新型冠状病毒肺炎（新冠肺炎）已在全球多个国家和地区蔓延，且不同国家和地区新冠肺炎的传播扩散过程和传染预测模式不尽相同。以美国和中国新冠肺炎公开数据为样本，首先应用广义SEIR模型，对疾病传播参数进行估计。其次，确定基本传染数R0中各参数对疾病的影响，进而基于Anylogic仿真，利用系统动力学模型进行参数敏感性分析，定量表征关键参数对疾病传播的影响。最后，根据分析结果提出针对性的防控干预措施，以中美为例模拟不同防控级别的干预效果。研究发现，广义SEIR模型对中国与美国新冠肺炎传播机制的拟合性较好，保护率、感染率和平均检疫时间对防控疫情影响显著，并可以通过针对性的措施来提高保护率、降低感染率、缩短平均检疫时间。
The coronavirus disease 2019 has spread to a great number of countries and regions around the world, and the spread processes and the infection prediction models of the COVID-19 vary from country to country. Based on the COVID-19 published data from the United States and China, this paper first applies the generalized SEIR model to estimate the disease transmission parameters, then, determines the influence of each parameter in the basic infection number R0 on the disease. Based on the Anylogic simulation, a system dynamics model is used to analyze the parameter sensitivity, and to quantitatively characterize the influence of key parameters on the disease transmission. Finally, according to the analysis results, some targeted prevention and control intervention measures are put forward, with China and the United States as examples to simulate the intervention effects of different prevention and control levels. It is found that the generalized SEIR model fits well with the transmission mechanism of the COVID-19 in China and the United States. The protection rate, the infection rate and the average quarantine time have a significant impact on the prevention and the control of the epidemic. Targeted measures are adopted to improve the protection rate, reduce the infection rate and shorten the average quarantine time.
[1] 盛华雄, 吴琳, 肖长亮. 新冠肺炎疫情传播建模分析与预测[J]. 系统仿真学报, 2020, 32(5):759-766.
[2] 梅文娟, 刘震, 朱静怡, 等. 新冠肺炎疫情极限IR实时预测模型[J]. 电子科技大学学报, 2020, 49(3):362-368.
[3] 张艳霞, 李进. 基于SIR模型的新冠肺炎疫情传播预测分析[J]. 安徽工业大学学报(自然科学版), 2020, 37(1):94-101.
[4] 汪剑眉, 李钢. 新冠肺炎非均匀感染力传播模型与干预分析[J]. 电子科技大学学报, 2020, 49(3):392-398.
[5] 范如国, 王奕博, 罗明, 等. 基于SEIR的新冠肺炎传播模型及拐点预测分析[J]. 电子科技大学学报, 2020, 49(3):369-374.
[6] 游光荣, 游翰霖, 赵得智, 等. 新冠肺炎疫情传播模型及防控干预措施的因果分析评估[J]. 科技导报, 2020, 38(6):90-96.
[7] 张宇, 田万利, 吴忠广, 等. 基于改进SEIR模型的新冠肺炎疫情沿交通线路传播机制[J]. 交通运输工程学报, 2020, 20(3):150-158.
[8] Yang Z, Zeng Z, Wang K, et al. Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions[J]. Journal Thoracic Disease. 2020, 12(3):165-174.
[9] Guerrero-Nancuante C, Manríquez R. An epidemiological forecast of COVID-19 in Chile based on the generalized SEIR model and the concept of recovered[J]. Medwave, 2020, 20(4):1-15.
[10] 邵年, 钟敏, 程晋, 等. 基于FUDAN-CCDC模型对新冠肺炎的建模和确诊人数的预测[J]. 数学建模及其应用, 2020, 9(1):29-32.
[11] 刘红亮, 贾洪文, 王雁, 等. 新型冠状病毒肺炎初期传播规模的系统动力学模型估计方法及评价——以甘肃省为例的研究[J]. 电子科技大学学报(社科版), 2020, 22(3):36-45.
[12] 崔景安, 吕金隆, 郭松柏, 等. 新发传染病动力学模型——应用于2019新冠肺炎传播分析[J]. 应用数学学报, 2020, 43(2):147-155.
[13] 张原, 尤翀, 蔡振豪, 等. 新冠肺炎(COVID-19)新型随机传播动力学模型及应用[J]. 应用数学学报, 2020, 43(2):440-451.
[14] 贾仁安. 系统动力学反馈动态性复杂分析[M]. 北京:高等教育出版社, 2002:78-96.
[15] 张争艳, 周志强, 董敏.基于系统动力学的供应链牛鞭效应仿真研究[J]. 物流技术, 2014, 33(2):164-167.
[16] 周涛, 刘权辉, 杨紫陌, 等. 新型冠状病毒肺炎基本再生数的初步预测[J]. 中国循证医学杂志, 2020, 20(3):359-364.
