基于贝叶斯网络的高校突发传染病疫情情景演化分析
Scenario Evolution Analysis of Infectious Disease Emergencies in Universities Based on Bayesian Networks
胡晨宇 1胡安 2刘丹 1陈方宇3
作者信息
- 1. 武汉理工大学 中国应急管理研究中心,湖北 武汉 430070;武汉理工大学 安全科学与应急管理学院,湖北 武汉 430070
- 2. 武汉理工大学 安全科学与应急管理学院,湖北 武汉 430070;中铁隧道股份有限公司,河南 郑州 450001
- 3. 中南财经政法大学 工商管理学院,湖北 武汉 430073
- 折叠
摘要
针对高校如何科学有效地应对突发传染病疫情的问题,从湖北省武汉市各高校官方网站筛选100 份文本资料,运用扎根理论将其整理为193 个概念和117 个范畴,构建高校突发传染病疫情的情景要素.建立高校突发传染病疫情的事故树,梳理情景演化逻辑.结合专家打分法和数据分析法,建立包含19 个节点的高校突发传染病疫情的情景演化贝叶斯网络.结果表明:疫区管控,大规模接种疫苗和病患统一管理是新增病例数量下降的关键因素;提供充足物资与宣传防控知识对人员心理问题有较大影响;在病毒突变的情况下,未采取任何措施新增病例数量下降概率仅21%,而采取常态化疫情防控情况下可达86.7%.
Abstract
To address the issue of how universities can scientifically and effectively respond to sudden outbreaks of infectious diseases,first,100 text materials were selected from the official websites of universities in Wuhan,Hubei Province.Using the grounded theory,they were organized into 193 concepts and 117 categories to construct situational elements of sudden outbreaks of infectious diseases in universities.An accident tree for sudden outbreaks of infectious diseases in universities was established to sort out the logic of scenario evolution.Combining with expert scoring method and data analysis method,a scenario evolution Bayesian network containing 19 nodes for the outbreak of infectious diseases in universities was established.The results indicated that"epidemic area control","large-scale vaccination",and"unified patient management"are the key factors for the decrease in the number of new cases;"Providing sufficient materials"and"promoting prevention and control knowledge"have a signifi-cant impact on"personnel psychological problems";In the case of viral mutations,the probability of a decrease in the number of new cases without taking any measures is only 21%,while in the case of normalized epidemic prevention and control,it can reach 86.7%.
关键词
突发传染病疫情/情景演化/扎根理论/贝叶斯网络/事故树分析法Key words
infectious disease emergencies/scenario evolution/grounded theory/Bayesian network/fault tree analysis引用本文复制引用
基金项目
国家社会科学基金重大项目(21&ZD127)
国家自然科学基金青年项目(72204194)
国家社会科学基金一般项目(23BGL280)
出版年
2024
NETL
NSTL
维普
万方数据