitalization can be formulated.Combining the time-delay model(TDD-NCP)proposed by Jin Cheng’s group with the statistical data from CCDC,we propose a statistical time-delay model(Fudan-CCDC)and present some numerical results on parameter identification and outbreak predictions.

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 疫情预测 

分 类 号：R563.1[医药卫生—呼吸系统] O175[医药卫生—内科学]