摘要
基于重庆市万州区2020年1月21日至2月21日118例新型冠状病毒肺炎确诊病例数据,运用空间统计分析方法探究了病例的时空特征、来源及传播路径。结果表明:万州区疫情经历了缓慢增长期、暴发期和平稳期3个阶段,25~54岁组所占比例最大,为62.7%,男女比例分别为57.6%和42.4%。确诊病例分布在24个乡镇(街道),并表现出显著的全局空间正相关性和以城区街道为热点的局部自相关性。人口密度和确诊病例发生之间亦呈现显著的全局空间正相关性。全部病例中,外部输入型病例和域内扩散型病例分别占32.2%和67.8%。病例传播路径52条,涉及84个确诊病例,总体传播形式呈“岛状”和“线状”特征。万州区新型冠状病毒肺炎确诊病例的时空特征表明,控制疫情初期的人群流动,做好隔离措施,可以有效控制疫情的传播与扩散。
Based on the data of confirmed COVID-19 cases from January 21,solstice to February 21,2020 in Wanzhou District,Chongqing City,the spatial and temporal characteristics,source and transmission route of the cases were investigated by using spatial statistical analysis method.The results showed that the epidemic in Wanzhou district had experienced three stages:slow growth period,outbreak period and stable period.The group aged 25 to 54 years accounted for 62.7%,and the ratio of male to female was 57.6%and 42.4%.The confirmed cases were distributed in 24 townships(subdistricts),and showed significant positive global spatial correlation and local autocorrelation with urban subdistricts as the hot spot.There was also a significant positive global spatial correlation between population density and the incidence of confirmed cases.Among the total cases,external imported cases and intra-domain spread cases accounted for 32.2%and 67.8%,respectively.There were 52 transmission routes,involving 84 confirmed cases,and the overall transmission pattern was“island-like”and“line-like”.The spatial and temporal characteristics of confirmed COVID-19 cases in Wanzhou District indicate that controlling the flow of people at the initial stage of the epidemic and implementing quarantine measures can effectively control the spread and spread of the epidemic.
作者
袁永剑
王晨雅
郭佳炜
刘付程
YUAN Yongjian;WANG Chenya;GUO Jiawei;LIU Fucheng(School of Marine Technology and Geomatics,Jiangsu Ocean University,Lianyungang 222005,China)
出处
《江苏海洋大学学报（自然科学版）》
CAS
2021年第3期76-81,共6页
Journal of Jiangsu Ocean University:Natural Science Edition
基金
国家自然科学基金资助项目(41976187)。
关键词
新型冠状病毒肺炎
时空分布
空间自相关
输入型
扩散型
COVID-19
spatiotemporal distribution
spatial autocorrelation
external imported cases
intra-domain spread cases
