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The Spread of COVID-19 in China at Its Initial Stage: A Township-Level Analysis in Association with the Built Environment
39 Pages Posted: 21 Oct 2020More...
Background: The built environment can contribute to the spread of the novel coronavirus disease (COVID-19) as well as other infectious diseases by facilitating human mobility and social contacts between infected and susceptible individuals. It can also provide a space that enables the direct transmission of pathogens to susceptible individuals. On the other hand, mobility data capturing interpersonal transmission at finegrain in large scale are not available. With the above background, we aim to assess the associations of key built environment factors, which create spaces for activities — “inferred activity” rather than “actually observed activity” — with the spread of COVID-19 in China at its initial stage.
Method: In this nationwide study, we used a random forest (RF) approach to objectively assess the associations between the built environment factors and the spread of COVID-19. The spread is measured for 2 994 township-level administrative units in China by two indicators, the first of which is the ratio of cumulative infection cases (RCIC) that were confirmed until February 13, 2020 (14 days after stopping public transportation operations in all Chinese cities). The other indicator is the coefficient of variation of infection cases (CVIC) between February 6 and February 13, 2020, which reflects the policy effect in the initial stage of the spread. Accordingly, we selected 19 independent variables covering built environment attributes (e.g., urban facilities, transportation infrastructure, and land use), socioeconomic characteristics, and inter-city (from Hubei Province) population flow (PF). We also investigated the spatial agglomerations using a bivariate local indicators of spatial association (BiLISA) between the above two indicators of the spread. The data is from Amap, Resource and Environment Data Cloud Platform, Google Earth Engineer (GEE), Baidu direction lite API, Baidu Migration Production, Tencent, People's Daily, and other references.
Findings: The spread of COVID-19 had obvious spatial agglomerations all across China. Clusters with both low RCIC and low CVIC at the same locations (Low-Low type) show the largest percentage, and are followed by the Low-High type. The Low-High type is more dangerous because clusters of low RCIC are surrounded by clusters with high CVIC which means that policy measures taken in the initial stage may be less effective to prevent/mitigate a rapid disease spread at these locations. The density of convenience shops, supermarkets, and shopping malls (DoCSS) along with PF were the two most important factors to RCIC, whereas PF was the most important factor to measure the policy effects (i.e., the CVIC). When the DoCSS as well as the density of road intersections (DoI), the density of gyms and sport centers (DoGSC) reach 21/km2, 72/km2, and 2/km2, respectively, positive associations of these density indicators with the RCIC become maximal and kept constant as the indicators further increase. Only after PF exceeds 75% its relationship with the RCIC starts showing a positive relationship with RCIC and the relationship keeps stable when the PF gets to 80%. For policy effects, the density of colleges and universities (DoCU) and the density of comprehensive hospitals (DoCH) had negative impacts on policy effects in the initial stage of the COVID-19 spread. Notably, when the DoCH was at less than 0.1/km2, it had a small positive trend. Accordingly, increasing betweenness centrality (BC) associated with increasing policy effect in the initial stage of COVID-19 spread before 5 000, and a reversed trend was observed after 5 000.
Interpretation: First, the findings from this study do not support nationwide uniform measures against COVID-19 (e.g., lockdown and other restriction measures, or reopening of economic activities). Second, the built environment factors may be associated with mitigating or accelerating the spread of COVID-19. Third, the revealed thresholds of different built environment factors in association with the spread of COVID19 present insightful evidence on how to integrate urban and public health planning for future pandemics.
Funding: This study is financially supported by two funds from Japan Science and Technology Agency. One of these funds is the J-RAPID Collaborative Research/Survey Program for Urgent Research framework which is titled, “Impacts of COVID-19 on the transport and logistics sector and countermeasures.” The other fund is the Ethical, Legal, and Social Implications/Issues (ELSI) research framework, “Responsible Innovation with Conscience and Agility,” which is titled, “Overcoming Vulnerability and Restoring Social Justice in Community and Re-designing Cities by Introducing Social Distancing."
Declaration of Interests: The authors declare no competing interests.
Keywords: COVID-19, China, The built environment, Random forest approach, Bivariate local indicators of spatial association, Spatial agglomeration, Nonlinear effects, Urban and regional planning and management.
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