Table 1.
Major references about the association of the BE attributes with the spread of COVID-19.
Source | BE Attributes | Analysis method | Scale | Major findings related to the present study |
---|---|---|---|---|
Nguyen et al., 2020 | Presence of a crosswalk, non-single family home, single-lane roads, dilapidated building and visible wires | Google Street View (GSV) images and computer vision; Poisson regression models | 164 million images in the USA |
Indicators of mixed land use (non-single-family home), walkability (sidewalks) and physical disorder (dilapidated buildings and visible wires) were connected with higher COVID-19 cases. Indicators of lower urban development (single lane roads and green streets) were associated with fewer COVID-19 cases. |
Hamidi et al., 2020a | Metropolitan population, activity density (population & employment per square mile), ICU beds per 10,000 population, primary care physicians per 10,000 population | Multi-level linear model | 1165 metropolitan counties in the USA | Larger metropolitan areas lead to significantly higher COVID-19 infection rates and higher mortality rates |
Lee et al., 2020 | Traffic volumes on roads | Single linear regression | 6307 vehicle detection systems (VDS) in South Korea | In Incheon there was a positive, but insignificant, linear relationship between the increasing numbers of newly confirmed cases and increasing traffic. |
Ghosh et al., 2020 | Travel distance to London, population density | Mixed-effects model | Distance from London to four other cities (Birmingham, Leeds, Manchester and Sheffield) | As the distance from London increases, the number of COVID-19 cases decreases. |
Mizumoto & Chowell, 2020 | Occupant density on the Diamond Princess cruise ship | Mathematical modeling | 621epidemiological incidence cases | The increased exposure risks associated with high occupant density were demonstrated in the COVID-19 outbreak that occurred on the ship. |
Emeruwa et al. 2020 | Building-level variables, including the number of residential units per building and mean assessed value (per square foot), and neighborhood-level variables, including population density, household membership (persons per household) and household crowding. | Bivariable logistic Regression model | 71 infected cases in New York | COVID-19 transmission among pregnant women was associated with neighborhood- and building-level markers of large household membership and household crowding. |
Dai & Zhao, 2020 | Ventilation rate | Wells–Riley equation | Typical scenarios, including offices, classrooms, buses and aircraft cabins. | An infection probability of less than 1% requires a ventilation rate larger than 100–350 m3/h per infector and 1200–4000 m3/h per infector for 0.25 h and 3 h of exposure. |
Antony, Velray & Fariborz, 2020 | Population density, climate severity, the volume of indoor spaces and air-conditioning usage | Statistical analysis of correlations | Various states in India | Fast drying and size reduction of respiratory droplets makes the virus more active. |
Auger, Shah & Richardson, 2020 | Schools | Population-based time series analysis | All USA states | School closure was associated with a significant decline in the incidence of COVID-19 and mortality. |
Brown et al. 2020 | Nursing homes crowding | Population-based retrospective cohort study | 78,000 residents of 618 distinct nursing homes in Ontario, Canada | Crowding in nursing homes was associated with a higher incidence of COVID-19 infection and mortality. |
Hamidi et al., 2020b | County activity density and metropolitan area population | Structural equation model | 913 metropolitan counties in the USA | Metropolitan population is one of the most significant predictors of infection rates; larger metropolitan areas have higher infection and higher mortality rates. |