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新冠肺炎疫情演化情境下应急物资需求预测研究
引用本文:李 清,苏 强. 新冠肺炎疫情演化情境下应急物资需求预测研究[J]. 华中师范大学学报(自然科学版), 2021, 55(4): 661-670
作者姓名:李 清  苏 强
作者单位:上海政法学院经济管理学院,上海200333;同济大学经济与管理学院,上海200092
基金项目:国家自然科学基金;国家自然科学基金;上海政法学院青年科研基金
摘    要:根据新冠肺炎疫情演化过程中代表性事件、患病感染人数和疫情信息状态等特征,将其分为产生—爆发—高峰—衰退四个阶段.考虑湖北和除湖北以外的全国其他省市,绘制四个阶段下这两个区域每日新增患病感染人数图,总结每个阶段的特征.在假设患病感染者传染概率保持不变的前提下,基于传染病模型建立医疗物资时变需求模型;在患病感染者传染概率不...

关 键 词:新冠肺炎  传染病模型  贝叶斯序贯决策  配置决策
收稿时间:2021-08-09

Research on the medical allocation decisions in the context of COVID-19 epidemic evolution
LI Qing,SU Qiang. Research on the medical allocation decisions in the context of COVID-19 epidemic evolution[J]. Journal of Central China Normal University(Natural Sciences), 2021, 55(4): 661-670
Authors:LI Qing  SU Qiang
Affiliation:(1.School of Economics and Management, Shanghai University of Political Science and Law, Shanghai, 200333, China; 2.School of Economics and Management, Tongji University, Shanghai 200092, China)
Abstract:According to the characteristics of information, number of infected persons and representative events during the evolution of COVID-19 epidemic, it is divided into four stages emergence, outbreak, peak, and decline.Taking Hubei Province and other provinces of China except Hubei Province into consideration, the daily number of new infected person in these two parts under four stages was plotted.The characteristics of the four stage were summarized.Supposing that the transmission probability of infected persons remained unchanged, a time-varying demand model for medical supplies was established based on the SEIR epidemic model. Besides, considering that the transmission probability of infected persons is updated continuously, the Bayesian sequential decision model was adopted to make material allocation decision. The differences of material allocation decision between the two models were compared, and the influences of different models on the results were anlyzed. The errors of the Bayesian model and the traditional model are all smaller than those of the time-varing model for both the predicted and actual daily number of infections.
Keywords:COVID-19   SEIR epidemic model   Bayesian sequential decision making   material allocation decision  
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