摘要
针对新冠肺炎疫情的发展呈现阶段性变化的特征,提出一种以阶段划分为基础,各阶段分别进行模型拟合的分析方法。在传统SIR(Susceptible-Infected-Recovered)模型的基础上考虑"未确诊感染者"、"治愈者"和"死亡者",构建阶段性SIR-F模型。结合增长态势识别、阶段划分、防控措施影响分析的方法,使用Python实现模拟仿真。实验证明,分阶段的拟合方法能比较准确地刻画疫情数据随时间的变化规律,以及防控措施产生的影响。
According to the characteristics of the development of the COVID-19 epidemic,this paper proposes an analysis method of model fitting based on the division of the stages.Based on the classic SIR model,the staged SIR-F model which considers the“undiagnosed infected”,“recovered”and“fatal”was built.The model was combined with the epidemic growth identification,stage division,impact analysis of prevention and control measures,using Python to achieve simulation.The simulation experiments prove the staged fitting method can more accurately describe the changes of epidemic data over time and the impact of prevention and control measures.
作者
凡友荣
杨涛
孔华锋
Fan Yourong;Yang Tao;Kong Huafeng(Third Research Institute of Ministry of Public Security,Shanghai 201204,China;Wuhan Business University,Wuhan 430056,Hubei,China)
出处
《计算机应用与软件》
北大核心
2020年第11期51-56,62,共7页
Computer Applications and Software
基金
国家重点研发计划项目(2018YFC0806903)
公安部科技强警基础工作专项项目(2019GABJC20)。
