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| COVID-19疫情时空聚集性特征及影响因素分析——以重庆市为例 | |
| 引用本文： | 陈晓,黄宇金,李佳慧,汪诗洋,裴韬.COVID-19疫情时空聚集性特征及影响因素分析——以重庆市为例[J].地理科学进展,2020,39(11):1798-1808. |
| 作者姓名： | 陈晓 黄宇金 李佳慧 汪诗洋 裴韬 |
| 作者单位： | 1.中国科学院地理科学与资源研究所资源与环境信息系统国家重点实验室,北京100101 2.中国科学院大学资源与环境学院,北京 100049 3.中国科学院区域可持续发展分析与模拟重点实验室,北京 100101 4.中国科学院地理科学与资源研究所陆地表层格局与模拟重点实验室,北京100101 5.中国科学院大学计算机科学与技术学院,北京 100049 |
| 基金项目： | 国家自然科学基金项目(42041001);国家自然科学基金项目(41525004);国家自然科学基金项目(41421001) |
| 摘 要： | 新型冠状病毒肺炎(COVID-19)在城市内部的病例时空聚集性特征及影响因素对于疫情防控具有重要参考价值。论文以重庆市为例,收集2020年1月21日—2月24日的458例COVID-19确诊病例数据,结合手机信令数据与空间环境数据,采用空间聚集性分析、因子分析与回归分析等方法,探究重庆市新冠疫情在街道尺度上的时空聚集性特征,并分析其影响因素。结果表明：① 时间上,确诊病例前期增长较快,以外地输入为主,后期逐渐趋缓,以本地传播(包括街道间传播、街道内传播、家庭内传播)为主,其中家庭内传播占比最大(23%);② 空间上,病例呈现显著的聚集特征,且聚集性逐渐增强,热点街道分布于重庆市西部、东北部;分类型来看,本地病例热点街道集中于人口密度较大、经济发展更好的西北部、西南部,外地输入病例热点街道集中于地理邻近湖北省的中部、东北部;③ 所有病例密度、本地病例密度、外地输入病例密度与因子分析所得4个因子(即城市交通因子、街道间活动因子、生活服务因子、居民分布因子)的回归结果显示,交通设施水平与病例密度存在密切关联,商店超市餐饮等生活服务地点与病毒传播显著相关,街道间活动是发生聚集性疫情的重要因素,而外地输入型病例多出现于人口密集区域。疫情防控中应根据不同区域疫情传播模式差异实施针对性措施,例如在重庆市中部、东北部关注外地输入防控,西北部、西南部以本地传播防控为重点;同时,应加强城市内部交通与街道居民接触密集区域的防控措施,有效防止疫情反弹。 |
| 关 键 词： | 新冠肺炎 聚集特征 街道尺度 重庆市 |
| 收稿时间： | 2020-07-22 |
| 修稿时间： | 2020-09-13 |
Clustering characteristics of COVID-19 cases and influencing factors in Chongqing Municipality |
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| CHEN Xiao,HUANG Yujin,LI Jiahui,WANG Shiyang,PEI Tao.Clustering characteristics of COVID-19 cases and influencing factors in Chongqing Municipality[J].Progress in Geography,2020,39(11):1798-1808. | |
| Authors: | CHEN Xiao HUANG Yujin LI Jiahui WANG Shiyang PEI Tao |
| Abstract: | Clustering characteristics of COVID-19 cases within cities and influencing factors are of significant referential value for epidemic prevention and control. In this study, we conducted spatial clustering analysis, factor analysis, and regression analysis on 458 COVID-19 confirmed cases from 21 January to 24 February 2020, and we used mobile phone signaling data, and environmental data to analyze the spatiotemporal variability of epidemic characteristics in Chongqing Municipality at the residential community scale and the influencing factors. The results show that: 1) Temporally, the number of confirmed cases showed a rapid increasing trend in the beginning, and most of the cases were imported cases. In the late stage, the increase rate lowered, and the main trend turned into local transmission (including inter-community, intra-community, and intra-family transmissions), among which intra-family transmissions made up the largest portion (23%). 2) Spatially, the cases showed significant clustering characteristics, and aggregation level increased with time. The hot spots of all cases were distributed in the west and northeast Chongqing. The hot spots of local cases were mainly concentrated in the northwestern and southwestern regions where population density and economic development level were higher, while the hot spots of imported cases were mainly concentrated in the central and northeastern regions adjacent to Hubei Province. 3) The regression results between the density of all cases, local cases, imported cases and four factors obtained by factor analysis (urban traffic factor, intra-community activity factor, service provision factor, and residents' distribution factor) provide some insights. Transportation facility level was closely related to the density of confirmed cases. Service places such as stores, supermarkets, and restaurants significantly contributed to the spread of the virus. Inter-community transmission was an important factor in local clustering of cases, while imported cases mostly occurred in densely populated areas. Hence, targeted measures should be adopted for future epidemic prevention and control according to various epidemic transmission patterns in different regions, such as paying attention to imported cases in the central and northeastern parts of Chongqing, and focusing on avoidance of local transmission in northwest and southwest. Moreover, measures should be strengthened in the areas with dense urban traffic and resident population to effectively prevent the outbreak from rebounding. |
| Keywords: | COVID-19 clustering characteristics residential community scale Chongqing Municipality |
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