Urban planning, development, and determinants of local COVID-19 transmission among older adults

1.Background

Cities are home to most of the world’s population and are centres of socioeconomic activity and innovation. Furthermore, the proportion of older adults in the world is projected to reach nearly 16% in 2050 and nearly 23% by 2100. Ageing and high concentration of people and development in cities make them vulnerable to various disasters and pandemics. Particularly, public health threats may disproportionately increase health and disaster risk for older people. However, prior to the emergence of the COVID-19 pandemic, limited literature has paid attention to the relationship between urban planning, design, management and epidemic transmission. This study aims to propose a novel approach to characterize the spatial distribution and dynamics of pandemic transmission, and to examine their relationship to urban physical, socioeconomic, health environments, and planning with a focus on the COVID-19 pandemic transmission among metropolitan older adults.

2.Methods and data

The methodology was built by combing SEIR (susceptible-exposed-infective-recovered) model with concepts of pandemic transmission and ageing resilience. Using a case study in Taipei metropolis, Taiwan, the analysis comprises four steps: (1) We established an analytical framework based on the pre-pandemic, during-pandemic, and post-pandemic phases through the SEIR model and resilience concept. (2) Applying a geographical information system (GIS)-based spatial statistical analysis (G-statistic and LISA (local indicators of spatial autocorrelation)), we investigated the spatial distribution patterns of COVID-19 spread among older adults and their changes over time. (3) Poisson and Panel-Poisson regression models were created to examine the relationship between land-use planning, public transportation, socioeconomic environment, population mobility, and the dynamics of COVID-19 transmission among older adults. (4) We applied geographically weighted models (GWR) to scrutinize the spatial distribution patterns of the key factors affecting COVID-19 transmission. To illustrate the proposed methodology, the data were collected from the Ministry of Health and Welfare, National Land Surveying and Mapping Center, Taiwan, and city official census statistics.

3.Results and policy implications

Results of spatial statistical analysis showed heterogeneous patterns of epidemic spread and highlighted their changes over time. The hotspots of COVID-19 infection unfolded as cross-regional aggregation and spatial analogy, concentrated in some densely populated and old downtown areas, rather than randomly distributed. The findings of Poisson analysis showed that the density of population, land use, and public transportation services were significantly positively related to older adults’ COVID-19 infection, while health service density was significantly negatively associated. However, vulnerability factors, such as low-income and population mobility had strongly positive impacts only during the peak of outbreak and in the post-pandemic periods. Moreover, GWR analysis revealed significant differences in the spatial distribution patterns between the key determinants of pandemic transmission. Results showed that the impacts of various land use types and public transportation density were inconsistent, but the effects increased with proximity to the city center. Our findings provide some policy implications for decision-making in metropolitan land use, transportation planning, design, and management. In the post-COVID era, planners need to re-evaluate the existing compact city and public transportation policies and move towards more age-friendly, resilient, and integrated governance measures.