| 陕西省COVID-19疫情时空演化与风险画像 |
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| 引用本文： | 王皎贝,李钢,王建坡,强靖淇,朱丹丹. 陕西省COVID-19疫情时空演化与风险画像[J]. 热带地理, 2020, 40(3): 432-445. DOI: 10.13284/j.cnki.rddl.003246 |
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| 作者姓名： | 王皎贝 李钢 王建坡 强靖淇 朱丹丹 |
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| 作者单位： | 1.西北大学 a. 城市与环境学院；b. 地表系统与灾害研究院，西安 710127;2.陕西省地表系统与环境承载力重点实验室 西安 710127 |
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| 基金项目： | 西北大学防治新型冠状病毒肺炎紧急科研专项引导基金重点项目(2020);西北大学“仲英青年学者”支持计划(2016);西北大学“人地关系与空间安全”特色优势科研团队建设项目(2019) |
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| 摘 要： | 新型冠状病毒肺炎(COVID-19)疫情的暴发与蔓延,给中国乃至全球带来了极大挑战,已成为社会与学界关注的痛点问题。大流行病在人群与地域的传播扩散是人类与疫情抗争的时空动态过程,值得从地理学视角展开讨论。文章以毗邻中国疫情始发省域湖北的陕西省为研究区,基于官方通报的病例数据与人工判读采集的病例详细信息及相关POI数据,综合运用数理统计、空间分析、文本分析、案例分析等方法,重点探究确诊病例的社会人口学特征和疫情的时空演化格局,评估重点市域的疫情风险等级。结果表明:1)陕西省确诊病例以男性居多,年龄上整体趋向中高龄化,以40~49岁人群为最;外地感染与本地感染均以由"城"作起点的迁移主导;多呈由武汉输入型病例所致的小型核心家庭群聚感染模式,而大型复合感染及特殊场所感染影响深远。2)疫情时间变化差异性显著,整体呈现波动发展、低速衰退、平稳清存3个阶段,确诊时间相对发病时间及初诊时间存在滞后性,且输入型病例多在返陕后0~3 d出现症状。3)疫情在陕西省的空间扩散呈现"远鄂单核"的结构模式,这显著区别于其他环鄂省域。陕西省COVID-19发病率空间分异显著,表现为集中于中南部的倒"T"型分布模式;市域演变呈"三足鼎立"模态;流动路径以"武汉-西安"为主,呈"一源多汇、汉入中南"的流动格局。4)重点市域西安疫情风险等级的空间分布特征表现为"一组团三小片"格局,主城区风险等级高于周边区县。
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| 关 键 词： | COVID-19 重大疫情 时空演化 风险等级 陕西省 |
| 收稿时间： | 2020-04-06 |
Spatio-Temporal Evolution and Risk Profiling of the COVID-19 Epidemic in Shaanxi Province |
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| Wang Jiaobei,Li Gang,Wang Jianpo,Qiang Jingqi,Zhu Dandan. Spatio-Temporal Evolution and Risk Profiling of the COVID-19 Epidemic in Shaanxi Province[J]. Tropical Geography, 2020, 40(3): 432-445. DOI: 10.13284/j.cnki.rddl.003246 |
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| Authors: | Wang Jiaobei Li Gang Wang Jianpo Qiang Jingqi Zhu Dandan |
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| Affiliation: | 1.a. College of Urban and Environmental Sciences; b. Institute of Earth Surface System and Hazards, Northwest University, Xi’an 710127, China;2.Institute of Earth Surface System and Hazards, Northwest University, Xi’an 710127, China |
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| Abstract: | The sudden outbreak and spread of the Corona Virus Disease 2019 (COVID-19) has posed great challenges to the society as well as the academia of not only China but the whole world. The occurrence of epidemic has obvious time and space attributes. Analysis of the spatiotemporal diffusion pattern and process of the epidemic reflects the dynamics of the interaction between humans and the COVID-19, it is worth discussing from the perspective of geography, which is very important for measures for prevention and control of this public health emergency. Based on the confirmed COVID-19 cases’ details manually extracted from the official reports and the relevant Point Of Interest (POI) data, this study aims to reveal the spatiotemporal evolution and risk profiling of the COVID-19 epidemic in Shaanxi province. The results are as follows: Firstly, the age-gender structure of the confirmed cases was diamond-shaped, where more males than females are confirmed, and the overall age trended to be medium-old aged, especially the age group of 40-49. Both non-local and local infections were dominantly caused by the flows of people between cities. Most of the infections belonged to small clusters of core families due to imported cases from Wuhan City, while other large mixed cluster infections in special places may have a deep influence. Secondly, the epidemic evolution process can be roughly divided into three stages, namely wave development stage (Jan.23rd-Feb.6th, 2020), low-speed recession stage (Feb.7th-20th), and stable clearance stage (Feb.21st-Mar.15th). There were lag periods between the cases’ confirmed dates and the onset or initial diagnosis dates. Moreover, the initial reporting dates of confirmed populations were synchronized with the overall evolution of the epidemic. Thirdly, the spatial flow of the epidemic to Shaanxi province was different from that to other provinces around Hubei. That is, it had a unique spatial pattern of only a single cluster center. The overall spatial distribution of the epidemic presented an inverted T-type pattern concentrated in central and southern Shaanxi with significant spatial differentiation. The spatial evolution at the city level was three-pronged. Here, the Wuhan-Xi'an path was the most frequent flow path, exhibiting the flow pattern of "from one source to many sinks, and from Wuhan to central and southern Shaanxi". Finally, the high risk areas were these key cities, for example Xi'an, as shown by the "one big cluster with three small collections" pattern, with the risk level in urban areas being higher than that in the surrounding counties. |
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| Keywords: | COVID-19 major epidemic spatio-temporal evolution risk level Shaanxi province |
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