The socio-spatial determinants of COVID-19 diffusion: the impact of globalisation, settlement characteristics and population.
The socio-spatial determinants of COVID-19 diffusion: the impact of globalisation, settlement characteristics and population.
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DOI：
10.1186/s12992-021-00707-2
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发表时间：
2021-05-20
影响因子：
10.8
通讯作者：
Corcoran J
中科院分区：
文献类型：
--
作者：
Sigler T;Mahmuda S;Kimpton A;Loginova J;Wohland P;Charles-Edwards E;Corcoran J
COVID-19 是一种新出现的传染病，已在地理上传播并成为全球大流行病。尽管许多研究都集中在 COVID-19 传播的流行病学和病毒学方面，但对于不同地点之间地理传播的驱动因素（尤其是在全球范围内）的认识仍然存在重大差距。在这里，我们使用分位数回归来模拟全球化、人类住区和人口特征的作用，作为 2020 年 3 月和 4 月六周内报告的 COVID-19 扩散的社会空间决定因素。我们的探索性分析基于约翰霍普金斯大学发布的报告的 COVID-19 数据，尽管有其局限性，但它是各国报告的 COVID-19 病例的最佳存储库。分位数回归模型表明，与人类高流动性和相互作用相关的全球化、定居和人口特征可以预测所报告的疾病扩散。人类发展水人口密度和人口特征（例如总人口、老年人口和家庭规模）在最初几周是强有力的预测因素，但随着时间的推移，对报告的 COVID-19 扩散的影响微乎其微。相比之下，人际和贸易全球化的影响随着时间的推移而增强，这表明人口流动可能是疾病持续传播的最佳解释。模型结果证实，全球化、定居和人口特征以及与高人口流动性相关的变量导致报告的疾病扩散更大。这些结果有助于为抑制策略提供信息，特别是因为它们与预期的从较发达国家和地区向欠发达国家和地区的迁移扩散以及从人口和密度较高的国家的等级扩散有关。许多这些过程很可能在国家和区域内的较小地理范围内重复。因此，必须根据人口流动模式以及国家的居住和人口特征制定流行病学策略。我们建议，最大程度地限制人员流动将最好地抑制 COVID-19 的扩散，在没有广泛接种疫苗的情况下，这可能是流行病学防御的最佳防线之一。在线版本包含可在 10.1186/s12992-021-00707-2 获取的补充材料。
COVID-19 is an emergent infectious disease that has spread geographically to become a global pandemic. While much research focuses on the epidemiological and virological aspects of COVID-19 transmission, there remains an important gap in knowledge regarding the drivers of geographical diffusion between places, in particular at the global scale. Here, we use quantile regression to model the roles of globalisation, human settlement and population characteristics as socio-spatial determinants of reported COVID-19 diffusion over a six-week period in March and April 2020. Our exploratory analysis is based on reported COVID-19 data published by Johns Hopkins University which, despite its limitations, serves as the best repository of reported COVID-19 cases across nations. The quantile regression model suggests that globalisation, settlement, and population characteristics related to high human mobility and interaction predict reported disease diffusion. Human development level (HDI) and total population predict COVID-19 diffusion in countries with a high number of total reported cases (per million) whereas larger household size, older populations, and globalisation tied to human interaction predict COVID-19 diffusion in countries with a low number of total reported cases (per million). Population density, and population characteristics such as total population, older populations, and household size are strong predictors in early weeks but have a muted impact over time on reported COVID-19 diffusion. In contrast, the impacts of interpersonal and trade globalisation are enhanced over time, indicating that human mobility may best explain sustained disease diffusion. Model results confirm that globalisation, settlement and population characteristics, and variables tied to high human mobility lead to greater reported disease diffusion. These outcomes serve to inform suppression strategies, particularly as they are related to anticipated relocation diffusion from more- to less-developed countries and regions, and hierarchical diffusion from countries with higher population and density. It is likely that many of these processes are replicated at smaller geographical scales both within countries and within regions. Epidemiological strategies must therefore be tailored according to human mobility patterns, as well as countries’ settlement and population characteristics. We suggest that limiting human mobility to the greatest extent practical will best restrain COVID-19 diffusion, which in the absence of widespread vaccination may be one of the best lines of epidemiological defense. The online version contains supplementary material available at 10.1186/s12992-021-00707-2.
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DOI：
10.1177/0042098020910873
发表时间：
2020-03-31
期刊：
Urban Studies (Edinburgh, Scotland)
影响因子：
--
作者：
Connolly C;Keil R;Ali SH
通讯作者：
Ali SH
移民和移民在新兴的传染病中的作用。
DOI：
10.1016/j.mcna.2008.07.001
发表时间：
2008-11
期刊：
The Medical clinics of North America
影响因子：
--
作者：
Barnett ED;Walker PF
通讯作者：
Walker PF
影响因子：
56.9
作者：
Chinazzi, Matteo;Davis, Jessica T.;Vespignani, Alessandro
通讯作者：
Vespignani, Alessandro
影响因子：
12.7
作者：
Dey, Samrat K.;Rahman, Md. Mahbubur;Howlader, Arpita
通讯作者：
Howlader, Arpita
影响因子：
2.5
作者：
Bell, Martin;Charles-Edwards, Elin;Kupiszewska, Dorota
通讯作者：
Kupiszewska, Dorota