摘要
基于2020年1月24日至12月18日我国314个城市的COVID-19现存确诊数、累计确诊数等统计数据,采用地理数据探索、新兴时空热点分析、空间马尔科夫链、动态面板数据空间杜宾模型等方法分析了COVID-19疫情的时空变化特征。研究发现:我国COVID-19疫情大致划分为大规模快速爆发期、全国严格防控期、全国抑制期、局部复发期、常态化防疫期5个阶段,绝大部分城市的疫情变化特征与全国总体情况类似;新兴时空热点方法识别出4种类型共124个疫情冷热点,其中增强热点24个、振荡热点27个、持续冷点16个、渐少的冷点57个;疫情热点主要分布于中东部地区特别是湖北周边区域,疫情冷点主要分布于西南、西北及东北地区;各城市现存确诊人数的马尔科夫链转移概率矩阵分析结果显示,各种类型维持现状的概率大于0.893,向下转移的平均概率明显高于向上转移的概率,在不同空间滞后类型的影响下各类型转移概率发生明显变化;动态面板空间杜宾模型估计结果显示314个城市现存确诊数具有显著的时空自相关性且不同阶段有明显差异。
Based on statistical data including number of active cases,total confirmed,deaths of COVID-19 in 314 cities from January 24 to December 18 in 2020,this paper used methods including exploratory spatial data analysis,emerging hot spot analysis,spatial Markov chain,spatial Dubin model of dynamic panel data to analyze spatio-temporal evolution characteristics of COVID-19 epidemic in China.The study found that the COVID-19 epidemic in China can be roughly divided into five stages including large-scale rapid outbreak period,national strict prevention and control period,national suppression period,local recurrence period,and normalized epidemic prevention period.The epidemic change characteristics of most cities are similar to nationwide’s situation.The emerging hot spot analysis identified 4 different types of 124 cold and hot spots,including 24 enhanced hot spots,27 oscillating hot spots,16 continuous cold spots,and 57 decreasing cold spots.The hot spot models are mainly distributed in the central and eastern regions,especially the surrounding areas of Hubei,and the cold spot models are mainly distributed in the southwest,northwest and northeast regions.The results of Markov chain transfer probability matrix analysis of active cases of COVID-19 in cities show that various types are more stable and the probability of maintaining the original type is greater than 0.893.The average probability of downward transfer is significantly higher than the probability of upward transfer.The probability of each type of transition changes significantly under the influence of different spatial lag types.The estimation results of spatial Dubin model of dynamic panel data show that the number of active cases of COVID-19 in 314 cites has significant spatio-temporal autocorrelation and differences at different stages obviously.
作者
巫细波
张小英
葛志专
赖长强
WU Xibo;ZHANG Xiaoying;GE Zhizhuan;LAI Changqiang(Guangzhou Academy of Social Sciences, Guangzhou 510410, China)
出处
《地域研究与开发》
CSSCI
CSCD
北大核心
2021年第1期1-6,共6页
Areal Research and Development
基金
国家社会科学基金项目(20BTJ055)。
