The socio-spatial determinants of COVID-19 diffusion: the impact of globalisation, settlement characteristics and population.

Autor: Sigler T; Queensland Centre for Population Research, The University of Queensland, St Lucia, Queensland, 4072, Australia. t.sigler@uq.edu.au., Mahmuda S; Queensland Centre for Population Research, The University of Queensland, St Lucia, Queensland, 4072, Australia., Kimpton A; Queensland Centre for Population Research, The University of Queensland, St Lucia, Queensland, 4072, Australia., Loginova J; Queensland Centre for Population Research, The University of Queensland, St Lucia, Queensland, 4072, Australia., Wohland P; Queensland Centre for Population Research, The University of Queensland, St Lucia, Queensland, 4072, Australia., Charles-Edwards E; Queensland Centre for Population Research, The University of Queensland, St Lucia, Queensland, 4072, Australia., Corcoran J; Queensland Centre for Population Research, The University of Queensland, St Lucia, Queensland, 4072, Australia.
Jazyk: angličtina
Zdroj: Globalization and health [Global Health] 2021 May 20; Vol. 17 (1), pp. 56. Date of Electronic Publication: 2021 May 20.
DOI: 10.1186/s12992-021-00707-2
Abstrakt: Background: 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.
Results: 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.
Conclusions: 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.
Databáze: MEDLINE
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