中国COVID-19疫情时空演化与综合防控

doi:10.11821/dlxb202011015

Abstract

Abstract:

The sudden outbreak of the novel coronavirus disease (COVID-19), an unexpected emergency event in human society, is spreading globally and has severe impacts on human health and social development. Based on the confirmed COVID-19 cases' details manually extracted from the official reports and the relevant Point of Interest (POI) data, this paper aims to explore the sociodemographic characteristics of confirmed COVID-19 cases and examine the spatio-temporal evolution patterns on different spatial scales in China by using text analysis, spatial analysis and other methods. Furthermore, we provide targeted measures for prevention and control of this epidemic. The results indicate that: (1) In contrast to the twenty-four solar terms, the epidemic started at Heavy Snow, ran rampant in the Beginning of Spring, declined in the Awakening of Insects, and stagnated at the Vernal Equinox. It experienced four stages: the early period of Wuhan dominant outbreak, the middle period of cross-country diffusion, the later period of Wuhan dominant decline and the ending period of rare local epidemic transmission. (2) The spatial distribution presents a pattern of "one core, two arcs and multiple islands", mainly affected by the distance to Wuhan and the flow of population; the spatial evolution is manifested in the mode of "core outbreak - cluster occurrence - dispersion occurrence - point occurrence". The core aggregation area in each stage was located in Hubei province. Finally, by integrating the influencing factors and effective measures of the epidemic evolution in China, this paper proposes effective countermeasures for the spread of the current epidemic and the coordinated development of human resources in response to similar challenges in the future, which are manifested in instant response, coordinated cooperation, regular prevention and control, and strict prevention of importation, etc. Also, the directions of in-depth study in the future are pointed out.

Key words: COVID-19, major epidemic, spatio-temporal evolution, prevention and control measures, China

LI Gang, WANG Jiaobei, XU Tingting, GAO Xing, JIN Annan, YU Yue. Spatio-temporal evolution process and integrated measures for prevention and control of COVID-19 epidemic in China[J].Acta Geographica Sinica, 2020, 75(11): 2475-2489.

Figures/Tables 7

Fig. 1

Fig. 2

Tab. 1

Fig. 3

Fig. 4

Tab. 2

Fig. 5

References 33

[1]	Lu R J, Zhao X, Li J, et al. Genomic characterisation and epidemiology of 2019 novel coronavirus: Implications for virus origins and receptor binging. Lancet, 2020,395(10224):565-574. doi: 10.1016/S0140-6736(20)30251-8 pmid: 32007145
[2]	Zhou P, Yang X L, Wang X G, et al. A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature, 2020,579(7798):270-273. doi: 10.1038/s41586-020-2012-7 pmid: 32015507
[3]	National Health Committee General Office "About print and distribute new coronavirus pneumonia diagnosis and treatment scheme (trial version 5)" notice. http://www.nhc.gov.cn/wjw/gfxwjj/list.shtml, 2020-02-05.
	[ 国家卫生健康委办公厅《关于印发新型冠状病毒感染肺炎诊疗方案 (试行第五版)》的通知. http://www.nhc.gov.cn/wjw/gfxwjj/list.shtml, 2020-02-05.]
[4]	Huang C L, Wang Y M, Li X W, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet, 2020,395(10223):497-506. doi: 10.1016/S0140-6736(20)30183-5 pmid: 31986264
[5]	Zhou Tao, Liu Quanhui, Yang Zimo, et al. Preliminary prediction of the basic reproduction number of the novel coronavirus 2019-nCoV. Chinese Journal of Evidence-Based Medicine, 2020,20(3):359-364.
	[ 周涛, 刘权辉, 杨紫陌, 等. 新型冠状病毒肺炎基本再生数的初步预测. 中国循证医学杂志, 2020,20(3):359-364.]
[6]	Xiong H, Yan H L. Simulating the infected population and spread trend of 2019-nCov under different policy by EIR model. Medrxiv, 2020. Doi: 10.1101/2020.02.10.20021519. doi: 10.1101/2020.10.30.20223172 pmid: 33173932
[7]	Gong Shengsheng. Changes of the temporal-spatial distribution of epidemic disasters in 770BC-AD1911, China. Acta Geographica Sinica, 2003,58(6):870-878. doi: 10.11821/xb200306010
	[ 龚胜生. 中国疫灾的时空分布变迁规律. 地理学报, 2003,58(6):870-878.]
[8]	Yu Xinzhong. From epidemic avoidance to epidemic prevention: The evolution of epidemic response in late Qing dynasty. Journal of Central China Normal University (Humanities and Social Sciences Edition), 2008,47(2):51-60.
	[ 余新忠. 从避疫到防疫: 晚清因应疫病观念的演变. 华中师范大学学报(人文社会科学版), 2008,47(2):51-60.]
[9]	Cheng Yang, Li Hairong, Yang Linsheng. Temporal-spatial distribution of epidemics in Ming and Qing dynasties (1368-1911 A.D.) in China. Geographical Research, 2009,28(4):1059-1068.
	[ 程杨, 李海蓉, 杨林生. 中国明清时期疫病时空分布规律的定量研究. 地理研究, 2009,28(4):1059-1068.]
[10]	Tan Jianan, Li Ribang, Zhu Wenyu. The progress in medical geography of China and its prospect. Acta Geographica Sinica, 1990,45(2):187-201.
	[ 谭见安, 李日邦, 朱文郁. 我国医学地理研究的主要进展和展望. 地理学报, 1990,45(2):187-201.]
[11]	Wu Jilei, Wang Jinfeng, Meng Bin, et al. Spatial association analysis on epidemic of SARS in Beijing, 2003. Journal of Zhejiang University (Agriculture and Life Sciences), 2005,31(1):97-101.
	[ 武继磊, 王劲峰, 孟斌, 等. 2003年北京市SARS疫情空间相关性分析. 浙江大学学报(农业与生命科学版), 2005,31(1):97-101.]
[12]	Fan Xinsheng, Ying Longgen. An exploratory spatial data analysis of SARS epidemic in China. Advances in Earth Science, 2005,20(3):282-291.
	[ 范新生, 应龙根. 中国SARS疫情的探索性空间数据分析. 地球科学进展, 2005,20(3):282-291.]
[13]	Wang Jinfeng, Meng Bin, Zheng Xiaoying, et al. Analysis on the multi-distribution and the major influencing factors on severe acute respiratory syndrome in Beijing. Chinese Journal of Epidemiology, 2005,26(3):164-168. pmid: 15941497
	[ 王劲峰, 孟斌, 郑晓瑛, 等. 北京市2003年SARS疫情的多维分布及其影响因素分析. 中华流行病学杂志, 2005,26(3):164-168.] pmid: 15941497
[14]	Cao Zhidong, Zeng Dajun, Zheng Xiaolong, et al. Characteristics of SARS epidemic in Beijing and laws of spatial and temporal transmission. Scientia Sinica (Terrae), 2010,40(6):776-788.
	[ 曹志冬, 曾大军, 郑晓龙, 等. 北京市SARS流行的特征与时空传播规律. 中国科学: 地球科学, 2010,40(6):776-788.]
[15]	Cao Zhidong, Wang Jinfeng, Gao Yige, et al. Risk factors and autocorrelation characteristics on severe acute respiratory syndrome in Guangzhou. Acta Geographica Sinica, 2008,63(9):981-993. doi: 10.11821/xb200809008
	[ 曹志冬, 王劲峰, 高一鸽, 等. 广州SARS流行的空间风险因子与空间相关性特征. 地理学报, 2008,63(9):981-993.]
[16]	Feng Yerong, Zhu Kelun, Ji Zhongping, et al. On the study of the relationship between Guangzhou atmospheric environment factors and the SARS epidemic. Journal of Tropical Meteorology, 2005,21(2):191-198.
	[ 冯业荣, 朱科伦, 纪忠萍, 等. 广州大气环境因素与SARS疫情短期变化关系的研究. 热带气象学报, 2005,21(2):191-198.]
[17]	Liu Jiyuan, Zhong Ershun, Zhuang Dafang, et al. SARS control and early warning geographic information system. Modern Scientific Instruments, 2003(4):10-13.
	[ 刘纪远, 钟耳顺, 庄大方, 等. SARS控制与预警地理信息系统. 现代科学仪器, 2003(4):10-13.]
[18]	Liu Jiyuan, Zhong Ershun, Zhuang Dafang, et al. Development and application of SARS control and early warning geographic information system. Journal of Remote Sensing, 2003,7(5):337-344, 433. doi: 10.11834/jrs.20030501
	[ 刘纪远, 钟耳顺, 庄大方, 等. SARS控制与预警地理信息系统的研制与应用. 遥感学报, 2003,7(5):337-344, 433.]
[19]	Xu L D, Yuan J, Zhang Y R. et al. Highland of COVID-19 outside Hubei: Epidemic characteristics, control and projections of Wenzhou, China. Medrxiv, 2020. Doi: 10.1101/2020.02.25.20024398. doi: 10.1101/2020.10.30.20223172 pmid: 33173932
[20]	Wang Jiaobei, Li Gang, Wang Jianpo, et al. Spatio-temporal evolution and risk profiling of COVID-19 epidemic in Shaanxi province. Tropical Geography, 2020,40(3):432-445.
	[ 王皎贝, 李钢, 王建坡, 等. 陕西省COVID-19疫情时空演化与风险画像. 热带地理, 2020,40(3):432-445.]
[21]	Liu Yi, Li Yuan, Li Zhuoling, et al. Diffusion characteristics of the Corona Virus Disease 2019(COVID-19) outbreak in Guangdong province. Tropical Geography, 2020,40(3):367-374.
	[ 刘逸, 李源, 黎卓灵, 等. 新冠肺炎疫情在广东省的扩散特征. 热带地理, 2020,40(3):367-374.]
[22]	Jin Annan, Li Gang, Wang Jiaobei, et al. Spatio-temporal evolution and control strategies of COVID-19 epidemic in Shenzhen, China. Journal of Shaanxi Normal University (Natural Science Edition), 2020,48(3):18-32.
	[ 金安楠, 李钢, 王皎贝, 等. 深圳市新型冠状病毒肺炎(COVID-19)疫情时空演化与防控对策. 陕西师范大学学报(自然科学版), 2020,48(3):18-32.]
[23]	Wang Jiaoe, Du Delin, Wei Ye, et al. The development of COVID-19 in China: Spatial diffusion and geographical pattern. Geographical Research, 2020,39(7):1450-1462.
	[ 王姣娥, 杜德林, 魏冶, 等. 新冠肺炎疫情的空间扩散过程与模式研究. 地理研究, 2020,39(7):1450-1462.]
[24]	Lefever D W. Measuring geographic concentration by means of the standard deviational ellipse. The American Journal of Sociology, 1926,32(1):88-94. doi: 10.1086/214027
[25]	Xie Z, Yan J. Kernel density estimation of traffic accidents in a network space. Computers, Environment and Urban Systems, 2008,32(5):396-406. doi: 10.1016/j.compenvurbsys.2008.05.001
[26]	Xie Z, Yan J. Detecting traffic accidents clusters with network kernel density estimation and local spatial statistics: An integrated approach. Journal of Transport Geography, 2013,31(5):64-71. doi: 10.1016/j.jtrangeo.2013.05.009
[27]	Getis A, Ord J K. The analysis of spatial association by use of distance statistics. Geographical Analysis, 2010,24(3):189-206. doi: 10.1111/gean.1992.24.issue-3
[28]	Goodchild M. Spatial Autocorrelation Concepts and Techniques in Modern Geography. London: Norwich UK Geo Books, 1986.
[29]	Cai Yan, Chen Huijun, Chen Chuangrong. Analysis of the thought of Chinese medicine's and solar terms. Traditional Chinese Medicinal Research, 2007,20(8):9-11.
	[ 蔡彦, 陈惠军, 陈创荣. 中医节气思想浅析. 中医研究, 2007,20(8):9-11.]
[30]	Xiang Biao. Communities across Borders:The History of "Zhejiang village" in Beijing. Beijing: SDX Joint Publishing Company, Sanlian Bookstore, 2000.
	[ 项飚. 跨越边界的社区:北京“浙江村”的生活史. 北京: 生活—读书—新知三联书店, 2000.]
[31]	Liu Zhijia, Huang Heqing. Tempo-spatial characteristics of interactions among changes in built-up land, GDP and demography in the Pearl River Delta. Resource Science, 2015,37(7):1394-1402.
	[ 刘志佳, 黄河清. 珠三角地区建设用地扩张与经济、人口变化之间相互作用的时空演变特征分析. 资源科学, 2015,37(7):1394-1402.]
[32]	Deng Houpei. Basic theory and method of disease geography research. Journal of Capital Normal University (Natural Science Edition), 1994,15(3):94-98.
	[ 邓厚培. 疾病地理研究的基本理论和方法. 首都师范大学学报(自然科学版), 1994,15(3):94-98.]
[33]	Long Ying. Wisdom the technology to improve the toughness of the city: Talk to 2020 COVID-19 accidents will. Urban Planning, 2020. Doi: 10.11819/cpr20201614v. doi: 10.17645/up.v1i4.756 pmid: 29085765
	[ 龙瀛. 泛智慧城市技术提高城市韧性: 应对2020新型冠状病毒肺炎突发事件笔谈会. 城市规划, 2020. Doi: 10.11819/cpr20201614v.] pmid: 29085765

地域	平均值	属性	地域	平均值	属性	地域	平均值	属性
广东	27.79	沿海弧	安徽	2.60	邻近	海南	0.59	其他
浙江	14.29	沿海弧	陕西	2.27	邻近	辽宁	0.42	其他
湖南	7.81	邻近	四川	2.23	其他	甘肃	0.37	其他
江苏	7.00	沿海弧	山东	1.88	沿海弧	西藏	0.33	其他
河南	5.94	邻近	河北	1.57	沿海弧	新疆	0.31	其他
上海	4.31	多岛	广西	1.35	其他	黑龙江	0.22	多岛
福建	4.29	沿海弧	贵州	1.15	其他	内蒙古	0.21	其他
江西	3.66	邻近	云南	1.07	其他	吉林	0.20	其他
重庆	3.39	邻近	天津	0.71	多岛	宁夏	0.13	其他
北京	3.17	多岛	山西	0.68	其他	青海	0.12	其他

时间	高—高型	低—高型
随机扩散期	武汉市、黄冈市、孝感市	鄂州市^*
高度集聚期	十堰市^*、宜昌市、襄阳市、荆门市、随州市、孝感市、武汉市、仙桃市、荆州市、咸宁市、黄石市、黄冈市、鄂州市
集聚衰退期	随州市^、孝感市、武汉市、荆州市^、黄石市、黄冈市、鄂州市
集聚停滞期	随州市^、孝感市、武汉市、荆州市^、黄石市^*、黄冈市、鄂州市

Spatio-temporal evolution process and integrated measures for prevention and control of COVID-19 epidemic in China

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 7

References 33

Related Articles 15

Metrics

Comments

[1]	SUN Pingjun, WANG Kewen. Identification and stage division of urban shrinkage in the three provinces of Northeast China [J]. Acta Geographica Sinica, 2021, 76(6): 1366-1379.
[2]	WEI Shimei, PAN Jinghu. Network structure resilience of cities at the prefecture level and above in China [J]. Acta Geographica Sinica, 2021, 76(6): 1394-1407.
[3]	YANG Ren, PAN Yuxin. Spatial patterns, formation mechanism and coping strategies of rural vulnerability in China at the county level [J]. Acta Geographica Sinica, 2021, 76(6): 1438-1454.
[4]	YIN Jiangbin, LI Shangqian, JIANG Lei, CHENG Zhe, HUANG Xiaoyan, LU Gaigai. The spatio-temporal variations and driving factors of non-farm employment growth in contiguous destitute areas of China [J]. Acta Geographica Sinica, 2021, 76(6): 1471-1488.
[5]	HU Pan, CHEN Bo, SHI Peijun. Spatiotemporal patterns and influencing factors of rainstorm-induced flood disasters in China [J]. Acta Geographica Sinica, 2021, 76(5): 1148-1162.
[6]	LIU Zhenhai, WANG Shaoqiang, CHEN Bin. Spatial and temporal variations of frozen ground and its vegetation response in the eastern segment of China-Mongolia-Russia economic corridor from 2000 to 2015 [J]. Acta Geographica Sinica, 2021, 76(5): 1231-1244.
[7]	WU Feng, LIU Guijun, GUO Naliang, LI Zhihui, DENG Xiangzheng. The effects of COVID-19 epidemic on regional economy and industry in China [J]. Acta Geographica Sinica, 2021, 76(4): 1034-1048.
[8]	MA Zuopeng, LI Chenggu, ZHANG Pingyu. Characteristics, mechanism and response of urban shrinkage in the three provinces of Northeast China [J]. Acta Geographica Sinica, 2021, 76(4): 767-780.
[9]	HUANG Xiaodong, MA Haitao, MIAO Changhong. Connectivity characteristics for city networks in China based on innovative enterprises [J]. Acta Geographica Sinica, 2021, 76(4): 835-852.
[10]	WANG Lucang, LIU Haiyang, LIU Qing. China's city network based on Tencent's migration big data [J]. Acta Geographica Sinica, 2021, 76(4): 853-869.
[11]	XIA Xingsheng, PAN Yaozhong, ZHU Xiufang, ZHANG Jinshui. Monthly calibration and optimization of Ångström-Prescott equation coefficients for agricultural comprehensive area in China [J]. Acta Geographica Sinica, 2021, 76(4): 888-902.
[12]	FAN Zemeng. Spatial identification and scenario simulation of ecotone distribution in China [J]. Acta Geographica Sinica, 2021, 76(3): 626-644.
[13]	XU Yu, LI Xiubin, XIN Liangjie. Differentiation of scale-farmland transfer rent and its influencing factors in China [J]. Acta Geographica Sinica, 2021, 76(3): 753-763.
[14]	LI Gang, XUE Shuyan, MA Xueyao, ZHOU Junjun, XU Tingting, WANG Jiaobei. Spatio-temporal pattern evolution and formation mechanism of missing person incidents in China [J]. Acta Geographica Sinica, 2021, 76(2): 310-325.
[15]	GU Hengyu, SHEN Tiyan. Spatial evolution characteristics and driving forces of Chinese highly educated talents [J]. Acta Geographica Sinica, 2021, 76(2): 326-340.