摘要
新冠肺炎疫情是近年来国际社会防控难度较大的重大突发公共卫生事件,疫情防控的重点是解决“找人难、管人难、决策难”的“三难”问题。本研究利用大数据技术,结合管理学、控制学和社会学等相关理论,通过知识图谱和深度学习等技术来监测公共卫生事件中的风险人群,构建人的时空动态轨迹模型以动态监测疫情;搭建适用于不同疫情或突发事件的可扩展可移植防控系统,实现多元主体信息共享下的信息辅助闭环管理和防控协同。在厦门市城市疫情防控中的实践显示,该系统可提升社会疫情防控治理的综合能力。
COVID-19 has become a major public health emergency with great difficulty to prevent and control for the international community in recent years.The focus of epidemic control is to solve the"three difficult problems"of"locating people,managing people and making decisions".This paper uses big data technology,combined with relevant theories of management,cybernetics and sociology,and applies technologies such as knowledge graph and deep learning to monitor populations under risk in public health emergencies,and constructs a human spatiotemporal dynamic trajectory model to dynamically monitor the epidemic situation.Meanwhile,it builds an extensible and portable prevention and control system suitable for different epidemic situations or emergencies,which enables information-assisted closed-loop management and prevention and control coordination under the information sharing of multiple subjects.This model has been put into practice in the prevention and control of urban COVID-19 prevention and control in Xiamen,which improves the comprehensive capacity of social epidemic prevention and control.
作者
叶荔姗
Ye Lishan(Xiamen Health and Medical Big Data Center,Xiamen Medicine Research Institute,Xiamen 361006,Fujian Province,China)
出处
《中国数字医学》
2022年第9期17-22,共6页
China Digital Medicine
基金
福建省医学创新课题数据与知识驱动的公共卫生防控机制研究(2020CX01010011)
关键词
重大公共卫生事件
时空计算
精准防控
知识图谱
大数据
GIS
Major public health emergency
Space-time calculation
Precise prevention and control
Knowledge graph
Big data
GIS
