Diverse and nonlinear influences of built environment factors on COVID-19 spread across townships in China at its initial stage

Abstract

The built environment can contribute to the spread of the novel coronavirus disease (COVID-19) by facilitating human mobility and social contacts between infected and uninfected individuals. However, mobility data capturing detailed interpersonal transmission at a large scale are not available. In this study, we aimed to objectively assess the influence of key built environment factors, which create spaces for activities—“inferred activity” rather than “actually observed activity”—on the spread of COVID-19 across townships in China at its initial stage through a random forest approach. Taking data for 2994 township-level administrative units, the spread is measured by two indicators: the ratio of cumulative infection cases (RCIC), and the coefficient of variation of infection cases (CVIC) that reflects the policy effect in the initial stage of the spread. Accordingly, we selected 19 explanatory variables covering built environment factors (urban facilities, land use, and transportation infrastructure), the level of nighttime activities, and the inter-city population flow (from Hubei Province). We investigated the spatial agglomerations based on an analysis of bivariate local indicators of spatial association between RCIC and CVIC. We found spatial agglomeration (or positive spatial autocorrelations) of RCIC and CVIC in about 20% of all townships under study. The density of convenience shops, supermarkets and shopping malls (DoCSS), and the inter-city population flow (from Hubei Province) are the two most important variables to explain RCIC, while the population flow is the most important factor in measuring policy effects (CVIC). When the DoCSS gets to 21/km2, the density of comprehensive hospitals to 0.7/km2, the density of road intersections to 72/km2, and the density of gyms and sports centers to 2/km2, their impacts on RCIC reach their maximum and remain constant with further increases in the density values. Stricter policy measures should be taken at townships with a density of colleges and universities higher than 0.5/km2 or a density of comprehensive hospitals higher than 0.25/km2 in order to effectively control the spread of COVID-19.

Effects of the built environment and human factors on the spread of COVID-19: A systematic literature review

“…The impact of land use on the spread of COVID-19 is also measured by density and POIs. Higher density of specific land uses such as supermarkets, commercial land uses, clinics, hospitals, administrative, schools, shopping centers, and restaurants are found as the positive and significant predictors of COVID-19 cases in Tehran, Hong Kong, New York, Chicago, Wuhan and Huangzhou ( Ma et al, 2021 ; Nasiri et al, 2021 ; Li et al, 2020 ; Lak et al, 2021 ; Huang et al, 2021 , Huang et al, 2020 ; Yip et al, 2021 ). The high density of these land uses will contribute to higher POI and accordingly a higher rate of COVID-19 cases ( Xu et al, 2022 ; B. Li et al, 2021 ).…”

“…These are focused on the county (7), city (2), and urban district (22) scales (as shown in Table 3 ). The result of our review showed that land-use variables include 1. share of different land uses such as roads, open and green spaces ( Hassan et al, 2021 ; Kwok et al, 2021 ), 2. density of different land uses such as restaurants and clinics ( Ma et al, 2021 ; Huang et al, 2020 , Huang et al, 2021 ), 3. accessibility to different land uses such as commercial and educational ( Verma et al, 2021 ; Sun et al, 2021 ), 4. the mixture of land uses ( Li et al, 2020 ; Wali and Frank, 2021 ) and 5. Point of Interest (POI) for different land uses ( Xu et al, 2022 ; B. Li et al, 2021 ).…”

Understanding travel behavior adjustment under COVID-19

“…The first six features with the highest feature importance are selected to draw partial dependence plots ( Ma et al, 2021 ) for further analysis. The partial dependence plots of X 4 , X 6 , X 5 , X 1 , X 18 , and X 16 and each of the clusters are shown in Fig.…”

Spatio-temporal distribution pattern of COVID-19 in the Northern Italy during the first-wave scenario: The role of the highway network

“…Indeed, in the pre-COVID-19 the literature demonstrated different transport modes ( Findlater and Bogoch, 2018 ) as a direct factor affecting infectious diseases transmission ( Zhang et al, 2020a , Zhang et al, 2020b , Peak et al, 2018 , Arthur et al, 2017 , López-Quílez, 2019 ). The recent COVID-19 literature focused on built environment factors ( Ma et al, 2021 ), urban attributes ( Barak et al, 2021 ), and living conditions ( Sahasranaman and Jensen, 2021 ) to investigate the income on cases growth. The built environment plays a pivotal role in people health and well-being ( Angiello, 2021 ): density of schools, commercial and sport centers, proportion of built-up areas, building systems and nighttime light, socioeconomic and demographic characteristics have been identified have as key drivers of the SARS-CoV-2 transmission and infection ( Verma et al, 2021 , Barak et al, 2021 , Corazza et al, 2021 ).…”