2020年湖南省COVID-19疫情的地理学分析

doi:10.13284/j.cnki.rddl.003367

摘要/Abstract

摘要：

以发病数和发病率为依据，利用ArcGIS 10.3软件和地理探测器，探究湖南省2020-01-21—02-28间COVID-19疫情的时空变化及其影响因素，结果显示：1）疫情演变具有时序阶段性。湖南省COVID-19流行过程可分为扩散期—鼎盛期—衰减期—消亡期4个阶段；所有发病县区中84.47%的首发病例为武汉输入。2）疫情传播具有交通依赖性。确诊患者和高发病率地区集中在铁路干线附近，“地级市早发、主城区多发”特征明显。3）疫情程度具有距离衰减性。邻近武汉的湘中、湘北、湘东北地区发病数和发病率最高。4）疫情分布具有空间集聚性。发病数和发病率集聚水平先增大后保持高位，疫情热点区位于长株潭及岳阳市，冷点区位于湘西州。5）疫情流行具有因子交互性。无风、寒冷、干燥的天气更利于疫情传播；人口密度、路网密度和GDP是影响疫情空间分异的主要因素；自然因子与社会因子的交互作用最强。6）影响因子具有空间异质性。空间距离、人口密度对发病数和发病率两者的影响都有空间异质性，空间距离对湘东北地区疫情的影响最大，人口密度对湘西北、湘北地区疫情的影响最大；人口流动性、卫技人员数仅对发病数的影响有空间异质性，人口流动性的影响程度由北向南递减，卫技人员数的影响程度以长株潭城市群为核心向外围圈层递减。

关键词: COVID-19疫情, 传播路径, 时空格局, 湖南省

Abstract:

The COVID-19 epidemic spread globally at the start of 2020, resulting in a wide range of diffusion of infectious diseases. At present, SARS-CoV-2 is still prevalent worldwide, and it is of great practical significance to strengthen the study on the COVID-19 epidemic. Based on the COVID-19 confirmed cases and incidence, this article explored both the temporal and spatial distribution pattern of COVID-19 in Hunan Province and its influencing variables between January 21 and February 28, 2020 by using ArcGIS 10.3 and GeoDetector. The results show that: 1) The epidemic evolved with temporal phasing. The epidemic in Hunan Province can be divided into four basic stages: the diffusion period, peak period, decay period, and the stagnant period.The first COVID-19 confirmed case of 84.47% affected counties was imported from Wuhan;2) The epidemic spread as a result of transportation convenience. The confirmed COVID-19 cases were concentrated near major railway lines, which were also high-incidence areas. The epidemic occurred "early in prefecture-level cities and frequently in urban areas"; 3) the extent of the epidemic has distance attenuation. The COVID-19 cases and high-incidence areas were mainly distributed in the middle, north, and northeast of Hunan, in regions in proximity to Wuhan; 4) the epidemic was distributed with spatial aggregation. The agglomeration level of cases and incidences increased first and then remained high. The hot spots identified are in Changzhutan and Yueyang City, while the cold spots are in Xiangxi Prefecture; 5) the epidemic prevalence was of variable interaction. Wind-free, cold and dry weather was more conducive to the spread of the epidemic; population density, road network density and GDP were the main factors affecting the epidemic spatial differentiation; and the strongest variable interaction happened between natural and social factors. 6) the influencing variables of the COVID-19 epidemic have spatial heterogeneity. The impacts of spatial distance and population density have spatial heterogeneity on both the reported cases the total infections rates of COVID-19, spatial distance has the greatest impact in northeast Hunan, and population density has the greatest impact in northwest and northern Hunan; but the mobility of population and the number of health technicians only have spatial heterogeneity of impacts on the reported cases of COVID-19, the impact of population mobility is diminishing from north to south in Hunan, and the influence of the number of health technicians decreases gradually with the Changsha-Zhuzhou-Xiangtan city agglomeration as the core to the outer circle.The paper's conclusions are consistent with other scholars' studies in other regions, confirming that the epidemic not only spreads significantly in neighboring areas, but there are also "jump spreads" to distant cities that rely on the transportation network in Hunan. It is evident that restricting the entry of residents of affected areas plays a key role in COVID-19 prevention and control, and moreover, allocating plentiful health technicians, executing rigorous transport quarantine, and adopting compatible preventing measures with local and temporal conditions, are the top priorities in effectively responding to the impact of the COVID-19 epidemic.

Key words: COVID-19 epidemic, transmission path, spatial-temporal pattern, Hunan Province

中图分类号:

R181.8

龚胜生, 莫慧. 2020年湖南省COVID-19疫情的地理学分析[J]. 热带地理, 2021, 41(4): 708-722.

Shengsheng Gong, Hui Mo. Geographical Analysis of the COVID-19 Epidemic in Hunan Province, China in 2020[J]. Tropical Geography, 2021, 41(4): 708-722.

图/表 12

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图2

图3

表1

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图5

图6

图7

图8

表2

湖南省COVID-19发病数和发病率的影响因子q值

发病数	$x 1$	$x 2$	$x 3$	$x 5$	$x 6$	$x 7$	$x 8$	$x 9$	$x 10$	$x 11$	$x 12$	发病率	$x 1$	$x 2$	$x 3$	$x 5$	$x 6'$	$x 7$	$x 8$	$x 9'$	$x 10'$	$x 11'$	$x 12'$
$x 1$	0.18											$x 1$	0.21
$x 2$	0.58	0.29										$x 2$	0.64	0.35
$x 3$	0.76	0.73	0.30									$x 3$	0.47	0.60	0.15
$x 5$	0.62	0.74	0.85	0.38								$x 5$	0.66	0.80	0.50^*	0.37
$x 6$	0.60	0.85	0.85	0.86	0.28							$x 6'$	0.71	0.74	0.50	0.88	0.21
$x 7$	0.67	0.74^*	0.79	0.88	0.79	0.46						$x 7$	0.72^*	0.78^*	0.72	0.83^*	0.68^*	0.55
$x 8$	0.64^*	0.72^*	0.80	0.85^*	0.82	0.54^*	0.47					$x 8$	0.81	0.83^*	0.76	0.89^*	0.71^*	0.68^*	0.55
$x 9$	0.66	0.72^*	0.78	0.83^*	0.62^*	0.75^*	0.71^*	0.46				$x 9'$	0.81	0.83	0.74	0.80^*	0.88	0.76^*	0.83^*	0.47
$x 10$	0.49^*	0.91	0.74	0.82	0.72	0.72^*	0.80^*	0.66^*	0.35			$x 10'$	0.76	0.75^*	0.70	0.80^*	0.76	0.69^*	0.75^*	0.74^*	0.44
$x 11$	0.74^*	0.78^*	0.87	0.96	0.85	0.84^*	0.86^*	0.74^*	0.77^*	0.56		$x 11'$	0.68	0.72	0.63	0.83	0.73	0.74^*	0.71^*	0.77	0.71	0.21
$x 12$	0.72	0.78	0.84	0.87	0.69^*	0.80^*	0.83^*	0.70^*	0.77^*	0.62^*	0.44	$x 12'$	0.59	0.68	0.57	0.81	0.68	0.73^*	0.68^*	0.65^*	0.71	0.33^*	0.19

表2

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图10

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发病数/例	2020-01-27		2020-02-06		2020-02-14		2020-02-28
发病数/例	染疫县数/个	占比/%	染疫县数/个	占比/%	染疫县数/个	占比/%	染疫县数/个	占比/%
合计	68	55.74	102	83.61	102	83.61	103	84.43
1~5	64	52.46	57	46.72	49	40.16	49	40.16
6~10	4	3.28	26	21.31	24	19.67	24	19.67
11~15	0	0.00	9	7.38	12	9.84	13	10.66
16~30	0	0.00	5	4.10	11	9.02	11	9.02
31~50	0	0.00	3	2.46	4	3.28	3	2.46
>50	0	0.00	2	1.64	2	1.64	3	2.46

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