突发重大疫情下城市系统风险量化评估方法

A Quantitative Assessment Method for Urban Systemic Risk in the Case of a Major Epidemic Outbreak

疫情传播具有高度的时空特征,中国幅员辽阔,区域发展差异较大,对传染疾病的防控应充分考虑城市系统中的时空差异以降低系统风险。本文通过大数据分析,对全国各城市的疫情风险进行科学评估和比对,探索了一种全国城市系统风险量化评估方法,可为今后类似突发重大疫情防控和风险管理提供参考借鉴。通过抓取并整合多来源疫情公开信息、网络大数据和统计年鉴资料,从五大维度12个子项指标入手构建了338个中国城市的总体疫情风险指数。对各城市疫情风险指数的测量、可视化、聚类分析和空间分析表明,疫情扩大存在明显的空间地缘和分层特征,需要全国层面“分级管理”、地方层面“精准防控”。同时应该建立基于多来源大数据的城市综合风险评估“中央平台”,形成集公共卫生、公共安全和公共舆情“三位一体”的实时大数据整合、监控和分析系统;利用区块链技术,实现对城市关键性风险数据的全面监督和检视分析,为“中央平台”提供散点预警。

The spread of epidemic disease is highly spatial and temporal.As China is vast in territory and varies greatly in regional development,it is necessary to fully consider the spatial and temporal differences in urban systems for the prevention and control of infectious diseases.Through the comprehensive use of big data,this study assessed the epidemic risk in cities across the country and explored a systemic quantitative risk assessment method for the urban areas,which could provide a reference for the prevention and control as well as risk management of similar outbreaks in the future.By capturing and integrating public information from multiple sources,big data networks and statistical yearbooks,we constructed an overall epidemic risk index for 338 Chinese cities based on 12 sub-indexes in five dimensions.From the measurement,visualization,cluster analysis and spatial analysis of the epidemic risk index in these cities,we noticed that the expansion of the epidemic is characterized with obvious spatial geography and stratification,which requires“hierarchical management”at the national level and“precise prevention and control”at the local level.Moreover,a data center for city-level risk assessment based on multiple big data sources should be established to serve as a comprehensive system covering public health,public security,and public opinions altogether.With the development of blockchain technology,such a data center can achieve the surveillance and analysis for crucial city risk factors and provide targeted forewarnings.