Queensland Centre for Population Research, The University of Queensland, St Lucia, Queensland, 4072, Australia.
Global Health. 2021 May 20;17(1):56. doi: 10.1186/s12992-021-00707-2.
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.
COVID-19 是一种新发传染病，已在全球范围内传播，成为全球性大流行。虽然许多研究都集中在 COVID-19 传播的流行病学和病毒学方面，但对于地方之间地理扩散的驱动因素，特别是在全球范围内，仍存在重要的知识空白。在这里，我们使用分位数回归来构建模型，将全球化、人类住区和人口特征作为社会空间决定因素，以解释 2020 年 3 月至 4 月六周内报告的 COVID-19 扩散。我们的探索性分析基于约翰霍普金斯大学发布的报告 COVID-19 数据，尽管存在局限性，但该数据是各国报告 COVID-19 病例的最佳数据库。
分位数回归模型表明，全球化、定居点和与高人类流动性和相互作用相关的人口特征预测了报告的疾病扩散。人类发展水平（HDI）和总人口预测了高报告病例数（每百万）的国家的 COVID-19 传播，而较大的家庭规模、较老的人口以及与人类相互作用相关的全球化预测了低报告病例数（每百万）的国家的 COVID-19 传播。人口密度以及总人口、较老人口和家庭规模等人口特征在早期几周是强有力的预测指标，但随着时间的推移，对报告的 COVID-19 传播的影响减弱。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，表明人类流动性可能最好地解释了持续的疾病传播。
模型结果证实，全球化、定居点和人口特征以及与高人类流动性相关的变量导致了更大的报告疾病传播。这些结果有助于制定抑制策略，特别是因为它们与预期从较发达和欠发达国家和地区的疾病转移扩散以及从人口和密度较高的国家的分层扩散有关。这些过程很可能在国家内部和区域内部的较小地理范围内复制。因此，流行病学策略必须根据人类流动性模式以及国家的定居点和人口特征进行调整。我们建议，在实际可行的范围内最大程度地限制人类流动性，将是最好地遏制 COVID-19 传播的方法，在广泛接种疫苗之前，这可能是最好的流行病学防御手段之一。
