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The Socio-Spatial Determinants of COVID-19 Diffusion

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The Socio-Spatial Determinants of COVID-19 Diffusion Sigler et al. Globalization and Health (2021) 17:56 https://doi.org/10.1186/s12992-021-00707-2 RESEARCH Open Access The socio-spatial determinants of COVID-19 diffusion: the impact of globalisation, settlement characteristics and population Thomas Sigler* , Sirat Mahmuda, Anthony Kimpton, Julia Loginova, Pia Wohland, Elin Charles-Edwards and Jonathan Corcoran Abstract 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. Keywords: COVID-19, Coronavirus, Spatial diffusion, Globalisation, Urbanisation, Quantile regression * Correspondence: [email protected] Queensland Centre for Population Research, The University of Queensland, St Lucia, Queensland 4072, Australia © The Author(s). 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Sigler et al. Globalization and Health (2021) 17:56 Page 2 of 14 Introduction COVID-19 cases, and how this relationship shifted early The Coronavirus disease (COVID-19) has spread more on in the pandemic (Weeks 10–15), when travel restric- globally and rapidly than previous outbreaks (e.g., the tions were still relatively incipient yet viral transmission 1918 Spanish Influenza pandemic and the 2003 SARS began to globalise rapidly. epidemic) [1], which suggests that rising international connectivity [2, 3] and urbanisation [4, 5] have played a Background key role in its diffusion between and within territories. In Infectious diseases diffuse over space and time the pandemic’s early stages, countries with high numbers through inherently geographical processes [23]. The of reported cases (e.g. Italy, Spain, the United Kingdom geographical concept of spatial diffusion is defined as and the United States) and high numbers of reported the spread of a phenomenon across space [24], of cases per capita (e.g. Qatar, Luxembourg, Panama and which disease diffusion through interpersonal trans- Bahrain) have been highly globalised nations with high mission is but one variant [23, 25]. Here, we investi- levels of urbanisation and human mobility, whilst those gate the role of globalisation, settlement and with fewer cases are mostly less globalised, with smaller population characteristics as socio-spatial determi- numbers of visitors, lower rates of urbanisation, and in nants of reported COVID-19 diffusion between coun- general less domestic mobility [6]. This observation tries as an outcome of transmission between holds true at the national scale as well, in that major individuals. Although each new case is by definition a outbreaks of COVID-19 were reported in the pandemic’s product of interpersonal transmission—both directly early stages in countries’ densest, and often most global- via contact, and indirectly via fomites—diffusion can ized and affluent, regions. For example, Lombardia occur across large distances as an outcome of human (Italy) [7, 8], New York (the United States) [9], Madrid movement and mobility. Understandings of viral (Spain) [10], and Tehran (Iran) [11] all by far outnum- transmission lie more firmly within the academic do- bered cases in other regions within their respective main of virology than diffusion does, which is a fun- countries in the first few months of 2020. damentally geographic phenomenon that can be This exploratory study seeks to test the role played by applied to many other forms of spread (for example, globalisation, settlement and population characteristics innovation diffusion [24]). Different underlying pro- to explain the spatial diffusion of reported COVID-19 cesses characterise types of spatial diffusion [26, 27]. cases at a global scale in the early stages of the pan- Expansion diffusion identifies the general tendency for demic. Widely understood to have diffused geographic- phenomena to spread ‘outward’, and infectious dis- ally from a single point of origin in China in late eases are most associated with contagious (expansion) December 2019 [12, 13], spatial diffusion across country diffusion, indicating direct transmission between borders was at first relatively slow. It took 45 days for neighbours due to their physical proximity. the virus to spread to 30 countries, areas or territories As infectious diseases spread through the global popu- [14]. After this time, geographical diffusion accelerated lation, different types of diffusion come into play, often and within the next 45 days, COVID-19 would reach in combination [25, 27]. Disease spread that occurs over nearly all global territories [14]. By April 8th 2020 – the a large distance from its origin is captured by relocation final week in this study – there had been 20,277,716 diffusion, which is often mobilised by air travel or other reported cases recorded within the COVID-19 Data modes of extra-local transportation. On a global scale, Repository by the Center for Systems Science and mobility and connectivity between countries collectively Engineering at Johns Hopkins University (JHU) [15]. contribute to disease outbreaks across the globe, an Only 12 states and territories had purportedly remained observation brought forward by previous research on free of COVID-19 by the end of May 2020, including human rhinovirus, influenza, and SARS [28, 29]. Indeed, 10 small and isolated Pacific island states, and two coun- globalisation in its diverse forms is diminishing the role tries relatively closed to outside influence: Turkmenistan of physical (Euclidian) distance in diffusion. Though and North Korea [16]. disease vectors require human contact, the speed and Despite extensive epidemiological research and ubiquity of global transportation and travel have led to mathematical modelling of the COVID-19 transmission time-space compression [2, 30], which reduces the time- [7, 17–22], there has been a lacuna of work aiming to distance required to connect any two global points. understand how social and geographic factors converge In recent studies [31, 32], globalisation has been shown to explain COVID-19 diffusion on a global scale. In this to be positively linked to the
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