新冠后疫情时代复学风险评估的不确定性量化分析

doi:10.16182/j.issn1004731x.joss.20-0679

系统仿真学报 ›› 2021, Vol. 33 ›› Issue (1): 13-23.doi: 10.16182/j.issn1004731x.joss.20-0679

新冠后疫情时代复学风险评估的不确定性量化分析

李海滨^1,2, 王佳亮², 李海燕³

1.内蒙古工业大学工程训练教学部,内蒙古呼和浩特 010051;
2.内蒙古自治区生命数据统计分析理论与神经网络建模重点实验室,内蒙古工业大学理学院,内蒙古呼和浩特 010051;
3.内蒙古医科大学第一附属医院内分泌科,内蒙古呼和浩特 010010

收稿日期:2020-09-09 修回日期:2020-11-10 发布日期:2021-01-18
作者简介:李海滨(1973-),男,蒙古族,博士,教授,研究方向为结构不确定性分析与量化、神经网络计算、六维力传感器设计等。E-mail：lhb@imut.edu.cn
基金资助:
国家自然科学基金(11962021)

Uncertainty Quantitative Analysis in Risk Assessment of Returning to School in the Post-COVID-19 Era

Li haibin^1,2, Wang jialiang², Li haiyan³

1. Engineering Training Center of Inner Mongolia University of Technology, Hohhot 010051, China;
2. Inner Mongolia Key Laboratory of Statistical Analysis Theory for Life Data and Neural Network Modeling, College of Sciences, Inner Mongolia University of Technology, Hohhot 010051, China;
3. Department of Endocrinology the First Affiliated Hospital of Inner Mongolia Medical University, Hohhot 010010, China

Received:2020-09-09 Revised:2020-11-10 Published:2021-01-18

摘要/Abstract

摘要： 针对疫情后的复工复产复学问题,以返校复学中的疫情传播为例,进行了风险评估量化研究。以易感态个体从感染到隔离转化全过程的运动轨迹描述为线索,建立了一种适合于风险评估的流行病动力学模型。在模型参数量化的基础上,对复学风险指标的感染人数进行了量化。根据模型参数的取值特性,将感染人数作为离散型随机变量函数,通过动力学仿真计算,结合概率守恒原理给出了感染人数的概率分布,由此实现了复学风险的不确定性量化。算例仿真表明所提方法在复学风险评估中的可行性,可为复工复产复学决策提供理论依据。

关键词: 复学, 风险评估, 新冠病毒, 疫情, 动力学建模

Abstract: After the epidemic, taking the spread of the epidemic in returning to school as an example, a quantitative risk assessment study is conducted. Taking the activity trajectory description of the whole process of susceptible individuals from infection to isolation as a clue, an epidemiological model for risk assessment is established. The number of infected persons in the risk indicators of returning to school is quantified based on the quantified model parameters. According to the value characteristics of the parameters, the number of infected persons is taken as a function of discrete random variables. The probability distribution of the infected population is given through dynamic simulation calculation, combined with the principle of conservation of probability, and the uncertainty quantification of the risk of returning to school is realized. The simulation results show the feasibility of the method in the risk assessment of school resumption, and can provide a theoretical basis for the decision to resume work and school.

Key words: return to school, risk assessment, COVID-19, epidemic situation, dynamic modeling

中图分类号:

TP391.9

李海滨, 王佳亮, 李海燕. 新冠后疫情时代复学风险评估的不确定性量化分析[J]. 系统仿真学报, 2021, 33(1): 13-23.

Li haibin, Wang jialiang, Li haiyan. Uncertainty Quantitative Analysis in Risk Assessment of Returning to School in the Post-COVID-19 Era[J]. Journal of System Simulation, 2021, 33(1): 13-23.

参考文献

[1] 王庆德, 吴欣哲. 大数据助力疫情防控和复工复产-基于新冠肺炎疫情[J]. 中国物价, 2020(5): 9-11.
Wang Qingde, Wu Xinzhe.Big Data Helps Prevent and Control the Epidemic, Resume Work and Resume Production-based on the New Crown Pneumonia Epidemic[J]. China Prices, 2020(5): 9-11.
[2] 李俊, 彭艳英, 潘胜琼. 新型冠状病毒肺炎疫情下上海市某工业园区企业复工复产防疫工作策略与思考[J]. 职业卫生与应急救援, 2020, 38(5): 539-542.
Li Jun, Peng Yanying, Pan Shengqiong.Thinking on Epidemic Prevention Strategy for Resumption of Production of Enterprises in an Industrial Park in Shanghai Under Epidemic of COVID-2019[J]. Occupational Health and Emergency Rescue, 2020, 38(5): 539-542.
[3] 何志辉, 宋铁, 黄琼, 等. 快速风险评估方法在城市防控新冠肺炎疫情工作的探索与运用-以温州市为例[J]. 华南预防医学, 2020, 46(2): 101-105.
He Zhihui, Song Tie, Huang Qiong, et al.Exploration and Application of Rapid Risk Assessment Method in Prevention and Control of COVID-19 in Urban Areas: A Case Study based on Data of Wenzhou[J]. South China Preventive Medicine, 2020, 46(2): 101-105.
[4] 刘勇, 杨东阳, 董冠鹏, 等. 河南省新冠肺炎疫情时空扩散特征与人口流动风险评估——基于1243例病例报告的分析[J]. 经济地理, 2020, 40(3): 24-32.
Liu Yong, Yang Dongyang, Dong Guanpeng, et al.The Spatio-Temporal Spread Characteristics of 2019 Novel Coronavirus Pneumonia and Risk Assessment Based on Population Movement in Henan Province: Analysis of 1243 Individual Case Reports[J]. Economic Geography, 2020, 40(3): 24-32.
[5] 张旭宇, 弥磊鹏. 新冠肺炎疫情期间某驰援武汉医疗队感染风险管理实践[J]. 中国感染控制杂志, 2020, 19(3): 267-270.
Zhang Xuyu, Mi Leipeng.Management Practice of Infection Risk of A Medical Team Which Rushed to the Rescue of Wuhan during COVID-19 Epidemic Period[J]. Chinese Journal of Infection Control, 2020, 19(3): 267-270.
[6] 刘武忠, 王祖兵, 张雪涛, 等. 复工复产用人单位新型冠状病毒肺炎关键风险点评估[J]. 职业卫生与应急救援, 2020, 38(2): 138-141.
Liu Wuzhong, Wang Zubing, Zhang Xuetao, et al.Evaluation on Key Risk Points for Prevention of COVID-2019 by Employers During Resumption of Work and Production[J]. Occupational Health and Emergency Rescue, 2020, 38(2): 138-141.
[7] Kim S, Kim Y J, Peck K R, et al.School Opening Delay Effect on Transmission Dynamics of Coronavirus Disease 2019 in Korea: Based on Mathematical Modeling and Simulation Study[J]. Journal of Korean medical science (S1598-6357), 2020, 35(13): e143.
[8] 王霞, 唐三一, 陈勇, 等. 新型冠状病毒肺炎疫情下武汉及周边地区何时复工? 数据驱动的网络模型分析[J]. 中国科学(数学), 2020, 50(7): 969-978. doi: 10.1360/SSM-2020-0037.
Wang Xia, Tang Sanyi, Chen Yong, et al.When Will be the Resumption of Work in Wuhan and its Surrounding Areas During COVID-19 Epidemic? A Data-driven Network Modeling Analysis[J]. Sci Sin Math, 2020, 50(7): 969-978, doi: 10.1360/SSM-2020-0037.
[9] 郑宇军, 吴晨昕, 陈恩富, 等. 新冠肺炎疫情条件下的企业复工复产优化规划方法[J/OL]. 运筹学学报[2020-03-17].http://kns.cnki.net/kcms/detail/31.1732.O1.20200416.0857.004.html.
Zheng Yujun, Wu Chenxin, Chen Enfu, et al. An Optimization Method for Production Resumption Planning Under COVID-19 Epidemic[J/OL]. Journal of Operations Research[2020-03-17]. http://kns.cnki.net/ kcms/detail/31.1732.O1.20200416.0857.004.html.
[10] 李海滨. 基于社会分工的流行病动力学建模与仿真研究[J]. 系统仿真学报, 2020, 32(5): 745-758.
Li Haibin.Modeling and Simulation on Dynamics of Epidemic Disease Based on Social Division of Labor[J]. Journal of System Simulation, 2020, 32(5): 745-758.
[11] Xi He, Eric H, Lau Y, et al.Temporal Dynamics in Viral Shedding and Transmissibility of COVID-19[J]. Nature Medicine (S1078-8956), 2020, 26(5): 672-675.
[12] 李杰, 陈建斌. 随机动力系统中的概率密度演化方程及其研究进展[J]. 力学进展, 2010, 40(2): 170-188.
Li Jie, Chen Jianbin.Probability Density Evolution Equation in Stochastic Dynamic System and Its Research Progress[J]. Advances in Mechanics, 2010, 40(2): 170-188.
[13] 邱明, 悦胡涛, 崔恒建. 双区间删失下新冠病毒肺炎潜伏期分布的参数估计[J]. 应用数学学报, 2020, 43(2): 200-210.
Qiu Ming, Yue Hutao, Cui Hengjian.Parameter Estimation of Incubation Period Distribution of New Coronavirus Pneumonia Under Double Interval Censoring[J]. Journal of Applied Mathematics, 2020, 43(2): 200-210.

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新冠后疫情时代复学风险评估的不确定性量化分析

Uncertainty Quantitative Analysis in Risk Assessment of Returning to School in the Post-COVID-19 Era

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