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

DOI编号  10.7641/CTA.2020.00069
2020,37(4):697-704

 作者 单位 E-mail 邵年 复旦大学数学科学学院 16307130024@fudan.edu.cn 陈瑜 上海财经大学数学学院 程晋 复旦大学数学科学学院 陈文斌 复旦大学数学科学学院 wbchen@fudan.edu.cn

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)发布的\cite{cdc}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三个分布, 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), 我们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 probability 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 elay 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.