首页|资源约束下基于改进传统SEIR模型的传染病传播网络建模研究

资源约束下基于改进传统SEIR模型的传染病传播网络建模研究

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为了进一步厘清传染性疾病的传播脉络,针对传染病突发性、意外性、群体性等特征,提出了一种基于元胞自动机,考虑人口数量变动且应急救援物资数量影响动态接触率的SEIRD疫情传播网络模型,以克服传统动力学传播模型SIR和SEIR的不足.首先,通过数据分析,构建应急物资数量及人口密度相关的接触率函数,建立动态SEIRD传染病传播模型.其次,基于最小二乘估计最优化算法及迭代的四阶龙格-库塔法对模型中的未知参数进行估计.再次,利用LSODA算法进行隐式数值积分运算来求解常微分方程组的数值解,通过求解出的参数值建立元胞演化规则并将传染病的传播趋势以基于元胞自动机的方式进行展示.最后,采用 2020 年 2月 1日—4月 1日中国湖北省新型冠状病毒肺炎感染数据,对传染性疾病传播网络进行数据预测及模型验证.通过设置模型相关参数,展示传染病传播过程并将其传播趋势可视化,并对模型参数进行灵敏度分析.预测结果显示,动态SEIRD模型预测结果有效.
Modeling research of the propagation network of infectious diseases based on improved traditional SEIR model under resource constraints
In order to further clarify the transmission context of infectious diseases,a SEIRD epidemic transmission network model based on cellular automata,considering the changes in population size and the impact of emergency relief supplies on dynamic contact rate,was proposed to overcome the shortcomings of traditional dynamic transmission models SIR And SEIR in view of the sudden,unexpected and group characteristics of infectious diseases.Firstly,through data analysis,the contact rate function related to the quantity of emergency supplies and population density was constructed,and the dynamic SEIRD infectious disease transmission model was established.Secondly,the unknown parameters in the model were estimated based on the least square estimation optimization algorithm and the iterative fourth-order Runge-Kutta method.Thirdly,LSODA algorithm was used to carry out implicit numerical integration operation to solve the numerical solution of the ordinary differential equations,and cell evolution rules were established through the solved parameter values,and the transmission trend of infectious diseases was displayed in a way based on cellular automata.Finally,the data of COVID-19 in Hubei Province,China from February 1 to April 1,2020 were used to predict the transmission network of infectious diseases and verify the model.By setting the relevant parameters of the model,the transmission process of infectious diseases was displayed and its transmission trend was visualized,and the sensitivity of the model parameters was analyzed.The prediction results show that the dynamic SEIRD model is effective.

propagation networkemergency materialscellular automatacomputer simulationtransmission dynamics

徐梦婷、刘勤明、何基伟

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上海理工大学管理学院,上海 200093

传播网络 应急物资 元胞自动机 计算机模拟 传播动力学

2024

上海理工大学学报
上海理工大学

上海理工大学学报

北大核心
影响因子:0.767
ISSN:1007-6735
年,卷(期):2024.46(6)